stevenbucaille/object-detection-models-dataset

model_id stringlengths 9 102	model_card stringlengths 4 343k	model_labels listlengths 2 50.8k
vrglx333/detr-finetuned-personal-equipment	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14" ]
nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session3](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session3) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]
oskarkuuse/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0882 \| 1.0 \| 1250 \| 1.3492 \| \| 1.6047 \| 2.0 \| 2500 \| 1.2964 \| \| 1.5492 \| 3.0 \| 3750 \| 1.2105 \| \| 1.3223 \| 4.0 \| 5000 \| 1.1513 \| \| 1.1328 \| 5.0 \| 6250 \| 1.1143 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session5 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]
maxencerch/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3326 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5582 \| 0.32 \| 100 \| 1.7392 \| \| 1.5953 \| 0.64 \| 200 \| 1.4379 \| \| 1.4199 \| 0.96 \| 300 \| 1.3326 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
rjhugs/modelStructure_TT_V3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # modelStructure_TT_V3 This model is a fine-tuned version of [microsoft/table-transformer-structure-recognition-v1.1-all](https://huggingface.co/microsoft/table-transformer-structure-recognition-v1.1-all) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.2+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3	[ "table", "table column header", "table column" ]
marie434/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9455 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.504 \| 0.16 \| 100 \| 1.4472 \| \| 1.3067 \| 0.32 \| 200 \| 1.2490 \| \| 1.1213 \| 0.48 \| 300 \| 1.1124 \| \| 1.1397 \| 0.64 \| 400 \| 1.0763 \| \| 1.0015 \| 0.8 \| 500 \| 0.9798 \| \| 1.0052 \| 0.96 \| 600 \| 0.9455 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "class_0", "class_1", "class_2", "class_3" ]
qubvel-hf/detr-resnet-50-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 0.9865 - Map: 0.3578 - Map 50: 0.6781 - Map 75: 0.3105 - Map Small: 0.3578 - Map Medium: -1.0 - Map Large: -1.0 - Mar 1: 0.365 - Mar 10: 0.535 - Mar 100: 0.5483 - Mar Small: 0.5483 - Mar Medium: -1.0 - Mar Large: -1.0 - Map Coverall: 0.6584 - Mar 100 Coverall: 0.7772 - Map Face Shield: 0.3691 - Mar 100 Face Shield: 0.6063 - Map Gloves: 0.2477 - Mar 100 Gloves: 0.4266 - Map Goggles: 0.1766 - Mar 100 Goggles: 0.4655 - Map Mask: 0.3371 - Mar 100 Mask: 0.4661 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 3.7198 \| 1.0 \| 107 \| 3.1869 \| 0.0036 \| 0.0144 \| 0.0006 \| 0.0036 \| -1.0 \| -1.0 \| 0.017 \| 0.0426 \| 0.0647 \| 0.0647 \| -1.0 \| -1.0 \| 0.0169 \| 0.1994 \| 0.0 \| 0.0 \| 0.0001 \| 0.0177 \| 0.0 \| 0.0 \| 0.0008 \| 0.1062 \| \| 3.0393 \| 2.0 \| 214 \| 2.8978 \| 0.007 \| 0.021 \| 0.003 \| 0.007 \| -1.0 \| -1.0 \| 0.0192 \| 0.0796 \| 0.1238 \| 0.1238 \| -1.0 \| -1.0 \| 0.0333 \| 0.5216 \| 0.0 \| 0.0 \| 0.0001 \| 0.0292 \| 0.0 \| 0.0 \| 0.0017 \| 0.0684 \| \| 2.7812 \| 3.0 \| 321 \| 2.5445 \| 0.0138 \| 0.0414 \| 0.0085 \| 0.0138 \| -1.0 \| -1.0 \| 0.0307 \| 0.0994 \| 0.1258 \| 0.1258 \| -1.0 \| -1.0 \| 0.0655 \| 0.4938 \| 0.0 \| 0.0 \| 0.0002 \| 0.0354 \| 0.0 \| 0.0 \| 0.0033 \| 0.1 \| \| 2.5992 \| 4.0 \| 428 \| 2.3828 \| 0.0232 \| 0.0601 \| 0.0155 \| 0.0232 \| -1.0 \| -1.0 \| 0.0423 \| 0.1202 \| 0.1518 \| 0.1518 \| -1.0 \| -1.0 \| 0.1021 \| 0.5481 \| 0.0 \| 0.0 \| 0.0006 \| 0.0495 \| 0.0059 \| 0.0109 \| 0.0072 \| 0.1503 \| \| 2.3828 \| 5.0 \| 535 \| 2.2672 \| 0.0283 \| 0.0703 \| 0.0179 \| 0.0283 \| -1.0 \| -1.0 \| 0.0521 \| 0.1283 \| 0.1737 \| 0.1737 \| -1.0 \| -1.0 \| 0.1344 \| 0.5846 \| 0.0 \| 0.0 \| 0.001 \| 0.0833 \| 0.0 \| 0.0 \| 0.0063 \| 0.2006 \| \| 2.2633 \| 6.0 \| 642 \| 2.0618 \| 0.0479 \| 0.0996 \| 0.0416 \| 0.0479 \| -1.0 \| -1.0 \| 0.0782 \| 0.1679 \| 0.2035 \| 0.2035 \| -1.0 \| -1.0 \| 0.2099 \| 0.6333 \| 0.003 \| 0.0159 \| 0.0018 \| 0.1187 \| 0.0052 \| 0.0218 \| 0.0195 \| 0.2277 \| \| 2.1837 \| 7.0 \| 749 \| 2.1100 \| 0.0455 \| 0.1159 \| 0.0255 \| 0.0455 \| -1.0 \| -1.0 \| 0.0747 \| 0.1582 \| 0.1894 \| 0.1894 \| -1.0 \| -1.0 \| 0.2068 \| 0.6185 \| 0.0085 \| 0.0556 \| 0.001 \| 0.0734 \| 0.0002 \| 0.0018 \| 0.0113 \| 0.1977 \| \| 2.0689 \| 8.0 \| 856 \| 2.0000 \| 0.054 \| 0.1389 \| 0.0301 \| 0.054 \| -1.0 \| -1.0 \| 0.0954 \| 0.1846 \| 0.2159 \| 0.2159 \| -1.0 \| -1.0 \| 0.2155 \| 0.5537 \| 0.0314 \| 0.1397 \| 0.0049 \| 0.1406 \| 0.0002 \| 0.0018 \| 0.0181 \| 0.2435 \| \| 2.0417 \| 9.0 \| 963 \| 1.8702 \| 0.0697 \| 0.1631 \| 0.0501 \| 0.0697 \| -1.0 \| -1.0 \| 0.1074 \| 0.2173 \| 0.257 \| 0.257 \| -1.0 \| -1.0 \| 0.2826 \| 0.6086 \| 0.0279 \| 0.181 \| 0.0046 \| 0.1734 \| 0.0102 \| 0.0418 \| 0.0234 \| 0.2802 \| \| 1.9972 \| 10.0 \| 1070 \| 1.8563 \| 0.0742 \| 0.1568 \| 0.0541 \| 0.0742 \| -1.0 \| -1.0 \| 0.1196 \| 0.2416 \| 0.2786 \| 0.2786 \| -1.0 \| -1.0 \| 0.2933 \| 0.6086 \| 0.0233 \| 0.1921 \| 0.0053 \| 0.1672 \| 0.0239 \| 0.0891 \| 0.025 \| 0.3362 \| \| 1.8931 \| 11.0 \| 1177 \| 1.6778 \| 0.1054 \| 0.2248 \| 0.0898 \| 0.1054 \| -1.0 \| -1.0 \| 0.1456 \| 0.2764 \| 0.3033 \| 0.3033 \| -1.0 \| -1.0 \| 0.3955 \| 0.671 \| 0.0498 \| 0.2603 \| 0.0108 \| 0.2188 \| 0.0149 \| 0.0382 \| 0.056 \| 0.3282 \| \| 1.8269 \| 12.0 \| 1284 \| 1.6905 \| 0.1111 \| 0.2399 \| 0.0942 \| 0.1111 \| -1.0 \| -1.0 \| 0.1543 \| 0.2949 \| 0.3257 \| 0.3257 \| -1.0 \| -1.0 \| 0.4113 \| 0.679 \| 0.069 \| 0.319 \| 0.0087 \| 0.2021 \| 0.015 \| 0.0909 \| 0.0514 \| 0.3373 \| \| 1.8036 \| 13.0 \| 1391 \| 1.6406 \| 0.1149 \| 0.2407 \| 0.097 \| 0.1149 \| -1.0 \| -1.0 \| 0.1636 \| 0.3108 \| 0.3372 \| 0.3372 \| -1.0 \| -1.0 \| 0.4255 \| 0.6759 \| 0.0771 \| 0.3381 \| 0.0109 \| 0.2182 \| 0.0137 \| 0.1309 \| 0.047 \| 0.3226 \| \| 1.7463 \| 14.0 \| 1498 \| 1.7169 \| 0.1106 \| 0.2421 \| 0.0875 \| 0.1106 \| -1.0 \| -1.0 \| 0.1776 \| 0.3205 \| 0.3511 \| 0.3511 \| -1.0 \| -1.0 \| 0.3996 \| 0.7 \| 0.0404 \| 0.2476 \| 0.0117 \| 0.2458 \| 0.0257 \| 0.2036 \| 0.0757 \| 0.3582 \| \| 1.763 \| 15.0 \| 1605 \| 1.5961 \| 0.1245 \| 0.2577 \| 0.1018 \| 0.1245 \| -1.0 \| -1.0 \| 0.1817 \| 0.3384 \| 0.3677 \| 0.3677 \| -1.0 \| -1.0 \| 0.4575 \| 0.6679 \| 0.0775 \| 0.3698 \| 0.0107 \| 0.2505 \| 0.0318 \| 0.1964 \| 0.0447 \| 0.3537 \| \| 1.6467 \| 16.0 \| 1712 \| 1.5365 \| 0.1376 \| 0.3073 \| 0.1062 \| 0.1376 \| -1.0 \| -1.0 \| 0.2164 \| 0.38 \| 0.408 \| 0.408 \| -1.0 \| -1.0 \| 0.455 \| 0.6852 \| 0.0739 \| 0.3873 \| 0.0215 \| 0.2719 \| 0.0442 \| 0.2891 \| 0.0934 \| 0.4068 \| \| 1.6222 \| 17.0 \| 1819 \| 1.5990 \| 0.1295 \| 0.2696 \| 0.1026 \| 0.1295 \| -1.0 \| -1.0 \| 0.1802 \| 0.3409 \| 0.3693 \| 0.3693 \| -1.0 \| -1.0 \| 0.4577 \| 0.6654 \| 0.0786 \| 0.3619 \| 0.0297 \| 0.2958 \| 0.0211 \| 0.2218 \| 0.0603 \| 0.3017 \| \| 1.6239 \| 18.0 \| 1926 \| 1.4164 \| 0.159 \| 0.3543 \| 0.1262 \| 0.159 \| -1.0 \| -1.0 \| 0.235 \| 0.3929 \| 0.4138 \| 0.4138 \| -1.0 \| -1.0 \| 0.4753 \| 0.7204 \| 0.0921 \| 0.3968 \| 0.039 \| 0.2922 \| 0.0323 \| 0.2636 \| 0.1565 \| 0.396 \| \| 1.5448 \| 19.0 \| 2033 \| 1.4689 \| 0.1628 \| 0.3725 \| 0.1314 \| 0.1628 \| -1.0 \| -1.0 \| 0.205 \| 0.3811 \| 0.4064 \| 0.4064 \| -1.0 \| -1.0 \| 0.4794 \| 0.6895 \| 0.1038 \| 0.419 \| 0.0398 \| 0.2828 \| 0.0333 \| 0.28 \| 0.1578 \| 0.3605 \| \| 1.5026 \| 20.0 \| 2140 \| 1.4093 \| 0.1798 \| 0.397 \| 0.1369 \| 0.1798 \| -1.0 \| -1.0 \| 0.2336 \| 0.4125 \| 0.4349 \| 0.4349 \| -1.0 \| -1.0 \| 0.4851 \| 0.6858 \| 0.1494 \| 0.4508 \| 0.0341 \| 0.2859 \| 0.0434 \| 0.3382 \| 0.1869 \| 0.4136 \| \| 1.4797 \| 21.0 \| 2247 \| 1.4605 \| 0.1652 \| 0.3605 \| 0.1254 \| 0.1652 \| -1.0 \| -1.0 \| 0.2295 \| 0.3823 \| 0.4041 \| 0.4041 \| -1.0 \| -1.0 \| 0.4978 \| 0.6957 \| 0.0968 \| 0.3825 \| 0.0529 \| 0.2797 \| 0.0263 \| 0.3236 \| 0.1522 \| 0.339 \| \| 1.4298 \| 22.0 \| 2354 \| 1.4231 \| 0.163 \| 0.3558 \| 0.115 \| 0.163 \| -1.0 \| -1.0 \| 0.2256 \| 0.3851 \| 0.4108 \| 0.4108 \| -1.0 \| -1.0 \| 0.4902 \| 0.7093 \| 0.1033 \| 0.4159 \| 0.0515 \| 0.313 \| 0.0261 \| 0.3109 \| 0.1437 \| 0.3051 \| \| 1.4157 \| 23.0 \| 2461 \| 1.3665 \| 0.1914 \| 0.4048 \| 0.1533 \| 0.1914 \| -1.0 \| -1.0 \| 0.2478 \| 0.4232 \| 0.447 \| 0.447 \| -1.0 \| -1.0 \| 0.491 \| 0.6975 \| 0.1599 \| 0.4683 \| 0.0502 \| 0.3021 \| 0.0603 \| 0.3618 \| 0.1956 \| 0.4051 \| \| 1.4438 \| 24.0 \| 2568 \| 1.2908 \| 0.2103 \| 0.433 \| 0.168 \| 0.2103 \| -1.0 \| -1.0 \| 0.2643 \| 0.4512 \| 0.4761 \| 0.4761 \| -1.0 \| -1.0 \| 0.5368 \| 0.7136 \| 0.1493 \| 0.4873 \| 0.0789 \| 0.3609 \| 0.043 \| 0.3891 \| 0.2433 \| 0.4294 \| \| 1.4044 \| 25.0 \| 2675 \| 1.4752 \| 0.1709 \| 0.3749 \| 0.1388 \| 0.1709 \| -1.0 \| -1.0 \| 0.2187 \| 0.3926 \| 0.4191 \| 0.4191 \| -1.0 \| -1.0 \| 0.4862 \| 0.7167 \| 0.09 \| 0.3905 \| 0.0762 \| 0.299 \| 0.0393 \| 0.3527 \| 0.1627 \| 0.3367 \| \| 1.3703 \| 26.0 \| 2782 \| 1.3047 \| 0.2162 \| 0.4568 \| 0.1714 \| 0.2162 \| -1.0 \| -1.0 \| 0.2661 \| 0.4344 \| 0.4548 \| 0.4548 \| -1.0 \| -1.0 \| 0.5342 \| 0.7272 \| 0.166 \| 0.4508 \| 0.0971 \| 0.3281 \| 0.0424 \| 0.3527 \| 0.2414 \| 0.4153 \| \| 1.3292 \| 27.0 \| 2889 \| 1.2674 \| 0.22 \| 0.4681 \| 0.1702 \| 0.22 \| -1.0 \| -1.0 \| 0.2743 \| 0.4286 \| 0.4473 \| 0.4473 \| -1.0 \| -1.0 \| 0.5438 \| 0.7265 \| 0.2128 \| 0.4429 \| 0.1171 \| 0.3443 \| 0.0387 \| 0.3455 \| 0.1878 \| 0.3774 \| \| 1.359 \| 28.0 \| 2996 \| 1.3156 \| 0.2007 \| 0.4272 \| 0.1536 \| 0.2007 \| -1.0 \| -1.0 \| 0.2715 \| 0.4384 \| 0.4555 \| 0.4555 \| -1.0 \| -1.0 \| 0.5306 \| 0.7111 \| 0.163 \| 0.5016 \| 0.0896 \| 0.3135 \| 0.0307 \| 0.38 \| 0.1898 \| 0.3712 \| \| 1.3471 \| 29.0 \| 3103 \| 1.2646 \| 0.2161 \| 0.455 \| 0.172 \| 0.2161 \| -1.0 \| -1.0 \| 0.277 \| 0.4492 \| 0.4728 \| 0.4728 \| -1.0 \| -1.0 \| 0.5301 \| 0.7216 \| 0.1708 \| 0.519 \| 0.1216 \| 0.3271 \| 0.0391 \| 0.4145 \| 0.2189 \| 0.3819 \| \| 1.308 \| 30.0 \| 3210 \| 1.3017 \| 0.2107 \| 0.4465 \| 0.1718 \| 0.2107 \| -1.0 \| -1.0 \| 0.2556 \| 0.4141 \| 0.4387 \| 0.4387 \| -1.0 \| -1.0 \| 0.5321 \| 0.7136 \| 0.1531 \| 0.454 \| 0.1037 \| 0.3203 \| 0.0334 \| 0.3218 \| 0.2313 \| 0.3836 \| \| 1.3023 \| 31.0 \| 3317 \| 1.2809 \| 0.2174 \| 0.462 \| 0.1714 \| 0.2174 \| -1.0 \| -1.0 \| 0.2646 \| 0.4242 \| 0.4473 \| 0.4473 \| -1.0 \| -1.0 \| 0.5484 \| 0.7259 \| 0.1686 \| 0.427 \| 0.1163 \| 0.3536 \| 0.0506 \| 0.3564 \| 0.2029 \| 0.3734 \| \| 1.2561 \| 32.0 \| 3424 \| 1.2679 \| 0.2082 \| 0.4557 \| 0.1579 \| 0.2082 \| -1.0 \| -1.0 \| 0.263 \| 0.4283 \| 0.4511 \| 0.4511 \| -1.0 \| -1.0 \| 0.541 \| 0.7284 \| 0.1448 \| 0.4556 \| 0.1308 \| 0.3682 \| 0.0411 \| 0.3436 \| 0.1835 \| 0.3599 \| \| 1.268 \| 33.0 \| 3531 \| 1.2632 \| 0.2304 \| 0.4841 \| 0.1882 \| 0.2304 \| -1.0 \| -1.0 \| 0.2852 \| 0.4469 \| 0.4629 \| 0.4629 \| -1.0 \| -1.0 \| 0.5535 \| 0.7327 \| 0.2042 \| 0.5286 \| 0.1027 \| 0.2917 \| 0.0472 \| 0.38 \| 0.2441 \| 0.3814 \| \| 1.2337 \| 34.0 \| 3638 \| 1.2157 \| 0.2339 \| 0.497 \| 0.1854 \| 0.2339 \| -1.0 \| -1.0 \| 0.2845 \| 0.4495 \| 0.4756 \| 0.4756 \| -1.0 \| -1.0 \| 0.5704 \| 0.7414 \| 0.187 \| 0.5095 \| 0.1339 \| 0.3448 \| 0.0414 \| 0.3982 \| 0.2367 \| 0.3842 \| \| 1.2192 \| 35.0 \| 3745 \| 1.2109 \| 0.2505 \| 0.5078 \| 0.2013 \| 0.2505 \| -1.0 \| -1.0 \| 0.288 \| 0.4617 \| 0.4795 \| 0.4795 \| -1.0 \| -1.0 \| 0.5599 \| 0.7259 \| 0.2431 \| 0.5016 \| 0.1564 \| 0.3521 \| 0.0496 \| 0.4164 \| 0.2437 \| 0.4017 \| \| 1.2119 \| 36.0 \| 3852 \| 1.1897 \| 0.2584 \| 0.5229 \| 0.2198 \| 0.2584 \| -1.0 \| -1.0 \| 0.2986 \| 0.4575 \| 0.4744 \| 0.4744 \| -1.0 \| -1.0 \| 0.5742 \| 0.7383 \| 0.2667 \| 0.5159 \| 0.1389 \| 0.35 \| 0.0448 \| 0.3473 \| 0.2676 \| 0.4203 \| \| 1.1686 \| 37.0 \| 3959 \| 1.1441 \| 0.2581 \| 0.5164 \| 0.2072 \| 0.2581 \| -1.0 \| -1.0 \| 0.3002 \| 0.4712 \| 0.4918 \| 0.4918 \| -1.0 \| -1.0 \| 0.5904 \| 0.7642 \| 0.2406 \| 0.5238 \| 0.1535 \| 0.3708 \| 0.0404 \| 0.3945 \| 0.2654 \| 0.4056 \| \| 1.1664 \| 38.0 \| 4066 \| 1.1554 \| 0.2618 \| 0.546 \| 0.204 \| 0.2618 \| -1.0 \| -1.0 \| 0.3078 \| 0.4822 \| 0.5069 \| 0.5069 \| -1.0 \| -1.0 \| 0.5674 \| 0.734 \| 0.2486 \| 0.519 \| 0.1922 \| 0.3927 \| 0.0409 \| 0.4509 \| 0.26 \| 0.4379 \| \| 1.1746 \| 39.0 \| 4173 \| 1.1873 \| 0.2514 \| 0.5269 \| 0.1995 \| 0.2514 \| -1.0 \| -1.0 \| 0.292 \| 0.4628 \| 0.4842 \| 0.4842 \| -1.0 \| -1.0 \| 0.5527 \| 0.7235 \| 0.2427 \| 0.5127 \| 0.1591 \| 0.3448 \| 0.0468 \| 0.4218 \| 0.2557 \| 0.4181 \| \| 1.1559 \| 40.0 \| 4280 \| 1.1594 \| 0.2652 \| 0.5517 \| 0.2014 \| 0.2652 \| -1.0 \| -1.0 \| 0.2955 \| 0.4743 \| 0.4885 \| 0.4885 \| -1.0 \| -1.0 \| 0.5685 \| 0.7377 \| 0.2624 \| 0.5238 \| 0.1852 \| 0.3755 \| 0.0505 \| 0.3873 \| 0.2595 \| 0.4181 \| \| 1.1668 \| 41.0 \| 4387 \| 1.1562 \| 0.2479 \| 0.5294 \| 0.1853 \| 0.2479 \| -1.0 \| -1.0 \| 0.2967 \| 0.4772 \| 0.4994 \| 0.4994 \| -1.0 \| -1.0 \| 0.5742 \| 0.7272 \| 0.217 \| 0.5413 \| 0.1492 \| 0.3453 \| 0.058 \| 0.4709 \| 0.2411 \| 0.4124 \| \| 1.1084 \| 42.0 \| 4494 \| 1.1302 \| 0.2634 \| 0.5352 \| 0.2094 \| 0.2634 \| -1.0 \| -1.0 \| 0.3135 \| 0.4783 \| 0.4945 \| 0.4945 \| -1.0 \| -1.0 \| 0.5847 \| 0.7333 \| 0.2335 \| 0.5397 \| 0.1653 \| 0.3583 \| 0.0484 \| 0.4127 \| 0.2851 \| 0.4282 \| \| 1.1118 \| 43.0 \| 4601 \| 1.1270 \| 0.2754 \| 0.5506 \| 0.2408 \| 0.2754 \| -1.0 \| -1.0 \| 0.3103 \| 0.4789 \| 0.4973 \| 0.4973 \| -1.0 \| -1.0 \| 0.5867 \| 0.7364 \| 0.2662 \| 0.5476 \| 0.1587 \| 0.3682 \| 0.0599 \| 0.3855 \| 0.3057 \| 0.4486 \| \| 1.1096 \| 44.0 \| 4708 \| 1.1772 \| 0.2611 \| 0.5565 \| 0.2002 \| 0.2611 \| -1.0 \| -1.0 \| 0.2873 \| 0.4606 \| 0.4755 \| 0.4755 \| -1.0 \| -1.0 \| 0.5739 \| 0.7228 \| 0.247 \| 0.5143 \| 0.1649 \| 0.3516 \| 0.081 \| 0.4073 \| 0.2389 \| 0.3814 \| \| 1.0741 \| 45.0 \| 4815 \| 1.0871 \| 0.2857 \| 0.5718 \| 0.2186 \| 0.2857 \| -1.0 \| -1.0 \| 0.3237 \| 0.4934 \| 0.5104 \| 0.5104 \| -1.0 \| -1.0 \| 0.5927 \| 0.75 \| 0.2775 \| 0.5698 \| 0.1899 \| 0.3786 \| 0.0781 \| 0.4145 \| 0.2904 \| 0.439 \| \| 1.0774 \| 46.0 \| 4922 \| 1.1049 \| 0.2684 \| 0.5461 \| 0.2197 \| 0.2684 \| -1.0 \| -1.0 \| 0.3112 \| 0.4956 \| 0.5115 \| 0.5115 \| -1.0 \| -1.0 \| 0.5979 \| 0.7574 \| 0.2552 \| 0.5794 \| 0.143 \| 0.3677 \| 0.062 \| 0.4291 \| 0.284 \| 0.4237 \| \| 1.0517 \| 47.0 \| 5029 \| 1.1005 \| 0.2806 \| 0.5821 \| 0.2184 \| 0.2806 \| -1.0 \| -1.0 \| 0.3112 \| 0.4872 \| 0.5048 \| 0.5048 \| -1.0 \| -1.0 \| 0.5861 \| 0.7543 \| 0.2862 \| 0.5619 \| 0.1749 \| 0.4016 \| 0.0737 \| 0.3945 \| 0.282 \| 0.4119 \| \| 1.0717 \| 48.0 \| 5136 \| 1.0878 \| 0.2956 \| 0.5902 \| 0.2492 \| 0.2956 \| -1.0 \| -1.0 \| 0.3146 \| 0.4925 \| 0.5089 \| 0.5089 \| -1.0 \| -1.0 \| 0.6128 \| 0.7506 \| 0.2813 \| 0.5381 \| 0.1947 \| 0.4036 \| 0.0849 \| 0.4091 \| 0.3041 \| 0.4429 \| \| 1.0578 \| 49.0 \| 5243 \| 1.0932 \| 0.2816 \| 0.5671 \| 0.2246 \| 0.2816 \| -1.0 \| -1.0 \| 0.3137 \| 0.479 \| 0.4943 \| 0.4943 \| -1.0 \| -1.0 \| 0.6069 \| 0.7537 \| 0.2616 \| 0.4873 \| 0.1947 \| 0.4026 \| 0.0613 \| 0.3964 \| 0.2835 \| 0.4316 \| \| 1.0367 \| 50.0 \| 5350 \| 1.0784 \| 0.2956 \| 0.5813 \| 0.2416 \| 0.2956 \| -1.0 \| -1.0 \| 0.3308 \| 0.4864 \| 0.4979 \| 0.4979 \| -1.0 \| -1.0 \| 0.6193 \| 0.7642 \| 0.2809 \| 0.5143 \| 0.1841 \| 0.3854 \| 0.0885 \| 0.3891 \| 0.3053 \| 0.4367 \| \| 1.0239 \| 51.0 \| 5457 \| 1.0702 \| 0.2893 \| 0.5689 \| 0.2387 \| 0.2893 \| -1.0 \| -1.0 \| 0.315 \| 0.4944 \| 0.5097 \| 0.5097 \| -1.0 \| -1.0 \| 0.5978 \| 0.763 \| 0.2656 \| 0.5095 \| 0.1887 \| 0.3938 \| 0.0817 \| 0.4309 \| 0.3125 \| 0.4514 \| \| 1.0127 \| 52.0 \| 5564 \| 1.0771 \| 0.2833 \| 0.5713 \| 0.243 \| 0.2833 \| -1.0 \| -1.0 \| 0.3184 \| 0.4856 \| 0.5075 \| 0.5075 \| -1.0 \| -1.0 \| 0.6007 \| 0.7605 \| 0.2649 \| 0.5175 \| 0.1843 \| 0.3932 \| 0.0766 \| 0.4364 \| 0.2902 \| 0.4299 \| \| 0.9948 \| 53.0 \| 5671 \| 1.1039 \| 0.2821 \| 0.5628 \| 0.2607 \| 0.2821 \| -1.0 \| -1.0 \| 0.3143 \| 0.4954 \| 0.5132 \| 0.5132 \| -1.0 \| -1.0 \| 0.6002 \| 0.7599 \| 0.2846 \| 0.5587 \| 0.1729 \| 0.3693 \| 0.0646 \| 0.4582 \| 0.2884 \| 0.4198 \| \| 1.0026 \| 54.0 \| 5778 \| 1.1125 \| 0.2811 \| 0.5569 \| 0.2461 \| 0.2811 \| -1.0 \| -1.0 \| 0.3166 \| 0.4846 \| 0.5019 \| 0.5019 \| -1.0 \| -1.0 \| 0.6118 \| 0.7691 \| 0.2617 \| 0.5286 \| 0.1762 \| 0.376 \| 0.0625 \| 0.3964 \| 0.2932 \| 0.4395 \| \| 1.0037 \| 55.0 \| 5885 \| 1.1172 \| 0.2819 \| 0.5757 \| 0.2532 \| 0.2819 \| -1.0 \| -1.0 \| 0.3089 \| 0.4653 \| 0.4811 \| 0.4811 \| -1.0 \| -1.0 \| 0.5869 \| 0.7302 \| 0.2946 \| 0.5032 \| 0.1914 \| 0.388 \| 0.0565 \| 0.3745 \| 0.2802 \| 0.4096 \| \| 0.9892 \| 56.0 \| 5992 \| 1.0649 \| 0.311 \| 0.6027 \| 0.2673 \| 0.311 \| -1.0 \| -1.0 \| 0.3226 \| 0.4954 \| 0.515 \| 0.515 \| -1.0 \| -1.0 \| 0.629 \| 0.7679 \| 0.2972 \| 0.5556 \| 0.198 \| 0.3901 \| 0.1233 \| 0.4182 \| 0.3075 \| 0.4435 \| \| 0.98 \| 57.0 \| 6099 \| 1.0413 \| 0.3137 \| 0.6244 \| 0.2912 \| 0.3137 \| -1.0 \| -1.0 \| 0.3293 \| 0.5021 \| 0.5171 \| 0.5171 \| -1.0 \| -1.0 \| 0.6288 \| 0.7599 \| 0.3193 \| 0.5698 \| 0.2062 \| 0.399 \| 0.1101 \| 0.4236 \| 0.3043 \| 0.4333 \| \| 0.9682 \| 58.0 \| 6206 \| 1.0301 \| 0.3128 \| 0.6003 \| 0.2801 \| 0.3128 \| -1.0 \| -1.0 \| 0.3278 \| 0.4963 \| 0.517 \| 0.517 \| -1.0 \| -1.0 \| 0.632 \| 0.7704 \| 0.3038 \| 0.5238 \| 0.2093 \| 0.4203 \| 0.1204 \| 0.4164 \| 0.2984 \| 0.4542 \| \| 0.9533 \| 59.0 \| 6313 \| 1.0755 \| 0.2928 \| 0.5954 \| 0.25 \| 0.2928 \| -1.0 \| -1.0 \| 0.3177 \| 0.4937 \| 0.5084 \| 0.5084 \| -1.0 \| -1.0 \| 0.6102 \| 0.7401 \| 0.2906 \| 0.5635 \| 0.1942 \| 0.399 \| 0.1002 \| 0.4218 \| 0.2688 \| 0.4175 \| \| 0.9589 \| 60.0 \| 6420 \| 1.0364 \| 0.3213 \| 0.6182 \| 0.2717 \| 0.3213 \| -1.0 \| -1.0 \| 0.3383 \| 0.5121 \| 0.5273 \| 0.5273 \| -1.0 \| -1.0 \| 0.622 \| 0.7617 \| 0.3318 \| 0.5683 \| 0.2273 \| 0.4193 \| 0.1243 \| 0.4236 \| 0.301 \| 0.4638 \| \| 0.9398 \| 61.0 \| 6527 \| 1.0653 \| 0.3089 \| 0.6099 \| 0.2673 \| 0.3089 \| -1.0 \| -1.0 \| 0.3325 \| 0.4946 \| 0.5046 \| 0.5046 \| -1.0 \| -1.0 \| 0.6166 \| 0.7556 \| 0.3144 \| 0.5286 \| 0.177 \| 0.376 \| 0.1451 \| 0.4127 \| 0.2915 \| 0.4503 \| \| 0.924 \| 62.0 \| 6634 \| 1.0409 \| 0.319 \| 0.6335 \| 0.2641 \| 0.319 \| -1.0 \| -1.0 \| 0.3437 \| 0.5085 \| 0.5234 \| 0.5234 \| -1.0 \| -1.0 \| 0.6223 \| 0.7537 \| 0.3263 \| 0.5476 \| 0.21 \| 0.4021 \| 0.1269 \| 0.4582 \| 0.3093 \| 0.4554 \| \| 0.924 \| 63.0 \| 6741 \| 1.0517 \| 0.3212 \| 0.6485 \| 0.2727 \| 0.3212 \| -1.0 \| -1.0 \| 0.3382 \| 0.5109 \| 0.5232 \| 0.5232 \| -1.0 \| -1.0 \| 0.6392 \| 0.7704 \| 0.3195 \| 0.5857 \| 0.1967 \| 0.399 \| 0.1268 \| 0.4218 \| 0.3241 \| 0.439 \| \| 0.9458 \| 64.0 \| 6848 \| 1.0314 \| 0.3213 \| 0.6357 \| 0.28 \| 0.3213 \| -1.0 \| -1.0 \| 0.3414 \| 0.5152 \| 0.5278 \| 0.5278 \| -1.0 \| -1.0 \| 0.6272 \| 0.7648 \| 0.3137 \| 0.573 \| 0.227 \| 0.4078 \| 0.1257 \| 0.4545 \| 0.3129 \| 0.439 \| \| 0.9136 \| 65.0 \| 6955 \| 1.0237 \| 0.3262 \| 0.6377 \| 0.284 \| 0.3262 \| -1.0 \| -1.0 \| 0.3489 \| 0.5157 \| 0.5299 \| 0.5299 \| -1.0 \| -1.0 \| 0.6277 \| 0.766 \| 0.3294 \| 0.5587 \| 0.2223 \| 0.3958 \| 0.13 \| 0.4655 \| 0.3216 \| 0.4633 \| \| 0.8969 \| 66.0 \| 7062 \| 1.0190 \| 0.3188 \| 0.6478 \| 0.2833 \| 0.3188 \| -1.0 \| -1.0 \| 0.3456 \| 0.5153 \| 0.528 \| 0.528 \| -1.0 \| -1.0 \| 0.6254 \| 0.7704 \| 0.3338 \| 0.5762 \| 0.187 \| 0.3859 \| 0.1258 \| 0.4582 \| 0.322 \| 0.4492 \| \| 0.8917 \| 67.0 \| 7169 \| 0.9900 \| 0.326 \| 0.6304 \| 0.281 \| 0.326 \| -1.0 \| -1.0 \| 0.354 \| 0.5289 \| 0.5382 \| 0.5382 \| -1.0 \| -1.0 \| 0.6478 \| 0.7759 \| 0.3118 \| 0.5556 \| 0.2245 \| 0.4115 \| 0.1149 \| 0.4691 \| 0.3312 \| 0.4791 \| \| 0.8724 \| 68.0 \| 7276 \| 1.0181 \| 0.3141 \| 0.6119 \| 0.2757 \| 0.3141 \| -1.0 \| -1.0 \| 0.3398 \| 0.517 \| 0.5303 \| 0.5303 \| -1.0 \| -1.0 \| 0.642 \| 0.7716 \| 0.3118 \| 0.5778 \| 0.2091 \| 0.4177 \| 0.096 \| 0.4255 \| 0.3116 \| 0.4588 \| \| 0.8881 \| 69.0 \| 7383 \| 1.0231 \| 0.3119 \| 0.63 \| 0.2566 \| 0.3119 \| -1.0 \| -1.0 \| 0.3438 \| 0.507 \| 0.5148 \| 0.5148 \| -1.0 \| -1.0 \| 0.6278 \| 0.7556 \| 0.3062 \| 0.5476 \| 0.2077 \| 0.4052 \| 0.0951 \| 0.4073 \| 0.3228 \| 0.4582 \| \| 0.8604 \| 70.0 \| 7490 \| 1.0245 \| 0.3266 \| 0.6436 \| 0.2621 \| 0.3266 \| -1.0 \| -1.0 \| 0.3448 \| 0.5179 \| 0.5273 \| 0.5273 \| -1.0 \| -1.0 \| 0.6251 \| 0.7623 \| 0.3301 \| 0.5889 \| 0.2223 \| 0.4115 \| 0.1406 \| 0.4255 \| 0.315 \| 0.4486 \| \| 0.86 \| 71.0 \| 7597 \| 1.0249 \| 0.3226 \| 0.6426 \| 0.2709 \| 0.3226 \| -1.0 \| -1.0 \| 0.3442 \| 0.5201 \| 0.5325 \| 0.5325 \| -1.0 \| -1.0 \| 0.6353 \| 0.7716 \| 0.3171 \| 0.6032 \| 0.2233 \| 0.4177 \| 0.1289 \| 0.4236 \| 0.3082 \| 0.4463 \| \| 0.8452 \| 72.0 \| 7704 \| 1.0383 \| 0.3225 \| 0.6364 \| 0.2629 \| 0.3225 \| -1.0 \| -1.0 \| 0.344 \| 0.5124 \| 0.5223 \| 0.5223 \| -1.0 \| -1.0 \| 0.6375 \| 0.7728 \| 0.322 \| 0.5794 \| 0.2082 \| 0.399 \| 0.1443 \| 0.4291 \| 0.3003 \| 0.4311 \| \| 0.8444 \| 73.0 \| 7811 \| 1.0018 \| 0.3312 \| 0.6454 \| 0.2914 \| 0.3312 \| -1.0 \| -1.0 \| 0.3558 \| 0.5223 \| 0.535 \| 0.535 \| -1.0 \| -1.0 \| 0.6395 \| 0.771 \| 0.3471 \| 0.5841 \| 0.2158 \| 0.4182 \| 0.1433 \| 0.4345 \| 0.3105 \| 0.4672 \| \| 0.8325 \| 74.0 \| 7918 \| 1.0412 \| 0.3296 \| 0.6574 \| 0.272 \| 0.3296 \| -1.0 \| -1.0 \| 0.3404 \| 0.5203 \| 0.5312 \| 0.5312 \| -1.0 \| -1.0 \| 0.6449 \| 0.7722 \| 0.3422 \| 0.6032 \| 0.2302 \| 0.4125 \| 0.1352 \| 0.4509 \| 0.2956 \| 0.4169 \| \| 0.8438 \| 75.0 \| 8025 \| 1.0015 \| 0.34 \| 0.6482 \| 0.3027 \| 0.34 \| -1.0 \| -1.0 \| 0.3487 \| 0.5211 \| 0.5333 \| 0.5333 \| -1.0 \| -1.0 \| 0.6449 \| 0.7691 \| 0.3555 \| 0.5968 \| 0.2324 \| 0.4172 \| 0.1427 \| 0.4382 \| 0.3248 \| 0.4452 \| \| 0.8197 \| 76.0 \| 8132 \| 1.0196 \| 0.3426 \| 0.6544 \| 0.2931 \| 0.3426 \| -1.0 \| -1.0 \| 0.356 \| 0.5234 \| 0.5347 \| 0.5347 \| -1.0 \| -1.0 \| 0.6443 \| 0.7722 \| 0.35 \| 0.5794 \| 0.2465 \| 0.4172 \| 0.1434 \| 0.4455 \| 0.3288 \| 0.4593 \| \| 0.8241 \| 77.0 \| 8239 \| 1.0215 \| 0.339 \| 0.6531 \| 0.3012 \| 0.339 \| -1.0 \| -1.0 \| 0.3523 \| 0.5162 \| 0.5279 \| 0.5279 \| -1.0 \| -1.0 \| 0.6411 \| 0.771 \| 0.3486 \| 0.5921 \| 0.2324 \| 0.4073 \| 0.1375 \| 0.4091 \| 0.3357 \| 0.4599 \| \| 0.8019 \| 78.0 \| 8346 \| 1.0356 \| 0.3265 \| 0.6279 \| 0.2799 \| 0.3265 \| -1.0 \| -1.0 \| 0.3503 \| 0.5169 \| 0.5264 \| 0.5264 \| -1.0 \| -1.0 \| 0.639 \| 0.771 \| 0.3313 \| 0.5762 \| 0.2315 \| 0.413 \| 0.1144 \| 0.4127 \| 0.3162 \| 0.4593 \| \| 0.8113 \| 79.0 \| 8453 \| 1.0087 \| 0.3377 \| 0.6556 \| 0.3059 \| 0.3377 \| -1.0 \| -1.0 \| 0.3487 \| 0.5269 \| 0.5369 \| 0.5369 \| -1.0 \| -1.0 \| 0.6396 \| 0.7765 \| 0.3392 \| 0.5873 \| 0.2336 \| 0.4068 \| 0.1513 \| 0.4564 \| 0.3249 \| 0.4576 \| \| 0.8043 \| 80.0 \| 8560 \| 1.0225 \| 0.3351 \| 0.6526 \| 0.3035 \| 0.3351 \| -1.0 \| -1.0 \| 0.3485 \| 0.5177 \| 0.5285 \| 0.5285 \| -1.0 \| -1.0 \| 0.6529 \| 0.7827 \| 0.331 \| 0.5778 \| 0.2224 \| 0.399 \| 0.1421 \| 0.4255 \| 0.3273 \| 0.4576 \| \| 0.7909 \| 81.0 \| 8667 \| 1.0009 \| 0.345 \| 0.6563 \| 0.3086 \| 0.345 \| -1.0 \| -1.0 \| 0.3512 \| 0.522 \| 0.5359 \| 0.5359 \| -1.0 \| -1.0 \| 0.6649 \| 0.7877 \| 0.3589 \| 0.5714 \| 0.2366 \| 0.4219 \| 0.1374 \| 0.4418 \| 0.3272 \| 0.4565 \| \| 0.8034 \| 82.0 \| 8774 \| 1.0006 \| 0.333 \| 0.6455 \| 0.295 \| 0.333 \| -1.0 \| -1.0 \| 0.3529 \| 0.5257 \| 0.5369 \| 0.5369 \| -1.0 \| -1.0 \| 0.6387 \| 0.7741 \| 0.3352 \| 0.5873 \| 0.2342 \| 0.4208 \| 0.1362 \| 0.4491 \| 0.3206 \| 0.4531 \| \| 0.7744 \| 83.0 \| 8881 \| 0.9946 \| 0.341 \| 0.6513 \| 0.3182 \| 0.341 \| -1.0 \| -1.0 \| 0.3615 \| 0.5273 \| 0.5381 \| 0.5381 \| -1.0 \| -1.0 \| 0.6425 \| 0.7778 \| 0.3636 \| 0.5873 \| 0.2404 \| 0.4198 \| 0.1246 \| 0.4418 \| 0.3341 \| 0.4638 \| \| 0.7769 \| 84.0 \| 8988 \| 0.9994 \| 0.3406 \| 0.6523 \| 0.3009 \| 0.3406 \| -1.0 \| -1.0 \| 0.3637 \| 0.5304 \| 0.5384 \| 0.5384 \| -1.0 \| -1.0 \| 0.6478 \| 0.7728 \| 0.3472 \| 0.5968 \| 0.2355 \| 0.4198 \| 0.1444 \| 0.4382 \| 0.3281 \| 0.4644 \| \| 0.7679 \| 85.0 \| 9095 \| 1.0170 \| 0.3397 \| 0.6549 \| 0.2888 \| 0.3397 \| -1.0 \| -1.0 \| 0.3624 \| 0.5301 \| 0.5407 \| 0.5407 \| -1.0 \| -1.0 \| 0.6485 \| 0.7685 \| 0.3431 \| 0.6079 \| 0.2294 \| 0.4167 \| 0.1607 \| 0.46 \| 0.317 \| 0.4503 \| \| 0.772 \| 86.0 \| 9202 \| 0.9953 \| 0.3515 \| 0.6722 \| 0.3099 \| 0.3515 \| -1.0 \| -1.0 \| 0.3674 \| 0.5331 \| 0.543 \| 0.543 \| -1.0 \| -1.0 \| 0.6599 \| 0.7778 \| 0.3692 \| 0.6032 \| 0.24 \| 0.4187 \| 0.1523 \| 0.46 \| 0.3361 \| 0.4554 \| \| 0.783 \| 87.0 \| 9309 \| 1.0003 \| 0.3401 \| 0.6545 \| 0.2943 \| 0.3401 \| -1.0 \| -1.0 \| 0.3603 \| 0.5309 \| 0.5457 \| 0.5457 \| -1.0 \| -1.0 \| 0.651 \| 0.7759 \| 0.3521 \| 0.6222 \| 0.2296 \| 0.4161 \| 0.1433 \| 0.46 \| 0.3246 \| 0.4542 \| \| 0.7508 \| 88.0 \| 9416 \| 0.9849 \| 0.3517 \| 0.6734 \| 0.3217 \| 0.3517 \| -1.0 \| -1.0 \| 0.362 \| 0.5333 \| 0.5447 \| 0.5447 \| -1.0 \| -1.0 \| 0.6572 \| 0.7747 \| 0.369 \| 0.5984 \| 0.2439 \| 0.4318 \| 0.1503 \| 0.4527 \| 0.338 \| 0.4661 \| \| 0.7558 \| 89.0 \| 9523 \| 0.9861 \| 0.3517 \| 0.6749 \| 0.3173 \| 0.3517 \| -1.0 \| -1.0 \| 0.3628 \| 0.5329 \| 0.5408 \| 0.5408 \| -1.0 \| -1.0 \| 0.6469 \| 0.7728 \| 0.3661 \| 0.5889 \| 0.2323 \| 0.4104 \| 0.1747 \| 0.4709 \| 0.3385 \| 0.461 \| \| 0.7398 \| 90.0 \| 9630 \| 0.9966 \| 0.3565 \| 0.6784 \| 0.315 \| 0.3565 \| -1.0 \| -1.0 \| 0.3621 \| 0.5367 \| 0.5484 \| 0.5484 \| -1.0 \| -1.0 \| 0.6524 \| 0.7796 \| 0.3655 \| 0.6127 \| 0.2374 \| 0.4151 \| 0.1769 \| 0.4655 \| 0.3503 \| 0.4689 \| \| 0.7532 \| 91.0 \| 9737 \| 0.9883 \| 0.354 \| 0.6709 \| 0.3179 \| 0.354 \| -1.0 \| -1.0 \| 0.3623 \| 0.5395 \| 0.5475 \| 0.5475 \| -1.0 \| -1.0 \| 0.6551 \| 0.779 \| 0.3669 \| 0.6175 \| 0.2432 \| 0.4187 \| 0.1545 \| 0.4564 \| 0.3502 \| 0.4661 \| \| 0.7417 \| 92.0 \| 9844 \| 0.9932 \| 0.3557 \| 0.6777 \| 0.3131 \| 0.3557 \| -1.0 \| -1.0 \| 0.3629 \| 0.5361 \| 0.5465 \| 0.5465 \| -1.0 \| -1.0 \| 0.6566 \| 0.7765 \| 0.3713 \| 0.6143 \| 0.2425 \| 0.4224 \| 0.163 \| 0.4491 \| 0.3451 \| 0.4701 \| \| 0.7554 \| 93.0 \| 9951 \| 0.9921 \| 0.3598 \| 0.6841 \| 0.3212 \| 0.3598 \| -1.0 \| -1.0 \| 0.3643 \| 0.5375 \| 0.5466 \| 0.5466 \| -1.0 \| -1.0 \| 0.6591 \| 0.7821 \| 0.3696 \| 0.5952 \| 0.2482 \| 0.4214 \| 0.175 \| 0.4691 \| 0.3469 \| 0.465 \| \| 0.7339 \| 94.0 \| 10058 \| 1.0049 \| 0.3587 \| 0.6838 \| 0.3184 \| 0.3587 \| -1.0 \| -1.0 \| 0.3668 \| 0.5347 \| 0.5447 \| 0.5447 \| -1.0 \| -1.0 \| 0.658 \| 0.7753 \| 0.3767 \| 0.6048 \| 0.2434 \| 0.4177 \| 0.1744 \| 0.46 \| 0.3411 \| 0.4655 \| \| 0.7184 \| 95.0 \| 10165 \| 0.9969 \| 0.3547 \| 0.6739 \| 0.3214 \| 0.3547 \| -1.0 \| -1.0 \| 0.3633 \| 0.5353 \| 0.5449 \| 0.5449 \| -1.0 \| -1.0 \| 0.6521 \| 0.7722 \| 0.369 \| 0.6079 \| 0.2402 \| 0.413 \| 0.1707 \| 0.4673 \| 0.3412 \| 0.4638 \| \| 0.7448 \| 96.0 \| 10272 \| 0.9925 \| 0.3556 \| 0.6708 \| 0.3061 \| 0.3556 \| -1.0 \| -1.0 \| 0.365 \| 0.5327 \| 0.544 \| 0.544 \| -1.0 \| -1.0 \| 0.6517 \| 0.7765 \| 0.3707 \| 0.6016 \| 0.2354 \| 0.4208 \| 0.1781 \| 0.4527 \| 0.3421 \| 0.4684 \| \| 0.7264 \| 97.0 \| 10379 \| 0.9914 \| 0.3576 \| 0.6793 \| 0.315 \| 0.3576 \| -1.0 \| -1.0 \| 0.3657 \| 0.5332 \| 0.5439 \| 0.5439 \| -1.0 \| -1.0 \| 0.6517 \| 0.7753 \| 0.378 \| 0.6 \| 0.2464 \| 0.426 \| 0.1707 \| 0.4545 \| 0.3414 \| 0.4638 \| \| 0.7455 \| 98.0 \| 10486 \| 0.9829 \| 0.3586 \| 0.6794 \| 0.3132 \| 0.3586 \| -1.0 \| -1.0 \| 0.367 \| 0.5353 \| 0.5473 \| 0.5473 \| -1.0 \| -1.0 \| 0.6586 \| 0.7772 \| 0.3781 \| 0.6079 \| 0.2468 \| 0.4266 \| 0.1742 \| 0.4582 \| 0.3355 \| 0.4667 \| \| 0.7105 \| 99.0 \| 10593 \| 0.9862 \| 0.3577 \| 0.6795 \| 0.3089 \| 0.3577 \| -1.0 \| -1.0 \| 0.3649 \| 0.5352 \| 0.5476 \| 0.5476 \| -1.0 \| -1.0 \| 0.6591 \| 0.7778 \| 0.3712 \| 0.6032 \| 0.2481 \| 0.4271 \| 0.1727 \| 0.4655 \| 0.3376 \| 0.4644 \| \| 0.7089 \| 100.0 \| 10700 \| 0.9865 \| 0.3578 \| 0.6781 \| 0.3105 \| 0.3578 \| -1.0 \| -1.0 \| 0.365 \| 0.535 \| 0.5483 \| 0.5483 \| -1.0 \| -1.0 \| 0.6584 \| 0.7772 \| 0.3691 \| 0.6063 \| 0.2477 \| 0.4266 \| 0.1766 \| 0.4655 \| 0.3371 \| 0.4661 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
mali17361/detr-finetuned-table-v1	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ChingizA/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7117 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.0345 \| 0.08 \| 200 \| 1.8447 \| \| 1.5511 \| 0.16 \| 400 \| 1.4217 \| \| 1.444 \| 0.24 \| 600 \| 1.3814 \| \| 1.3746 \| 0.32 \| 800 \| 1.3241 \| \| 1.2361 \| 0.4 \| 1000 \| 1.2589 \| \| 1.3506 \| 0.48 \| 1200 \| 1.2441 \| \| 1.2833 \| 0.56 \| 1400 \| 1.2052 \| \| 1.1051 \| 0.64 \| 1600 \| 1.0607 \| \| 1.1091 \| 0.72 \| 1800 \| 1.0610 \| \| 1.0295 \| 0.8 \| 2000 \| 1.0241 \| \| 1.1376 \| 0.88 \| 2200 \| 1.0846 \| \| 1.1172 \| 0.96 \| 2400 \| 1.1095 \| \| 1.0186 \| 1.04 \| 2600 \| 0.9978 \| \| 1.0775 \| 1.12 \| 2800 \| 1.0225 \| \| 0.9973 \| 1.2 \| 3000 \| 0.9934 \| \| 1.006 \| 1.28 \| 3200 \| 0.9886 \| \| 0.9814 \| 1.36 \| 3400 \| 0.9256 \| \| 1.0253 \| 1.44 \| 3600 \| 0.9209 \| \| 0.9932 \| 1.52 \| 3800 \| 0.9159 \| \| 0.9307 \| 1.6 \| 4000 \| 0.9058 \| \| 0.9103 \| 1.68 \| 4200 \| 0.9049 \| \| 0.9034 \| 1.76 \| 4400 \| 0.8643 \| \| 0.9544 \| 1.84 \| 4600 \| 0.9114 \| \| 0.889 \| 1.92 \| 4800 \| 0.8880 \| \| 0.8888 \| 2.0 \| 5000 \| 0.8515 \| \| 0.8877 \| 2.08 \| 5200 \| 0.8707 \| \| 0.8799 \| 2.16 \| 5400 \| 0.8458 \| \| 0.8398 \| 2.24 \| 5600 \| 0.8292 \| \| 0.8181 \| 2.32 \| 5800 \| 0.8226 \| \| 0.8876 \| 2.4 \| 6000 \| 0.8021 \| \| 0.8893 \| 2.48 \| 6200 \| 0.8173 \| \| 0.8497 \| 2.56 \| 6400 \| 0.7870 \| \| 0.8369 \| 2.64 \| 6600 \| 0.7719 \| \| 0.8213 \| 2.72 \| 6800 \| 0.7877 \| \| 0.8044 \| 2.8 \| 7000 \| 0.7763 \| \| 0.8087 \| 2.88 \| 7200 \| 0.7702 \| \| 0.7616 \| 2.96 \| 7400 \| 0.7570 \| \| 0.7901 \| 3.04 \| 7600 \| 0.7451 \| \| 0.8454 \| 3.12 \| 7800 \| 0.7560 \| \| 0.7428 \| 3.2 \| 8000 \| 0.7455 \| \| 0.822 \| 3.28 \| 8200 \| 0.7390 \| \| 0.8293 \| 3.36 \| 8400 \| 0.7324 \| \| 0.7196 \| 3.44 \| 8600 \| 0.7270 \| \| 0.7508 \| 3.52 \| 8800 \| 0.7357 \| \| 0.783 \| 3.6 \| 9000 \| 0.7293 \| \| 0.7094 \| 3.68 \| 9200 \| 0.7276 \| \| 0.7811 \| 3.76 \| 9400 \| 0.7178 \| \| 0.7765 \| 3.84 \| 9600 \| 0.7129 \| \| 0.7542 \| 3.92 \| 9800 \| 0.7165 \| \| 0.756 \| 4.0 \| 10000 \| 0.7117 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
mali17361/detr-finetuned-balloon-v1	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]
Ldicet/detr-jan	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-jan This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "zs", "znr", "zst" ]
sekhharr/detr_finetuned_v10_last_checkpoint	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]
qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Map: 0.3131 - Map 50: 0.6268 - Map 75: 0.2698 - Map Small: 0.2089 - Map Medium: 0.2237 - Map Large: 0.5358 - Mar 1: 0.3352 - Mar 10: 0.4586 - Mar 100: 0.4682 - Mar Small: 0.2773 - Mar Medium: 0.3748 - Mar Large: 0.6417 - Map Coverall: 0.5362 - Mar 100 Coverall: 0.6844 - Map Face Shield: 0.3199 - Mar 100 Face Shield: 0.4635 - Map Gloves: 0.1628 - Mar 100 Gloves: 0.3176 - Map Goggles: 0.2427 - Mar 100 Goggles: 0.4475 - Map Mask: 0.3038 - Mar 100 Mask: 0.4278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 13.6734 \| 1.0 \| 107 \| 3.0782 \| 0.0022 \| 0.009 \| 0.0004 \| 0.0002 \| 0.0002 \| 0.0027 \| 0.0054 \| 0.0291 \| 0.0632 \| 0.0028 \| 0.0108 \| 0.0955 \| 0.0105 \| 0.2595 \| 0.0 \| 0.0 \| 0.0 \| 0.0126 \| 0.0 \| 0.0 \| 0.0005 \| 0.0438 \| \| 2.9712 \| 2.0 \| 214 \| 2.7340 \| 0.0089 \| 0.0267 \| 0.0034 \| 0.0036 \| 0.0017 \| 0.0114 \| 0.0186 \| 0.0749 \| 0.1162 \| 0.0082 \| 0.0358 \| 0.1266 \| 0.0374 \| 0.4457 \| 0.0 \| 0.0 \| 0.0004 \| 0.0101 \| 0.0 \| 0.0 \| 0.0069 \| 0.1253 \| \| 2.6599 \| 3.0 \| 321 \| 2.6057 \| 0.0122 \| 0.0432 \| 0.0034 \| 0.0011 \| 0.003 \| 0.0138 \| 0.0281 \| 0.1003 \| 0.1355 \| 0.0324 \| 0.0542 \| 0.1526 \| 0.0538 \| 0.4428 \| 0.0004 \| 0.0288 \| 0.0003 \| 0.0322 \| 0.0 \| 0.0049 \| 0.0064 \| 0.1686 \| \| 2.53 \| 4.0 \| 428 \| 2.3681 \| 0.0187 \| 0.0542 \| 0.0099 \| 0.0061 \| 0.0057 \| 0.0372 \| 0.0494 \| 0.1502 \| 0.1831 \| 0.0272 \| 0.1134 \| 0.1631 \| 0.0737 \| 0.5191 \| 0.0032 \| 0.1 \| 0.0012 \| 0.0487 \| 0.0058 \| 0.0656 \| 0.0097 \| 0.182 \| \| 2.3495 \| 5.0 \| 535 \| 2.2947 \| 0.0282 \| 0.0843 \| 0.0163 \| 0.0163 \| 0.0175 \| 0.0387 \| 0.0796 \| 0.194 \| 0.2282 \| 0.0407 \| 0.1538 \| 0.2516 \| 0.0928 \| 0.5798 \| 0.015 \| 0.1577 \| 0.0009 \| 0.0563 \| 0.0131 \| 0.123 \| 0.0193 \| 0.2242 \| \| 2.2478 \| 6.0 \| 642 \| 2.2016 \| 0.0346 \| 0.0914 \| 0.0186 \| 0.0079 \| 0.0116 \| 0.0668 \| 0.0938 \| 0.2406 \| 0.2729 \| 0.0362 \| 0.1578 \| 0.4043 \| 0.1208 \| 0.6121 \| 0.0178 \| 0.2577 \| 0.0021 \| 0.0925 \| 0.0152 \| 0.2033 \| 0.017 \| 0.199 \| \| 2.1515 \| 7.0 \| 749 \| 2.1026 \| 0.0424 \| 0.1121 \| 0.0226 \| 0.0162 \| 0.0197 \| 0.0747 \| 0.1058 \| 0.2613 \| 0.2973 \| 0.0996 \| 0.2269 \| 0.3484 \| 0.1382 \| 0.5769 \| 0.0236 \| 0.3 \| 0.0052 \| 0.1216 \| 0.0125 \| 0.2246 \| 0.0327 \| 0.2634 \| \| 2.0571 \| 8.0 \| 856 \| 2.1115 \| 0.0485 \| 0.1183 \| 0.0346 \| 0.0078 \| 0.0287 \| 0.0922 \| 0.1332 \| 0.2587 \| 0.2995 \| 0.0573 \| 0.1973 \| 0.424 \| 0.1446 \| 0.6318 \| 0.0442 \| 0.2808 \| 0.0027 \| 0.1211 \| 0.0182 \| 0.1836 \| 0.0327 \| 0.2804 \| \| 1.9981 \| 9.0 \| 963 \| 2.0429 \| 0.0433 \| 0.1161 \| 0.0242 \| 0.0239 \| 0.0216 \| 0.0831 \| 0.1163 \| 0.2543 \| 0.2985 \| 0.1208 \| 0.1892 \| 0.4359 \| 0.1403 \| 0.6358 \| 0.0251 \| 0.2365 \| 0.0059 \| 0.1673 \| 0.0187 \| 0.1754 \| 0.0265 \| 0.2773 \| \| 1.9338 \| 10.0 \| 1070 \| 1.9025 \| 0.079 \| 0.1694 \| 0.061 \| 0.0175 \| 0.0481 \| 0.1245 \| 0.1621 \| 0.313 \| 0.3524 \| 0.1106 \| 0.2565 \| 0.5211 \| 0.2324 \| 0.6809 \| 0.0753 \| 0.3269 \| 0.0094 \| 0.198 \| 0.0112 \| 0.2328 \| 0.0667 \| 0.3232 \| \| 1.8745 \| 11.0 \| 1177 \| 1.8462 \| 0.0938 \| 0.2008 \| 0.0772 \| 0.0191 \| 0.0666 \| 0.1644 \| 0.1632 \| 0.3078 \| 0.3485 \| 0.1358 \| 0.2725 \| 0.483 \| 0.3093 \| 0.6618 \| 0.0471 \| 0.3442 \| 0.0104 \| 0.2005 \| 0.0122 \| 0.2033 \| 0.0899 \| 0.3325 \| \| 1.7669 \| 12.0 \| 1284 \| 1.7660 \| 0.12 \| 0.271 \| 0.0939 \| 0.0298 \| 0.0714 \| 0.2035 \| 0.1928 \| 0.3391 \| 0.366 \| 0.1539 \| 0.2852 \| 0.5395 \| 0.368 \| 0.6566 \| 0.0741 \| 0.3327 \| 0.0236 \| 0.2442 \| 0.0363 \| 0.2459 \| 0.0981 \| 0.3505 \| \| 1.7122 \| 13.0 \| 1391 \| 1.7355 \| 0.1376 \| 0.2863 \| 0.1139 \| 0.027 \| 0.0899 \| 0.2379 \| 0.2151 \| 0.3567 \| 0.3878 \| 0.1285 \| 0.2998 \| 0.5798 \| 0.3958 \| 0.637 \| 0.0969 \| 0.3712 \| 0.0389 \| 0.2548 \| 0.0271 \| 0.3115 \| 0.1294 \| 0.3644 \| \| 1.6336 \| 14.0 \| 1498 \| 1.6695 \| 0.1609 \| 0.3374 \| 0.1317 \| 0.0196 \| 0.107 \| 0.2757 \| 0.2211 \| 0.3673 \| 0.3964 \| 0.1449 \| 0.3237 \| 0.5801 \| 0.4353 \| 0.6595 \| 0.146 \| 0.3923 \| 0.034 \| 0.2482 \| 0.0437 \| 0.3361 \| 0.1455 \| 0.3459 \| \| 1.5696 \| 15.0 \| 1605 \| 1.6275 \| 0.1601 \| 0.3509 \| 0.127 \| 0.0231 \| 0.102 \| 0.2989 \| 0.2181 \| 0.3813 \| 0.4083 \| 0.1688 \| 0.3313 \| 0.5781 \| 0.4323 \| 0.6549 \| 0.1061 \| 0.4038 \| 0.0618 \| 0.3005 \| 0.029 \| 0.3246 \| 0.1714 \| 0.3577 \| \| 1.5149 \| 16.0 \| 1712 \| 1.5810 \| 0.1862 \| 0.3984 \| 0.1463 \| 0.0622 \| 0.1143 \| 0.3439 \| 0.2268 \| 0.3819 \| 0.4019 \| 0.1881 \| 0.3158 \| 0.6013 \| 0.4779 \| 0.6624 \| 0.1141 \| 0.4 \| 0.0694 \| 0.2668 \| 0.0704 \| 0.3262 \| 0.1991 \| 0.3541 \| \| 1.4732 \| 17.0 \| 1819 \| 1.5458 \| 0.1905 \| 0.4135 \| 0.1472 \| 0.0514 \| 0.1196 \| 0.3237 \| 0.2289 \| 0.3868 \| 0.4171 \| 0.167 \| 0.3378 \| 0.5905 \| 0.4746 \| 0.6538 \| 0.1547 \| 0.4519 \| 0.0932 \| 0.3055 \| 0.0488 \| 0.3279 \| 0.1812 \| 0.3464 \| \| 1.4327 \| 18.0 \| 1926 \| 1.5617 \| 0.1887 \| 0.4038 \| 0.142 \| 0.0323 \| 0.1178 \| 0.3574 \| 0.2395 \| 0.3855 \| 0.4114 \| 0.1902 \| 0.3236 \| 0.589 \| 0.4801 \| 0.6595 \| 0.1518 \| 0.4462 \| 0.0866 \| 0.2829 \| 0.0389 \| 0.3197 \| 0.186 \| 0.349 \| \| 1.3925 \| 19.0 \| 2033 \| 1.5032 \| 0.2162 \| 0.4531 \| 0.1701 \| 0.0303 \| 0.1292 \| 0.3854 \| 0.2572 \| 0.4077 \| 0.4328 \| 0.1759 \| 0.3377 \| 0.6233 \| 0.5043 \| 0.6734 \| 0.169 \| 0.4442 \| 0.1341 \| 0.3221 \| 0.0725 \| 0.3754 \| 0.201 \| 0.349 \| \| 1.3582 \| 20.0 \| 2140 \| 1.4728 \| 0.2077 \| 0.4357 \| 0.1744 \| 0.0412 \| 0.1414 \| 0.3759 \| 0.2516 \| 0.4017 \| 0.4251 \| 0.1842 \| 0.3402 \| 0.6106 \| 0.4879 \| 0.6607 \| 0.1537 \| 0.4404 \| 0.1159 \| 0.2955 \| 0.0633 \| 0.359 \| 0.2178 \| 0.3701 \| \| 1.329 \| 21.0 \| 2247 \| 1.4826 \| 0.2062 \| 0.4509 \| 0.1642 \| 0.0325 \| 0.1256 \| 0.4185 \| 0.2559 \| 0.3974 \| 0.4214 \| 0.1371 \| 0.3201 \| 0.619 \| 0.4879 \| 0.6584 \| 0.146 \| 0.4212 \| 0.1136 \| 0.2698 \| 0.0884 \| 0.4164 \| 0.195 \| 0.3412 \| \| 1.3458 \| 22.0 \| 2354 \| 1.4653 \| 0.2244 \| 0.4636 \| 0.1859 \| 0.074 \| 0.1501 \| 0.4031 \| 0.272 \| 0.4102 \| 0.4276 \| 0.1735 \| 0.3346 \| 0.6213 \| 0.5139 \| 0.6711 \| 0.1848 \| 0.4712 \| 0.1083 \| 0.2769 \| 0.0732 \| 0.323 \| 0.2419 \| 0.3959 \| \| 1.3028 \| 23.0 \| 2461 \| 1.4139 \| 0.2289 \| 0.4906 \| 0.1886 \| 0.0572 \| 0.1524 \| 0.4043 \| 0.2721 \| 0.4191 \| 0.4353 \| 0.1771 \| 0.3517 \| 0.5964 \| 0.4964 \| 0.6653 \| 0.1865 \| 0.4481 \| 0.1575 \| 0.3211 \| 0.0746 \| 0.3738 \| 0.2293 \| 0.368 \| \| 1.2704 \| 24.0 \| 2568 \| 1.4173 \| 0.2371 \| 0.4973 \| 0.199 \| 0.0605 \| 0.1634 \| 0.419 \| 0.2752 \| 0.4172 \| 0.4401 \| 0.1776 \| 0.3515 \| 0.6027 \| 0.5033 \| 0.6636 \| 0.2403 \| 0.4596 \| 0.1149 \| 0.3005 \| 0.1009 \| 0.4066 \| 0.2261 \| 0.3701 \| \| 1.2379 \| 25.0 \| 2675 \| 1.3933 \| 0.2608 \| 0.5303 \| 0.2271 \| 0.0541 \| 0.188 \| 0.4582 \| 0.2849 \| 0.4189 \| 0.4396 \| 0.158 \| 0.3455 \| 0.6343 \| 0.5376 \| 0.6913 \| 0.2509 \| 0.4635 \| 0.1194 \| 0.3065 \| 0.1534 \| 0.3623 \| 0.2428 \| 0.3742 \| \| 1.2257 \| 26.0 \| 2782 \| 1.4209 \| 0.2467 \| 0.5049 \| 0.2078 \| 0.0557 \| 0.1592 \| 0.4446 \| 0.2708 \| 0.4135 \| 0.4292 \| 0.1526 \| 0.3298 \| 0.6245 \| 0.5239 \| 0.6636 \| 0.2282 \| 0.4288 \| 0.134 \| 0.294 \| 0.1079 \| 0.3902 \| 0.2394 \| 0.3696 \| \| 1.1706 \| 27.0 \| 2889 \| 1.3593 \| 0.2631 \| 0.5273 \| 0.2353 \| 0.0955 \| 0.1891 \| 0.4521 \| 0.2912 \| 0.4196 \| 0.4422 \| 0.1554 \| 0.3475 \| 0.6437 \| 0.5374 \| 0.685 \| 0.261 \| 0.4692 \| 0.1276 \| 0.2925 \| 0.1269 \| 0.3803 \| 0.2624 \| 0.384 \| \| 1.1512 \| 28.0 \| 2996 \| 1.3320 \| 0.27 \| 0.5371 \| 0.2171 \| 0.0727 \| 0.192 \| 0.456 \| 0.2981 \| 0.4398 \| 0.4609 \| 0.1954 \| 0.3663 \| 0.6427 \| 0.5406 \| 0.6971 \| 0.2264 \| 0.5 \| 0.1629 \| 0.3131 \| 0.1617 \| 0.4066 \| 0.2584 \| 0.3876 \| \| 1.1511 \| 29.0 \| 3103 \| 1.3592 \| 0.2641 \| 0.526 \| 0.2346 \| 0.0526 \| 0.1701 \| 0.498 \| 0.2977 \| 0.4249 \| 0.4395 \| 0.1836 \| 0.3263 \| 0.6367 \| 0.5376 \| 0.6717 \| 0.2129 \| 0.4154 \| 0.1414 \| 0.291 \| 0.1589 \| 0.418 \| 0.2697 \| 0.4015 \| \| 1.1312 \| 30.0 \| 3210 \| 1.3863 \| 0.2582 \| 0.5328 \| 0.2115 \| 0.0667 \| 0.1747 \| 0.4443 \| 0.283 \| 0.4188 \| 0.4418 \| 0.2191 \| 0.332 \| 0.6478 \| 0.5086 \| 0.6526 \| 0.2387 \| 0.4808 \| 0.1249 \| 0.295 \| 0.1609 \| 0.3787 \| 0.2579 \| 0.4021 \| \| 1.1207 \| 31.0 \| 3317 \| 1.3294 \| 0.2813 \| 0.5581 \| 0.252 \| 0.1121 \| 0.1988 \| 0.4749 \| 0.296 \| 0.4362 \| 0.4568 \| 0.2423 \| 0.3532 \| 0.6468 \| 0.5314 \| 0.6775 \| 0.2837 \| 0.4788 \| 0.1526 \| 0.3131 \| 0.1613 \| 0.4033 \| 0.2777 \| 0.4113 \| \| 1.0856 \| 32.0 \| 3424 \| 1.3426 \| 0.274 \| 0.565 \| 0.2282 \| 0.1612 \| 0.2023 \| 0.4789 \| 0.2989 \| 0.4375 \| 0.4541 \| 0.2492 \| 0.3497 \| 0.6661 \| 0.5195 \| 0.6844 \| 0.2952 \| 0.4731 \| 0.149 \| 0.298 \| 0.1551 \| 0.4393 \| 0.2516 \| 0.3758 \| \| 1.0909 \| 33.0 \| 3531 \| 1.3174 \| 0.2868 \| 0.5799 \| 0.2445 \| 0.0956 \| 0.2097 \| 0.4865 \| 0.3119 \| 0.4466 \| 0.4652 \| 0.2553 \| 0.3672 \| 0.653 \| 0.536 \| 0.704 \| 0.3096 \| 0.4788 \| 0.1576 \| 0.3211 \| 0.1758 \| 0.441 \| 0.2552 \| 0.3809 \| \| 1.0726 \| 34.0 \| 3638 \| 1.3767 \| 0.2751 \| 0.561 \| 0.2242 \| 0.0993 \| 0.1951 \| 0.4811 \| 0.2831 \| 0.4241 \| 0.452 \| 0.2247 \| 0.3529 \| 0.6578 \| 0.5375 \| 0.6919 \| 0.2638 \| 0.4692 \| 0.1562 \| 0.3075 \| 0.1456 \| 0.3885 \| 0.2724 \| 0.4026 \| \| 1.0799 \| 35.0 \| 3745 \| 1.3316 \| 0.2691 \| 0.5569 \| 0.2126 \| 0.1108 \| 0.1876 \| 0.4522 \| 0.2913 \| 0.4243 \| 0.4443 \| 0.2217 \| 0.3366 \| 0.6247 \| 0.5257 \| 0.6879 \| 0.2661 \| 0.4712 \| 0.1575 \| 0.3055 \| 0.1659 \| 0.3869 \| 0.2301 \| 0.3701 \| \| 1.0402 \| 36.0 \| 3852 \| 1.3501 \| 0.2735 \| 0.5757 \| 0.207 \| 0.1217 \| 0.2057 \| 0.4564 \| 0.284 \| 0.4377 \| 0.4568 \| 0.2358 \| 0.3814 \| 0.6465 \| 0.5171 \| 0.685 \| 0.2325 \| 0.4481 \| 0.1429 \| 0.3035 \| 0.2158 \| 0.4656 \| 0.2594 \| 0.382 \| \| 1.0531 \| 37.0 \| 3959 \| 1.3324 \| 0.2701 \| 0.5336 \| 0.2233 \| 0.0834 \| 0.1907 \| 0.4826 \| 0.2852 \| 0.4439 \| 0.4644 \| 0.2142 \| 0.3754 \| 0.6658 \| 0.5318 \| 0.7052 \| 0.2218 \| 0.4596 \| 0.1431 \| 0.2995 \| 0.1909 \| 0.4639 \| 0.2632 \| 0.3938 \| \| 1.028 \| 38.0 \| 4066 \| 1.3593 \| 0.2637 \| 0.5412 \| 0.2147 \| 0.0638 \| 0.1752 \| 0.4642 \| 0.2853 \| 0.4231 \| 0.4438 \| 0.1997 \| 0.3274 \| 0.6417 \| 0.5256 \| 0.6705 \| 0.2526 \| 0.4327 \| 0.1312 \| 0.2854 \| 0.1766 \| 0.4344 \| 0.2324 \| 0.3959 \| \| 1.0211 \| 39.0 \| 4173 \| 1.3359 \| 0.2728 \| 0.5553 \| 0.2294 \| 0.1086 \| 0.1991 \| 0.4663 \| 0.3018 \| 0.4335 \| 0.4525 \| 0.2277 \| 0.3625 \| 0.6463 \| 0.5408 \| 0.6821 \| 0.2664 \| 0.4635 \| 0.1253 \| 0.2995 \| 0.1883 \| 0.4377 \| 0.2433 \| 0.3799 \| \| 0.9986 \| 40.0 \| 4280 \| 1.2900 \| 0.2921 \| 0.5961 \| 0.2449 \| 0.1617 \| 0.2199 \| 0.4851 \| 0.3028 \| 0.4506 \| 0.4659 \| 0.2422 \| 0.3836 \| 0.6408 \| 0.546 \| 0.696 \| 0.2649 \| 0.4538 \| 0.1729 \| 0.3111 \| 0.2041 \| 0.4574 \| 0.2725 \| 0.4113 \| \| 0.9951 \| 41.0 \| 4387 \| 1.3541 \| 0.2695 \| 0.5709 \| 0.2055 \| 0.1418 \| 0.1857 \| 0.4392 \| 0.2921 \| 0.4326 \| 0.4456 \| 0.2248 \| 0.3602 \| 0.602 \| 0.5355 \| 0.6855 \| 0.2612 \| 0.4365 \| 0.137 \| 0.2879 \| 0.1907 \| 0.4344 \| 0.2233 \| 0.3835 \| \| 0.9838 \| 42.0 \| 4494 \| 1.2978 \| 0.2868 \| 0.5888 \| 0.2326 \| 0.1262 \| 0.209 \| 0.5085 \| 0.3099 \| 0.4478 \| 0.463 \| 0.2469 \| 0.3642 \| 0.6518 \| 0.5266 \| 0.6867 \| 0.2904 \| 0.4692 \| 0.1673 \| 0.3291 \| 0.2 \| 0.4295 \| 0.2497 \| 0.4005 \| \| 0.9637 \| 43.0 \| 4601 \| 1.2950 \| 0.285 \| 0.5769 \| 0.2351 \| 0.1588 \| 0.2034 \| 0.4954 \| 0.3066 \| 0.4479 \| 0.463 \| 0.2504 \| 0.3566 \| 0.658 \| 0.5326 \| 0.689 \| 0.2793 \| 0.4615 \| 0.1678 \| 0.3171 \| 0.1861 \| 0.4262 \| 0.2593 \| 0.4211 \| \| 0.9408 \| 44.0 \| 4708 \| 1.3063 \| 0.2795 \| 0.5601 \| 0.2375 \| 0.1689 \| 0.1905 \| 0.5034 \| 0.3126 \| 0.4407 \| 0.4552 \| 0.2524 \| 0.3763 \| 0.6449 \| 0.5461 \| 0.6884 \| 0.2866 \| 0.4635 \| 0.1291 \| 0.2935 \| 0.1614 \| 0.4262 \| 0.2741 \| 0.4046 \| \| 0.9457 \| 45.0 \| 4815 \| 1.2866 \| 0.2851 \| 0.5794 \| 0.2471 \| 0.1712 \| 0.2011 \| 0.5017 \| 0.3168 \| 0.448 \| 0.4642 \| 0.2719 \| 0.3629 \| 0.6464 \| 0.544 \| 0.6896 \| 0.2772 \| 0.4692 \| 0.1691 \| 0.3176 \| 0.1561 \| 0.4328 \| 0.279 \| 0.4119 \| \| 0.9476 \| 46.0 \| 4922 \| 1.3047 \| 0.2737 \| 0.5779 \| 0.2219 \| 0.1539 \| 0.1932 \| 0.4666 \| 0.3059 \| 0.4356 \| 0.449 \| 0.2126 \| 0.3626 \| 0.6348 \| 0.5344 \| 0.6896 \| 0.2537 \| 0.4615 \| 0.1547 \| 0.3055 \| 0.1925 \| 0.4393 \| 0.233 \| 0.349 \| \| 0.9206 \| 47.0 \| 5029 \| 1.2955 \| 0.283 \| 0.579 \| 0.2365 \| 0.1437 \| 0.1993 \| 0.5024 \| 0.3048 \| 0.4388 \| 0.4501 \| 0.2412 \| 0.3643 \| 0.6283 \| 0.5501 \| 0.6913 \| 0.2723 \| 0.4096 \| 0.1598 \| 0.302 \| 0.1965 \| 0.4689 \| 0.2364 \| 0.3789 \| \| 0.9431 \| 48.0 \| 5136 \| 1.2809 \| 0.2955 \| 0.6039 \| 0.2482 \| 0.0914 \| 0.2308 \| 0.5222 \| 0.3096 \| 0.4442 \| 0.4548 \| 0.2012 \| 0.3786 \| 0.6271 \| 0.5476 \| 0.6855 \| 0.3006 \| 0.4154 \| 0.1713 \| 0.3246 \| 0.2015 \| 0.441 \| 0.2567 \| 0.4072 \| \| 0.8965 \| 49.0 \| 5243 \| 1.2968 \| 0.2935 \| 0.6015 \| 0.2566 \| 0.1579 \| 0.2276 \| 0.4831 \| 0.3112 \| 0.4451 \| 0.4623 \| 0.2279 \| 0.3816 \| 0.6389 \| 0.5336 \| 0.6844 \| 0.2907 \| 0.4558 \| 0.1725 \| 0.3317 \| 0.1938 \| 0.4344 \| 0.2771 \| 0.4052 \| \| 0.8636 \| 50.0 \| 5350 \| 1.2918 \| 0.2933 \| 0.611 \| 0.2363 \| 0.1728 \| 0.2087 \| 0.5035 \| 0.3236 \| 0.4496 \| 0.468 \| 0.2535 \| 0.3745 \| 0.6468 \| 0.5353 \| 0.6786 \| 0.2819 \| 0.4769 \| 0.1849 \| 0.3432 \| 0.2064 \| 0.4475 \| 0.2583 \| 0.3938 \| \| 0.8769 \| 51.0 \| 5457 \| 1.2744 \| 0.2959 \| 0.6161 \| 0.2408 \| 0.1599 \| 0.2291 \| 0.4925 \| 0.3212 \| 0.4461 \| 0.4567 \| 0.2249 \| 0.3782 \| 0.6405 \| 0.5475 \| 0.6942 \| 0.289 \| 0.4365 \| 0.1629 \| 0.3191 \| 0.2061 \| 0.4361 \| 0.2737 \| 0.3974 \| \| 0.8591 \| 52.0 \| 5564 \| 1.3174 \| 0.2949 \| 0.6031 \| 0.254 \| 0.1589 \| 0.2241 \| 0.5068 \| 0.3164 \| 0.4543 \| 0.4668 \| 0.2307 \| 0.3872 \| 0.6637 \| 0.5382 \| 0.6884 \| 0.2941 \| 0.4596 \| 0.1565 \| 0.3106 \| 0.2212 \| 0.4689 \| 0.2645 \| 0.4067 \| \| 0.8347 \| 53.0 \| 5671 \| 1.2626 \| 0.3071 \| 0.6126 \| 0.2618 \| 0.1107 \| 0.228 \| 0.5266 \| 0.3237 \| 0.4523 \| 0.4672 \| 0.2187 \| 0.3786 \| 0.6472 \| 0.5428 \| 0.6809 \| 0.3195 \| 0.4673 \| 0.1497 \| 0.3156 \| 0.2318 \| 0.4377 \| 0.2917 \| 0.4345 \| \| 0.8474 \| 54.0 \| 5778 \| 1.2730 \| 0.3094 \| 0.6174 \| 0.2591 \| 0.1729 \| 0.2387 \| 0.5153 \| 0.3268 \| 0.4591 \| 0.4714 \| 0.2482 \| 0.3975 \| 0.645 \| 0.5352 \| 0.6925 \| 0.3235 \| 0.4615 \| 0.1627 \| 0.3151 \| 0.2273 \| 0.459 \| 0.2983 \| 0.4289 \| \| 0.8326 \| 55.0 \| 5885 \| 1.3201 \| 0.2977 \| 0.6056 \| 0.259 \| 0.1635 \| 0.2296 \| 0.4971 \| 0.3159 \| 0.4505 \| 0.4619 \| 0.2297 \| 0.3789 \| 0.6458 \| 0.5208 \| 0.6769 \| 0.3094 \| 0.4462 \| 0.1661 \| 0.3075 \| 0.1961 \| 0.4508 \| 0.2963 \| 0.4284 \| \| 0.8263 \| 56.0 \| 5992 \| 1.2886 \| 0.3029 \| 0.6093 \| 0.2592 \| 0.1656 \| 0.2243 \| 0.5254 \| 0.3256 \| 0.4529 \| 0.471 \| 0.2464 \| 0.3875 \| 0.6623 \| 0.5289 \| 0.6838 \| 0.3448 \| 0.4808 \| 0.1479 \| 0.3095 \| 0.2026 \| 0.4574 \| 0.2906 \| 0.4237 \| \| 0.8222 \| 57.0 \| 6099 \| 1.2601 \| 0.3086 \| 0.6085 \| 0.2789 \| 0.1746 \| 0.2259 \| 0.5201 \| 0.3328 \| 0.4596 \| 0.4718 \| 0.2474 \| 0.3766 \| 0.6718 \| 0.5455 \| 0.6931 \| 0.3148 \| 0.4769 \| 0.1575 \| 0.3181 \| 0.2356 \| 0.4525 \| 0.2895 \| 0.4186 \| \| 0.8328 \| 58.0 \| 6206 \| 1.2603 \| 0.3137 \| 0.6183 \| 0.2922 \| 0.1647 \| 0.2253 \| 0.5418 \| 0.3307 \| 0.4605 \| 0.4712 \| 0.243 \| 0.3802 \| 0.6654 \| 0.5519 \| 0.6936 \| 0.3174 \| 0.4615 \| 0.1559 \| 0.3276 \| 0.2486 \| 0.4492 \| 0.2948 \| 0.4242 \| \| 0.8005 \| 59.0 \| 6313 \| 1.2536 \| 0.3103 \| 0.6141 \| 0.266 \| 0.173 \| 0.2226 \| 0.5386 \| 0.3322 \| 0.4597 \| 0.4703 \| 0.2512 \| 0.3798 \| 0.6546 \| 0.5517 \| 0.6867 \| 0.3166 \| 0.4673 \| 0.1487 \| 0.309 \| 0.2489 \| 0.4623 \| 0.2857 \| 0.4263 \| \| 0.7786 \| 60.0 \| 6420 \| 1.2642 \| 0.309 \| 0.6275 \| 0.2553 \| 0.1445 \| 0.2286 \| 0.5254 \| 0.3205 \| 0.4492 \| 0.4578 \| 0.223 \| 0.3713 \| 0.6501 \| 0.5394 \| 0.6786 \| 0.3146 \| 0.4346 \| 0.1573 \| 0.3126 \| 0.2571 \| 0.4623 \| 0.2764 \| 0.401 \| \| 0.8077 \| 61.0 \| 6527 \| 1.2750 \| 0.2959 \| 0.6106 \| 0.236 \| 0.1838 \| 0.2134 \| 0.5175 \| 0.3227 \| 0.4463 \| 0.4581 \| 0.2573 \| 0.3555 \| 0.662 \| 0.5316 \| 0.6884 \| 0.3034 \| 0.4308 \| 0.1394 \| 0.302 \| 0.2304 \| 0.459 \| 0.2748 \| 0.4103 \| \| 0.7867 \| 62.0 \| 6634 \| 1.2521 \| 0.3135 \| 0.6255 \| 0.2768 \| 0.1781 \| 0.2267 \| 0.5261 \| 0.3347 \| 0.4584 \| 0.4696 \| 0.2595 \| 0.3671 \| 0.6675 \| 0.5386 \| 0.6925 \| 0.3145 \| 0.4635 \| 0.1656 \| 0.3211 \| 0.258 \| 0.4639 \| 0.2908 \| 0.4072 \| \| 0.772 \| 63.0 \| 6741 \| 1.2546 \| 0.3072 \| 0.6313 \| 0.2524 \| 0.1802 \| 0.2297 \| 0.5233 \| 0.3295 \| 0.4587 \| 0.4696 \| 0.2715 \| 0.3667 \| 0.6625 \| 0.5381 \| 0.6913 \| 0.277 \| 0.4346 \| 0.1792 \| 0.3327 \| 0.2497 \| 0.4787 \| 0.2918 \| 0.4108 \| \| 0.7736 \| 64.0 \| 6848 \| 1.2673 \| 0.3021 \| 0.614 \| 0.244 \| 0.1813 \| 0.2327 \| 0.4996 \| 0.3305 \| 0.4561 \| 0.4622 \| 0.2509 \| 0.3738 \| 0.6488 \| 0.5308 \| 0.6803 \| 0.3057 \| 0.4423 \| 0.1597 \| 0.306 \| 0.2247 \| 0.459 \| 0.2895 \| 0.4232 \| \| 0.7561 \| 65.0 \| 6955 \| 1.3063 \| 0.2936 \| 0.6089 \| 0.2321 \| 0.1759 \| 0.2158 \| 0.5179 \| 0.3244 \| 0.4481 \| 0.4604 \| 0.2518 \| 0.3706 \| 0.6391 \| 0.519 \| 0.6769 \| 0.3054 \| 0.4558 \| 0.1472 \| 0.308 \| 0.2309 \| 0.459 \| 0.2653 \| 0.4021 \| \| 0.7316 \| 66.0 \| 7062 \| 1.2575 \| 0.315 \| 0.6168 \| 0.2804 \| 0.1995 \| 0.2251 \| 0.5303 \| 0.3363 \| 0.464 \| 0.4719 \| 0.2563 \| 0.3883 \| 0.6547 \| 0.5418 \| 0.6913 \| 0.3304 \| 0.4577 \| 0.1497 \| 0.3141 \| 0.2533 \| 0.4738 \| 0.3001 \| 0.4227 \| \| 0.746 \| 67.0 \| 7169 \| 1.2713 \| 0.3118 \| 0.6229 \| 0.2625 \| 0.1695 \| 0.2338 \| 0.5371 \| 0.3323 \| 0.4597 \| 0.4707 \| 0.2419 \| 0.3916 \| 0.6594 \| 0.5428 \| 0.6896 \| 0.3193 \| 0.4538 \| 0.1503 \| 0.3166 \| 0.2676 \| 0.4787 \| 0.2791 \| 0.4149 \| \| 0.7539 \| 68.0 \| 7276 \| 1.2705 \| 0.3161 \| 0.6353 \| 0.2591 \| 0.1718 \| 0.2332 \| 0.5451 \| 0.3343 \| 0.4568 \| 0.4702 \| 0.2554 \| 0.3826 \| 0.6569 \| 0.5358 \| 0.6803 \| 0.3318 \| 0.4577 \| 0.1494 \| 0.3146 \| 0.2715 \| 0.4918 \| 0.292 \| 0.4067 \| \| 0.7219 \| 69.0 \| 7383 \| 1.2644 \| 0.3175 \| 0.6321 \| 0.2758 \| 0.1924 \| 0.2341 \| 0.5379 \| 0.3404 \| 0.4653 \| 0.4744 \| 0.2457 \| 0.3925 \| 0.6648 \| 0.5442 \| 0.6988 \| 0.3151 \| 0.4712 \| 0.1662 \| 0.3176 \| 0.2727 \| 0.4672 \| 0.2895 \| 0.417 \| \| 0.7202 \| 70.0 \| 7490 \| 1.2718 \| 0.3124 \| 0.6233 \| 0.2622 \| 0.1758 \| 0.2246 \| 0.5305 \| 0.334 \| 0.4534 \| 0.4602 \| 0.2437 \| 0.3718 \| 0.6419 \| 0.547 \| 0.6954 \| 0.3204 \| 0.4327 \| 0.16 \| 0.3251 \| 0.2487 \| 0.4361 \| 0.2858 \| 0.4119 \| \| 0.7019 \| 71.0 \| 7597 \| 1.2819 \| 0.3073 \| 0.6104 \| 0.2737 \| 0.1777 \| 0.2295 \| 0.5232 \| 0.3287 \| 0.4558 \| 0.4679 \| 0.2492 \| 0.3812 \| 0.6526 \| 0.548 \| 0.6913 \| 0.3097 \| 0.4269 \| 0.1571 \| 0.3176 \| 0.2321 \| 0.4951 \| 0.2894 \| 0.4088 \| \| 0.6951 \| 72.0 \| 7704 \| 1.2853 \| 0.3099 \| 0.6174 \| 0.2613 \| 0.1872 \| 0.2375 \| 0.5177 \| 0.3358 \| 0.463 \| 0.4711 \| 0.2495 \| 0.3905 \| 0.6495 \| 0.5493 \| 0.6954 \| 0.3007 \| 0.4462 \| 0.1597 \| 0.3312 \| 0.2477 \| 0.4689 \| 0.2921 \| 0.4139 \| \| 0.6821 \| 73.0 \| 7811 \| 1.2949 \| 0.304 \| 0.6157 \| 0.2543 \| 0.1795 \| 0.2194 \| 0.5134 \| 0.3303 \| 0.4579 \| 0.4682 \| 0.2485 \| 0.3667 \| 0.6627 \| 0.5392 \| 0.6884 \| 0.3043 \| 0.4596 \| 0.1457 \| 0.3106 \| 0.2493 \| 0.4803 \| 0.2817 \| 0.4021 \| \| 0.6943 \| 74.0 \| 7918 \| 1.2790 \| 0.3101 \| 0.6194 \| 0.2567 \| 0.1863 \| 0.2366 \| 0.52 \| 0.3337 \| 0.4559 \| 0.4647 \| 0.2422 \| 0.3708 \| 0.6517 \| 0.5416 \| 0.6844 \| 0.3093 \| 0.4442 \| 0.1441 \| 0.3005 \| 0.2547 \| 0.4754 \| 0.3007 \| 0.4191 \| \| 0.6786 \| 75.0 \| 8025 \| 1.2823 \| 0.3121 \| 0.6285 \| 0.2576 \| 0.1497 \| 0.2309 \| 0.5135 \| 0.3348 \| 0.4575 \| 0.4686 \| 0.2583 \| 0.3764 \| 0.6485 \| 0.5449 \| 0.6838 \| 0.3174 \| 0.4635 \| 0.1517 \| 0.3111 \| 0.2466 \| 0.4639 \| 0.2997 \| 0.4206 \| \| 0.6606 \| 76.0 \| 8132 \| 1.2780 \| 0.3191 \| 0.628 \| 0.2754 \| 0.1958 \| 0.2363 \| 0.5414 \| 0.3399 \| 0.4597 \| 0.4708 \| 0.2592 \| 0.3744 \| 0.6633 \| 0.5547 \| 0.7 \| 0.316 \| 0.4442 \| 0.1575 \| 0.3111 \| 0.2601 \| 0.4672 \| 0.3071 \| 0.4314 \| \| 0.6585 \| 77.0 \| 8239 \| 1.2696 \| 0.3162 \| 0.6344 \| 0.2645 \| 0.1962 \| 0.2277 \| 0.5346 \| 0.3365 \| 0.4586 \| 0.4682 \| 0.2466 \| 0.3861 \| 0.6505 \| 0.544 \| 0.6931 \| 0.3221 \| 0.4462 \| 0.1604 \| 0.3231 \| 0.243 \| 0.4541 \| 0.3117 \| 0.4247 \| \| 0.6592 \| 78.0 \| 8346 \| 1.2684 \| 0.3196 \| 0.6245 \| 0.2718 \| 0.2043 \| 0.2306 \| 0.5384 \| 0.3429 \| 0.4657 \| 0.4755 \| 0.2704 \| 0.3934 \| 0.6572 \| 0.5458 \| 0.6902 \| 0.3302 \| 0.4577 \| 0.1748 \| 0.3377 \| 0.2455 \| 0.4672 \| 0.3014 \| 0.4247 \| \| 0.6585 \| 79.0 \| 8453 \| 1.2727 \| 0.3178 \| 0.6234 \| 0.2755 \| 0.2032 \| 0.2309 \| 0.5422 \| 0.337 \| 0.4627 \| 0.4718 \| 0.2685 \| 0.3828 \| 0.6604 \| 0.5462 \| 0.6879 \| 0.334 \| 0.4538 \| 0.1643 \| 0.3337 \| 0.2447 \| 0.4738 \| 0.2997 \| 0.4098 \| \| 0.6463 \| 80.0 \| 8560 \| 1.2868 \| 0.3123 \| 0.6186 \| 0.2752 \| 0.1921 \| 0.2364 \| 0.5315 \| 0.3377 \| 0.4535 \| 0.465 \| 0.2578 \| 0.3804 \| 0.6535 \| 0.5375 \| 0.6879 \| 0.333 \| 0.4519 \| 0.1566 \| 0.3201 \| 0.2372 \| 0.4525 \| 0.2973 \| 0.4129 \| \| 0.6458 \| 81.0 \| 8667 \| 1.2962 \| 0.3167 \| 0.6227 \| 0.277 \| 0.1767 \| 0.2339 \| 0.5406 \| 0.3376 \| 0.4549 \| 0.4652 \| 0.244 \| 0.3766 \| 0.6626 \| 0.5454 \| 0.6948 \| 0.3445 \| 0.4692 \| 0.1444 \| 0.3146 \| 0.2609 \| 0.441 \| 0.2885 \| 0.4062 \| \| 0.6207 \| 82.0 \| 8774 \| 1.2957 \| 0.3139 \| 0.6258 \| 0.2759 \| 0.202 \| 0.2299 \| 0.5281 \| 0.3389 \| 0.4568 \| 0.47 \| 0.2643 \| 0.3793 \| 0.6634 \| 0.5374 \| 0.6896 \| 0.3243 \| 0.4692 \| 0.151 \| 0.3246 \| 0.258 \| 0.4443 \| 0.2987 \| 0.4222 \| \| 0.6356 \| 83.0 \| 8881 \| 1.3102 \| 0.31 \| 0.618 \| 0.2765 \| 0.1833 \| 0.229 \| 0.5134 \| 0.3317 \| 0.4583 \| 0.4686 \| 0.2588 \| 0.394 \| 0.642 \| 0.5361 \| 0.689 \| 0.3412 \| 0.4769 \| 0.1453 \| 0.3241 \| 0.241 \| 0.4443 \| 0.2865 \| 0.4088 \| \| 0.6286 \| 84.0 \| 8988 \| 1.2985 \| 0.3066 \| 0.6197 \| 0.2678 \| 0.1879 \| 0.2187 \| 0.524 \| 0.3336 \| 0.4478 \| 0.4549 \| 0.2533 \| 0.3669 \| 0.6431 \| 0.5354 \| 0.6844 \| 0.3222 \| 0.4423 \| 0.1468 \| 0.306 \| 0.2344 \| 0.4344 \| 0.294 \| 0.4072 \| \| 0.6269 \| 85.0 \| 9095 \| 1.2769 \| 0.3174 \| 0.6259 \| 0.2708 \| 0.2034 \| 0.2266 \| 0.5397 \| 0.3425 \| 0.4593 \| 0.4669 \| 0.2736 \| 0.3741 \| 0.6577 \| 0.5429 \| 0.6919 \| 0.3334 \| 0.4596 \| 0.1592 \| 0.3211 \| 0.2454 \| 0.441 \| 0.3061 \| 0.4211 \| \| 0.5998 \| 86.0 \| 9202 \| 1.2841 \| 0.317 \| 0.616 \| 0.2823 \| 0.1881 \| 0.2347 \| 0.538 \| 0.3419 \| 0.4606 \| 0.4711 \| 0.2701 \| 0.3843 \| 0.6488 \| 0.5414 \| 0.6896 \| 0.3243 \| 0.4692 \| 0.1586 \| 0.3256 \| 0.2489 \| 0.4443 \| 0.3116 \| 0.4268 \| \| 0.6054 \| 87.0 \| 9309 \| 1.2951 \| 0.3163 \| 0.6223 \| 0.2731 \| 0.1937 \| 0.2287 \| 0.541 \| 0.3368 \| 0.4578 \| 0.4648 \| 0.2732 \| 0.3682 \| 0.6534 \| 0.5436 \| 0.6832 \| 0.3263 \| 0.4577 \| 0.1617 \| 0.3196 \| 0.251 \| 0.4377 \| 0.2988 \| 0.4258 \| \| 0.6138 \| 88.0 \| 9416 \| 1.2934 \| 0.3144 \| 0.6109 \| 0.2739 \| 0.1932 \| 0.2315 \| 0.5285 \| 0.3451 \| 0.4584 \| 0.4683 \| 0.264 \| 0.3767 \| 0.6479 \| 0.5384 \| 0.6844 \| 0.3286 \| 0.4712 \| 0.1513 \| 0.3131 \| 0.2518 \| 0.4492 \| 0.3019 \| 0.4237 \| \| 0.5887 \| 89.0 \| 9523 \| 1.2940 \| 0.3171 \| 0.6147 \| 0.279 \| 0.2023 \| 0.2306 \| 0.5411 \| 0.3377 \| 0.4613 \| 0.4698 \| 0.2773 \| 0.3725 \| 0.6505 \| 0.5397 \| 0.689 \| 0.336 \| 0.4673 \| 0.161 \| 0.3161 \| 0.248 \| 0.4541 \| 0.3008 \| 0.4227 \| \| 0.6026 \| 90.0 \| 9630 \| 1.3081 \| 0.3106 \| 0.6154 \| 0.2657 \| 0.2063 \| 0.2143 \| 0.5398 \| 0.3342 \| 0.4543 \| 0.4616 \| 0.2846 \| 0.3601 \| 0.6397 \| 0.5287 \| 0.6769 \| 0.3076 \| 0.4442 \| 0.1675 \| 0.3146 \| 0.2498 \| 0.4426 \| 0.2996 \| 0.4299 \| \| 0.6116 \| 91.0 \| 9737 \| 1.3193 \| 0.3046 \| 0.5974 \| 0.2664 \| 0.215 \| 0.2096 \| 0.5344 \| 0.3349 \| 0.4514 \| 0.4594 \| 0.2806 \| 0.3671 \| 0.6362 \| 0.5371 \| 0.6844 \| 0.2908 \| 0.4327 \| 0.1537 \| 0.3121 \| 0.2481 \| 0.4475 \| 0.2931 \| 0.4201 \| \| 0.5874 \| 92.0 \| 9844 \| 1.3148 \| 0.307 \| 0.5997 \| 0.2706 \| 0.207 \| 0.2168 \| 0.5303 \| 0.3364 \| 0.457 \| 0.4653 \| 0.2759 \| 0.38 \| 0.6418 \| 0.5305 \| 0.6827 \| 0.3038 \| 0.4519 \| 0.1554 \| 0.3176 \| 0.2488 \| 0.4541 \| 0.2965 \| 0.4201 \| \| 0.5765 \| 93.0 \| 9951 \| 1.3114 \| 0.31 \| 0.6063 \| 0.2757 \| 0.2056 \| 0.2172 \| 0.5388 \| 0.3349 \| 0.4549 \| 0.4625 \| 0.275 \| 0.369 \| 0.6406 \| 0.5308 \| 0.6827 \| 0.3106 \| 0.4538 \| 0.1567 \| 0.309 \| 0.2532 \| 0.4459 \| 0.2988 \| 0.4211 \| \| 0.5799 \| 94.0 \| 10058 \| 1.3074 \| 0.3101 \| 0.6134 \| 0.2681 \| 0.2042 \| 0.2135 \| 0.5519 \| 0.3365 \| 0.454 \| 0.4634 \| 0.272 \| 0.3674 \| 0.6508 \| 0.53 \| 0.6821 \| 0.3105 \| 0.4596 \| 0.1525 \| 0.3161 \| 0.2582 \| 0.4377 \| 0.2993 \| 0.4216 \| \| 0.5678 \| 95.0 \| 10165 \| 1.3073 \| 0.3102 \| 0.6184 \| 0.2637 \| 0.2103 \| 0.2233 \| 0.537 \| 0.3327 \| 0.4527 \| 0.461 \| 0.2775 \| 0.3668 \| 0.6309 \| 0.537 \| 0.6884 \| 0.3217 \| 0.4404 \| 0.1558 \| 0.3106 \| 0.2354 \| 0.4459 \| 0.3011 \| 0.4196 \| \| 0.5776 \| 96.0 \| 10272 \| 1.3083 \| 0.3146 \| 0.6177 \| 0.2751 \| 0.2083 \| 0.2193 \| 0.5465 \| 0.3364 \| 0.4548 \| 0.4627 \| 0.2731 \| 0.3681 \| 0.6399 \| 0.5359 \| 0.6861 \| 0.3306 \| 0.4538 \| 0.1566 \| 0.3176 \| 0.2484 \| 0.4361 \| 0.3012 \| 0.4201 \| \| 0.5768 \| 97.0 \| 10379 \| 1.3027 \| 0.3134 \| 0.6204 \| 0.2681 \| 0.2056 \| 0.2203 \| 0.5453 \| 0.3365 \| 0.4545 \| 0.4622 \| 0.2774 \| 0.3652 \| 0.6417 \| 0.5329 \| 0.6832 \| 0.3282 \| 0.4538 \| 0.1591 \| 0.3181 \| 0.2445 \| 0.4328 \| 0.3021 \| 0.4232 \| \| 0.5708 \| 98.0 \| 10486 \| 1.3045 \| 0.3094 \| 0.6223 \| 0.2705 \| 0.2077 \| 0.2196 \| 0.5411 \| 0.3325 \| 0.4544 \| 0.4623 \| 0.2754 \| 0.3668 \| 0.641 \| 0.5342 \| 0.6838 \| 0.3201 \| 0.4519 \| 0.1588 \| 0.3181 \| 0.2347 \| 0.4361 \| 0.299 \| 0.4216 \| \| 0.5779 \| 99.0 \| 10593 \| 1.3050 \| 0.3104 \| 0.6231 \| 0.2616 \| 0.2049 \| 0.2159 \| 0.5424 \| 0.3324 \| 0.455 \| 0.4645 \| 0.274 \| 0.3682 \| 0.6457 \| 0.536 \| 0.6838 \| 0.3162 \| 0.4615 \| 0.1593 \| 0.3176 \| 0.2376 \| 0.4377 \| 0.303 \| 0.4216 \| \| 0.5571 \| 100.0 \| 10700 \| 1.3007 \| 0.3131 \| 0.6268 \| 0.2698 \| 0.2089 \| 0.2237 \| 0.5358 \| 0.3352 \| 0.4586 \| 0.4682 \| 0.2773 \| 0.3748 \| 0.6417 \| 0.5362 \| 0.6844 \| 0.3199 \| 0.4635 \| 0.1628 \| 0.3176 \| 0.2427 \| 0.4475 \| 0.3038 \| 0.4278 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sensetime-deformable-detr-finetuned-10k-cppe5 This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.1028 - Map: 0.2808 - Map 50: 0.5257 - Map 75: 0.2589 - Map Small: 0.2879 - Map Medium: 0.1953 - Map Large: 0.4546 - Mar 1: 0.3004 - Mar 10: 0.5007 - Mar 100: 0.534 - Mar Small: 0.4339 - Mar Medium: 0.4079 - Mar Large: 0.7629 - Map Coverall: 0.5566 - Mar 100 Coverall: 0.7331 - Map Face Shield: 0.2761 - Mar 100 Face Shield: 0.549 - Map Gloves: 0.1484 - Mar 100 Gloves: 0.3943 - Map Goggles: 0.1271 - Mar 100 Goggles: 0.5085 - Map Mask: 0.2956 - Mar 100 Mask: 0.4853 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 9.1039 \| 0.9953 \| 106 \| 2.6450 \| 0.0073 \| 0.0229 \| 0.0025 \| 0.0 \| 0.0001 \| 0.0088 \| 0.0123 \| 0.0311 \| 0.0381 \| 0.0009 \| 0.0248 \| 0.0329 \| 0.0358 \| 0.1186 \| 0.0001 \| 0.0204 \| 0.0 \| 0.0066 \| 0.0002 \| 0.0034 \| 0.0002 \| 0.0417 \| \| 2.3921 \| 2.0 \| 213 \| 2.4742 \| 0.0067 \| 0.0207 \| 0.0017 \| 0.0009 \| 0.0009 \| 0.008 \| 0.0157 \| 0.0582 \| 0.0923 \| 0.0038 \| 0.0276 \| 0.1516 \| 0.0296 \| 0.3349 \| 0.0011 \| 0.0327 \| 0.0 \| 0.0199 \| 0.0016 \| 0.0203 \| 0.0013 \| 0.0536 \| \| 2.28 \| 2.9953 \| 319 \| 2.5260 \| 0.0064 \| 0.0198 \| 0.0024 \| 0.0342 \| 0.0005 \| 0.0063 \| 0.0124 \| 0.0547 \| 0.0777 \| 0.1079 \| 0.0291 \| 0.0848 \| 0.0294 \| 0.2849 \| 0.0009 \| 0.0082 \| 0.0 \| 0.0057 \| 0.0 \| 0.0 \| 0.0019 \| 0.0896 \| \| 2.1883 \| 4.0 \| 426 \| 2.2302 \| 0.0077 \| 0.0193 \| 0.006 \| 0.0013 \| 0.001 \| 0.0126 \| 0.0259 \| 0.0653 \| 0.1098 \| 0.0109 \| 0.035 \| 0.1264 \| 0.0323 \| 0.4203 \| 0.0 \| 0.0 \| 0.0 \| 0.01 \| 0.0023 \| 0.0119 \| 0.0036 \| 0.1066 \| \| 2.1263 \| 4.9953 \| 532 \| 2.1512 \| 0.0105 \| 0.0306 \| 0.0069 \| 0.0021 \| 0.0015 \| 0.0105 \| 0.0323 \| 0.0757 \| 0.1144 \| 0.0199 \| 0.0328 \| 0.1168 \| 0.0467 \| 0.45 \| 0.0001 \| 0.002 \| 0.0001 \| 0.0175 \| 0.0 \| 0.0 \| 0.0058 \| 0.1024 \| \| 2.0232 \| 6.0 \| 639 \| 2.1045 \| 0.0133 \| 0.0323 \| 0.0103 \| 0.0049 \| 0.0023 \| 0.0143 \| 0.0357 \| 0.0882 \| 0.1265 \| 0.0147 \| 0.044 \| 0.1458 \| 0.0575 \| 0.4773 \| 0.0 \| 0.0 \| 0.0002 \| 0.0147 \| 0.0 \| 0.0 \| 0.0086 \| 0.1403 \| \| 2.0103 \| 6.9953 \| 745 \| 2.1646 \| 0.0219 \| 0.0466 \| 0.0178 \| 0.0049 \| 0.0015 \| 0.0232 \| 0.0438 \| 0.1018 \| 0.1274 \| 0.0085 \| 0.0471 \| 0.1658 \| 0.1033 \| 0.4698 \| 0.0013 \| 0.0184 \| 0.0001 \| 0.0251 \| 0.0003 \| 0.0136 \| 0.0046 \| 0.1104 \| \| 1.9723 \| 8.0 \| 852 \| 1.9693 \| 0.021 \| 0.052 \| 0.0165 \| 0.0048 \| 0.0036 \| 0.0274 \| 0.039 \| 0.113 \| 0.1558 \| 0.0241 \| 0.053 \| 0.2211 \| 0.0881 \| 0.5407 \| 0.0029 \| 0.0286 \| 0.0001 \| 0.0265 \| 0.0007 \| 0.0119 \| 0.013 \| 0.1716 \| \| 1.9425 \| 8.9953 \| 958 \| 1.9505 \| 0.0199 \| 0.0505 \| 0.0148 \| 0.003 \| 0.0051 \| 0.0195 \| 0.0387 \| 0.1041 \| 0.1534 \| 0.0233 \| 0.0595 \| 0.1477 \| 0.0826 \| 0.564 \| 0.0037 \| 0.0122 \| 0.0001 \| 0.0156 \| 0.0003 \| 0.0068 \| 0.0126 \| 0.1682 \| \| 1.8248 \| 10.0 \| 1065 \| 1.9128 \| 0.0237 \| 0.0603 \| 0.0166 \| 0.0064 \| 0.0079 \| 0.0335 \| 0.0558 \| 0.137 \| 0.1739 \| 0.0569 \| 0.089 \| 0.1711 \| 0.0872 \| 0.5314 \| 0.015 \| 0.0939 \| 0.0002 \| 0.019 \| 0.0018 \| 0.0492 \| 0.0143 \| 0.1763 \| \| 1.7923 \| 10.9953 \| 1171 \| 1.8334 \| 0.0281 \| 0.0628 \| 0.0223 \| 0.0069 \| 0.0081 \| 0.0302 \| 0.0546 \| 0.1543 \| 0.204 \| 0.0577 \| 0.1129 \| 0.2174 \| 0.1088 \| 0.5523 \| 0.0103 \| 0.0857 \| 0.0002 \| 0.0265 \| 0.0048 \| 0.1424 \| 0.0163 \| 0.2133 \| \| 1.7922 \| 12.0 \| 1278 \| 1.8679 \| 0.0405 \| 0.0964 \| 0.0281 \| 0.0165 \| 0.0126 \| 0.0444 \| 0.0596 \| 0.1629 \| 0.2031 \| 0.0387 \| 0.1127 \| 0.2102 \| 0.1462 \| 0.5709 \| 0.0299 \| 0.1408 \| 0.0002 \| 0.0213 \| 0.0099 \| 0.0966 \| 0.0163 \| 0.1858 \| \| 1.7797 \| 12.9953 \| 1384 \| 1.7715 \| 0.036 \| 0.0739 \| 0.0337 \| 0.0066 \| 0.0089 \| 0.042 \| 0.0647 \| 0.1644 \| 0.2102 \| 0.0472 \| 0.1088 \| 0.2326 \| 0.1429 \| 0.5924 \| 0.014 \| 0.1082 \| 0.0006 \| 0.0469 \| 0.0016 \| 0.0797 \| 0.0207 \| 0.2237 \| \| 1.6925 \| 14.0 \| 1491 \| 1.7633 \| 0.0427 \| 0.0897 \| 0.044 \| 0.0073 \| 0.0069 \| 0.0564 \| 0.061 \| 0.163 \| 0.2152 \| 0.0631 \| 0.1133 \| 0.2339 \| 0.1819 \| 0.5988 \| 0.0063 \| 0.0429 \| 0.0007 \| 0.0493 \| 0.0069 \| 0.1695 \| 0.0175 \| 0.2156 \| \| 1.6927 \| 14.9953 \| 1597 \| 1.7455 \| 0.0429 \| 0.0973 \| 0.0311 \| 0.0081 \| 0.0139 \| 0.0464 \| 0.0718 \| 0.196 \| 0.243 \| 0.059 \| 0.1603 \| 0.2494 \| 0.1594 \| 0.5959 \| 0.0283 \| 0.1592 \| 0.0008 \| 0.0559 \| 0.0069 \| 0.1627 \| 0.0191 \| 0.2412 \| \| 1.6409 \| 16.0 \| 1704 \| 1.6922 \| 0.0414 \| 0.1001 \| 0.0295 \| 0.0186 \| 0.0152 \| 0.0447 \| 0.0612 \| 0.203 \| 0.2566 \| 0.0805 \| 0.1605 \| 0.3192 \| 0.1449 \| 0.5965 \| 0.0235 \| 0.151 \| 0.0012 \| 0.073 \| 0.0108 \| 0.2254 \| 0.0264 \| 0.237 \| \| 1.6535 \| 16.9953 \| 1810 \| 1.6837 \| 0.0508 \| 0.0973 \| 0.0468 \| 0.011 \| 0.021 \| 0.0457 \| 0.088 \| 0.2063 \| 0.2565 \| 0.0518 \| 0.1584 \| 0.2943 \| 0.1717 \| 0.6256 \| 0.0426 \| 0.1694 \| 0.0007 \| 0.0507 \| 0.0081 \| 0.1712 \| 0.0307 \| 0.2654 \| \| 1.5853 \| 18.0 \| 1917 \| 1.6565 \| 0.0424 \| 0.0842 \| 0.0379 \| 0.0103 \| 0.0177 \| 0.0497 \| 0.0954 \| 0.2317 \| 0.2809 \| 0.0502 \| 0.1839 \| 0.3685 \| 0.1457 \| 0.639 \| 0.0169 \| 0.1673 \| 0.002 \| 0.0986 \| 0.0155 \| 0.2339 \| 0.0318 \| 0.2659 \| \| 1.5743 \| 18.9953 \| 2023 \| 1.6498 \| 0.0409 \| 0.0877 \| 0.0367 \| 0.0989 \| 0.0149 \| 0.0487 \| 0.1 \| 0.2277 \| 0.2841 \| 0.2421 \| 0.1812 \| 0.3256 \| 0.1417 \| 0.6535 \| 0.0225 \| 0.2286 \| 0.0025 \| 0.1052 \| 0.0073 \| 0.1407 \| 0.0306 \| 0.2924 \| \| 1.5241 \| 20.0 \| 2130 \| 1.6974 \| 0.0496 \| 0.1014 \| 0.0484 \| 0.0401 \| 0.0168 \| 0.0579 \| 0.099 \| 0.2179 \| 0.2668 \| 0.0939 \| 0.1701 \| 0.3058 \| 0.179 \| 0.632 \| 0.0278 \| 0.2531 \| 0.003 \| 0.0825 \| 0.0096 \| 0.1525 \| 0.0283 \| 0.2137 \| \| 1.5431 \| 20.9953 \| 2236 \| 1.5908 \| 0.0584 \| 0.1168 \| 0.0529 \| 0.098 \| 0.0376 \| 0.0581 \| 0.1182 \| 0.2721 \| 0.3191 \| 0.15 \| 0.2129 \| 0.3986 \| 0.1837 \| 0.6558 \| 0.0505 \| 0.3224 \| 0.0039 \| 0.1005 \| 0.0151 \| 0.2508 \| 0.0388 \| 0.2659 \| \| 1.5106 \| 22.0 \| 2343 \| 1.6095 \| 0.0593 \| 0.1366 \| 0.0445 \| 0.0125 \| 0.0284 \| 0.0639 \| 0.1034 \| 0.2407 \| 0.2845 \| 0.063 \| 0.1819 \| 0.3299 \| 0.1892 \| 0.6238 \| 0.0521 \| 0.2327 \| 0.006 \| 0.1118 \| 0.0147 \| 0.1864 \| 0.0345 \| 0.2678 \| \| 1.5372 \| 22.9953 \| 2449 \| 1.5951 \| 0.0636 \| 0.1316 \| 0.0576 \| 0.0282 \| 0.0416 \| 0.0537 \| 0.1163 \| 0.266 \| 0.312 \| 0.0853 \| 0.2239 \| 0.3533 \| 0.1781 \| 0.6052 \| 0.0692 \| 0.3286 \| 0.008 \| 0.1393 \| 0.0153 \| 0.1983 \| 0.0475 \| 0.2886 \| \| 1.4386 \| 24.0 \| 2556 \| 1.5187 \| 0.0622 \| 0.123 \| 0.0587 \| 0.0185 \| 0.0405 \| 0.0661 \| 0.1229 \| 0.2786 \| 0.3385 \| 0.0898 \| 0.2326 \| 0.4092 \| 0.1881 \| 0.6599 \| 0.0659 \| 0.351 \| 0.0061 \| 0.1332 \| 0.0147 \| 0.2407 \| 0.0362 \| 0.3076 \| \| 1.4267 \| 24.9953 \| 2662 \| 1.5143 \| 0.0664 \| 0.1359 \| 0.0569 \| 0.0405 \| 0.0475 \| 0.0664 \| 0.1284 \| 0.2935 \| 0.3396 \| 0.0994 \| 0.2523 \| 0.3919 \| 0.1845 \| 0.6512 \| 0.0576 \| 0.3204 \| 0.0088 \| 0.1393 \| 0.0233 \| 0.2508 \| 0.0576 \| 0.3365 \| \| 1.397 \| 26.0 \| 2769 \| 1.5084 \| 0.0689 \| 0.1464 \| 0.0617 \| 0.0567 \| 0.047 \| 0.0686 \| 0.1508 \| 0.3158 \| 0.363 \| 0.1653 \| 0.2526 \| 0.453 \| 0.1731 \| 0.6605 \| 0.0708 \| 0.3796 \| 0.009 \| 0.1569 \| 0.0347 \| 0.2932 \| 0.057 \| 0.3246 \| \| 1.3846 \| 26.9953 \| 2875 \| 1.4379 \| 0.077 \| 0.1554 \| 0.0671 \| 0.052 \| 0.0457 \| 0.0747 \| 0.1576 \| 0.3191 \| 0.3686 \| 0.2459 \| 0.2615 \| 0.4191 \| 0.2061 \| 0.6762 \| 0.0678 \| 0.3939 \| 0.0154 \| 0.19 \| 0.027 \| 0.2136 \| 0.069 \| 0.3692 \| \| 1.3763 \| 28.0 \| 2982 \| 1.4605 \| 0.0727 \| 0.1483 \| 0.0675 \| 0.0761 \| 0.037 \| 0.0817 \| 0.1428 \| 0.3136 \| 0.3706 \| 0.1434 \| 0.2537 \| 0.4855 \| 0.2099 \| 0.6663 \| 0.0653 \| 0.398 \| 0.0116 \| 0.1801 \| 0.0247 \| 0.2678 \| 0.052 \| 0.3408 \| \| 1.3449 \| 28.9953 \| 3088 \| 1.4449 \| 0.0808 \| 0.1637 \| 0.0757 \| 0.1455 \| 0.0567 \| 0.0952 \| 0.1746 \| 0.3383 \| 0.3933 \| 0.3418 \| 0.2831 \| 0.5146 \| 0.2091 \| 0.6779 \| 0.0717 \| 0.4449 \| 0.0119 \| 0.1635 \| 0.032 \| 0.3085 \| 0.0794 \| 0.3716 \| \| 1.3355 \| 30.0 \| 3195 \| 1.4498 \| 0.0727 \| 0.1544 \| 0.0677 \| 0.0271 \| 0.0509 \| 0.0832 \| 0.1562 \| 0.3189 \| 0.3589 \| 0.2176 \| 0.2815 \| 0.4364 \| 0.2056 \| 0.6715 \| 0.0554 \| 0.3245 \| 0.0152 \| 0.1773 \| 0.0149 \| 0.2458 \| 0.0723 \| 0.3754 \| \| 1.3218 \| 30.9953 \| 3301 \| 1.4156 \| 0.0784 \| 0.1641 \| 0.0656 \| 0.0608 \| 0.0528 \| 0.0978 \| 0.1575 \| 0.3344 \| 0.3867 \| 0.1655 \| 0.2825 \| 0.5141 \| 0.2088 \| 0.686 \| 0.0624 \| 0.398 \| 0.0211 \| 0.21 \| 0.0238 \| 0.2661 \| 0.0761 \| 0.3735 \| \| 1.2773 \| 32.0 \| 3408 \| 1.3722 \| 0.0823 \| 0.1682 \| 0.0764 \| 0.0641 \| 0.056 \| 0.1128 \| 0.1602 \| 0.3656 \| 0.4143 \| 0.1975 \| 0.308 \| 0.5599 \| 0.2192 \| 0.6913 \| 0.0704 \| 0.449 \| 0.0199 \| 0.2213 \| 0.0216 \| 0.3153 \| 0.0803 \| 0.3948 \| \| 1.2839 \| 32.9953 \| 3514 \| 1.3795 \| 0.09 \| 0.186 \| 0.0785 \| 0.0733 \| 0.0697 \| 0.14 \| 0.1867 \| 0.3742 \| 0.416 \| 0.2753 \| 0.2984 \| 0.5739 \| 0.2294 \| 0.693 \| 0.0819 \| 0.4673 \| 0.0212 \| 0.2218 \| 0.0293 \| 0.3119 \| 0.0883 \| 0.3858 \| \| 1.2384 \| 34.0 \| 3621 \| 1.3960 \| 0.1002 \| 0.2061 \| 0.0858 \| 0.0783 \| 0.0594 \| 0.1223 \| 0.175 \| 0.36 \| 0.4115 \| 0.2457 \| 0.2905 \| 0.5741 \| 0.2479 \| 0.6895 \| 0.0775 \| 0.402 \| 0.0246 \| 0.2479 \| 0.0371 \| 0.3136 \| 0.114 \| 0.4047 \| \| 1.2495 \| 34.9953 \| 3727 \| 1.3261 \| 0.1007 \| 0.2102 \| 0.087 \| 0.0847 \| 0.0659 \| 0.1473 \| 0.1918 \| 0.3789 \| 0.4263 \| 0.2146 \| 0.3201 \| 0.5737 \| 0.246 \| 0.7035 \| 0.0753 \| 0.4204 \| 0.0362 \| 0.2588 \| 0.0315 \| 0.3593 \| 0.1145 \| 0.3896 \| \| 1.2032 \| 36.0 \| 3834 \| 1.3012 \| 0.1024 \| 0.2052 \| 0.0886 \| 0.105 \| 0.0723 \| 0.157 \| 0.1899 \| 0.394 \| 0.4376 \| 0.2237 \| 0.3284 \| 0.5945 \| 0.2543 \| 0.6907 \| 0.0731 \| 0.4673 \| 0.0369 \| 0.272 \| 0.0294 \| 0.3542 \| 0.1184 \| 0.4038 \| \| 1.2024 \| 36.9953 \| 3940 \| 1.2989 \| 0.107 \| 0.2271 \| 0.0844 \| 0.2679 \| 0.0987 \| 0.1728 \| 0.1961 \| 0.4048 \| 0.4467 \| 0.3934 \| 0.348 \| 0.5898 \| 0.2451 \| 0.7035 \| 0.0819 \| 0.449 \| 0.0419 \| 0.2773 \| 0.0373 \| 0.3814 \| 0.1288 \| 0.4223 \| \| 1.1714 \| 38.0 \| 4047 \| 1.3187 \| 0.1186 \| 0.2361 \| 0.1052 \| 0.0464 \| 0.0813 \| 0.16 \| 0.1932 \| 0.3883 \| 0.4304 \| 0.2583 \| 0.321 \| 0.6007 \| 0.3029 \| 0.7058 \| 0.0951 \| 0.4143 \| 0.0472 \| 0.2739 \| 0.0372 \| 0.3373 \| 0.1108 \| 0.4209 \| \| 1.1702 \| 38.9953 \| 4153 \| 1.3017 \| 0.1158 \| 0.2338 \| 0.1025 \| 0.1431 \| 0.0923 \| 0.1729 \| 0.1998 \| 0.4178 \| 0.4544 \| 0.2428 \| 0.3371 \| 0.6742 \| 0.2624 \| 0.7134 \| 0.0981 \| 0.4694 \| 0.0458 \| 0.2981 \| 0.0427 \| 0.3559 \| 0.1299 \| 0.4351 \| \| 1.1745 \| 40.0 \| 4260 \| 1.2802 \| 0.1167 \| 0.2351 \| 0.1035 \| 0.0578 \| 0.095 \| 0.1591 \| 0.2099 \| 0.4126 \| 0.4571 \| 0.3426 \| 0.3457 \| 0.6024 \| 0.2627 \| 0.7174 \| 0.1144 \| 0.4939 \| 0.0403 \| 0.29 \| 0.0334 \| 0.3407 \| 0.1328 \| 0.4436 \| \| 1.1502 \| 40.9953 \| 4366 \| 1.2797 \| 0.1283 \| 0.2538 \| 0.1168 \| 0.082 \| 0.0831 \| 0.1653 \| 0.2048 \| 0.4136 \| 0.4552 \| 0.322 \| 0.3349 \| 0.6206 \| 0.301 \| 0.7169 \| 0.1078 \| 0.449 \| 0.0513 \| 0.3085 \| 0.043 \| 0.3627 \| 0.1382 \| 0.4389 \| \| 1.1149 \| 42.0 \| 4473 \| 1.2880 \| 0.134 \| 0.2744 \| 0.1197 \| 0.1218 \| 0.1095 \| 0.2147 \| 0.2133 \| 0.4295 \| 0.4717 \| 0.2168 \| 0.3639 \| 0.7173 \| 0.3046 \| 0.7238 \| 0.1305 \| 0.5286 \| 0.0479 \| 0.3005 \| 0.0425 \| 0.3746 \| 0.1445 \| 0.4313 \| \| 1.1224 \| 42.9953 \| 4579 \| 1.2321 \| 0.1477 \| 0.2839 \| 0.1314 \| 0.1361 \| 0.1143 \| 0.2275 \| 0.22 \| 0.4406 \| 0.4751 \| 0.3523 \| 0.3623 \| 0.6821 \| 0.3288 \| 0.7326 \| 0.1463 \| 0.4918 \| 0.0582 \| 0.3242 \| 0.0434 \| 0.3915 \| 0.1616 \| 0.4355 \| \| 1.0958 \| 44.0 \| 4686 \| 1.2293 \| 0.1416 \| 0.2833 \| 0.1279 \| 0.1761 \| 0.1067 \| 0.2186 \| 0.2211 \| 0.4368 \| 0.4815 \| 0.3122 \| 0.3702 \| 0.6796 \| 0.3046 \| 0.732 \| 0.1434 \| 0.4959 \| 0.054 \| 0.3171 \| 0.0355 \| 0.3915 \| 0.1702 \| 0.4711 \| \| 1.081 \| 44.9953 \| 4792 \| 1.2400 \| 0.1403 \| 0.2807 \| 0.1243 \| 0.1979 \| 0.1054 \| 0.24 \| 0.2378 \| 0.4331 \| 0.4761 \| 0.3035 \| 0.3484 \| 0.7158 \| 0.297 \| 0.7291 \| 0.1405 \| 0.4959 \| 0.0624 \| 0.328 \| 0.0345 \| 0.3814 \| 0.167 \| 0.446 \| \| 1.0659 \| 46.0 \| 4899 \| 1.2180 \| 0.1518 \| 0.2984 \| 0.1404 \| 0.0926 \| 0.1198 \| 0.2352 \| 0.2261 \| 0.4455 \| 0.4907 \| 0.3604 \| 0.3798 \| 0.6992 \| 0.3282 \| 0.7262 \| 0.1433 \| 0.5102 \| 0.0668 \| 0.3502 \| 0.0452 \| 0.4085 \| 0.1757 \| 0.4583 \| \| 1.0741 \| 46.9953 \| 5005 \| 1.2355 \| 0.1501 \| 0.3066 \| 0.1381 \| 0.2278 \| 0.1125 \| 0.2314 \| 0.2259 \| 0.4356 \| 0.4874 \| 0.3453 \| 0.3694 \| 0.6911 \| 0.3114 \| 0.7134 \| 0.1447 \| 0.5367 \| 0.0688 \| 0.336 \| 0.0354 \| 0.3763 \| 0.1904 \| 0.4744 \| \| 1.074 \| 48.0 \| 5112 \| 1.2082 \| 0.1495 \| 0.2917 \| 0.1343 \| 0.0927 \| 0.1293 \| 0.2343 \| 0.2247 \| 0.4535 \| 0.4956 \| 0.346 \| 0.396 \| 0.712 \| 0.3327 \| 0.732 \| 0.1477 \| 0.5122 \| 0.056 \| 0.3498 \| 0.0308 \| 0.4237 \| 0.1805 \| 0.4602 \| \| 1.067 \| 48.9953 \| 5218 \| 1.2123 \| 0.165 \| 0.3104 \| 0.1467 \| 0.0957 \| 0.1188 \| 0.2524 \| 0.2522 \| 0.4578 \| 0.5 \| 0.3661 \| 0.3737 \| 0.7116 \| 0.3553 \| 0.7291 \| 0.1624 \| 0.5286 \| 0.0706 \| 0.3417 \| 0.0463 \| 0.422 \| 0.1902 \| 0.4787 \| \| 1.0404 \| 50.0 \| 5325 \| 1.2088 \| 0.1569 \| 0.3099 \| 0.1328 \| 0.1295 \| 0.1105 \| 0.2661 \| 0.235 \| 0.4425 \| 0.4879 \| 0.3104 \| 0.3708 \| 0.7181 \| 0.3615 \| 0.7273 \| 0.1395 \| 0.4796 \| 0.0763 \| 0.3597 \| 0.0414 \| 0.439 \| 0.1658 \| 0.4336 \| \| 1.052 \| 50.9953 \| 5431 \| 1.1966 \| 0.1676 \| 0.3345 \| 0.1453 \| 0.1543 \| 0.1219 \| 0.2477 \| 0.2439 \| 0.4492 \| 0.493 \| 0.2986 \| 0.387 \| 0.72 \| 0.3652 \| 0.725 \| 0.1442 \| 0.502 \| 0.0701 \| 0.3578 \| 0.0622 \| 0.4119 \| 0.1963 \| 0.4682 \| \| 1.0249 \| 52.0 \| 5538 \| 1.2046 \| 0.1601 \| 0.324 \| 0.1455 \| 0.0884 \| 0.1159 \| 0.2423 \| 0.2383 \| 0.4475 \| 0.4907 \| 0.3318 \| 0.3694 \| 0.7025 \| 0.3368 \| 0.7186 \| 0.1664 \| 0.4959 \| 0.072 \| 0.3758 \| 0.0313 \| 0.3915 \| 0.1941 \| 0.4716 \| \| 1.0286 \| 52.9953 \| 5644 \| 1.2160 \| 0.1732 \| 0.3482 \| 0.1486 \| 0.0809 \| 0.124 \| 0.2539 \| 0.2465 \| 0.4497 \| 0.4899 \| 0.2724 \| 0.3755 \| 0.6934 \| 0.3827 \| 0.7192 \| 0.1516 \| 0.5061 \| 0.0716 \| 0.337 \| 0.0494 \| 0.4322 \| 0.2107 \| 0.455 \| \| 1.0085 \| 54.0 \| 5751 \| 1.2242 \| 0.169 \| 0.3425 \| 0.1446 \| 0.0889 \| 0.1154 \| 0.2541 \| 0.2335 \| 0.4485 \| 0.4852 \| 0.3407 \| 0.3658 \| 0.6959 \| 0.3834 \| 0.718 \| 0.1546 \| 0.4959 \| 0.0797 \| 0.3365 \| 0.0465 \| 0.4373 \| 0.1805 \| 0.4384 \| \| 1.0105 \| 54.9953 \| 5857 \| 1.1761 \| 0.1885 \| 0.3678 \| 0.1675 \| 0.1149 \| 0.1313 \| 0.2775 \| 0.2653 \| 0.4731 \| 0.5102 \| 0.3635 \| 0.3872 \| 0.724 \| 0.4022 \| 0.736 \| 0.1799 \| 0.5429 \| 0.0971 \| 0.3739 \| 0.0536 \| 0.4288 \| 0.2098 \| 0.4692 \| \| 0.9725 \| 56.0 \| 5964 \| 1.2053 \| 0.1867 \| 0.3609 \| 0.1665 \| 0.2528 \| 0.1335 \| 0.2908 \| 0.2491 \| 0.4626 \| 0.5064 \| 0.3851 \| 0.3879 \| 0.7348 \| 0.4134 \| 0.7372 \| 0.1765 \| 0.5367 \| 0.0808 \| 0.3398 \| 0.0615 \| 0.4644 \| 0.2013 \| 0.454 \| \| 0.9825 \| 56.9953 \| 6070 \| 1.1839 \| 0.1857 \| 0.361 \| 0.1718 \| 0.1396 \| 0.1366 \| 0.2957 \| 0.2667 \| 0.4622 \| 0.5012 \| 0.3591 \| 0.3848 \| 0.715 \| 0.4079 \| 0.7395 \| 0.2112 \| 0.5245 \| 0.0823 \| 0.3664 \| 0.0412 \| 0.4237 \| 0.1858 \| 0.4517 \| \| 0.9768 \| 58.0 \| 6177 \| 1.1782 \| 0.1909 \| 0.3696 \| 0.1677 \| 0.1695 \| 0.1316 \| 0.3046 \| 0.2637 \| 0.4752 \| 0.5143 \| 0.3641 \| 0.3965 \| 0.7246 \| 0.4094 \| 0.7285 \| 0.1889 \| 0.5367 \| 0.0866 \| 0.3758 \| 0.0526 \| 0.4559 \| 0.2171 \| 0.4744 \| \| 0.9646 \| 58.9953 \| 6283 \| 1.2234 \| 0.1752 \| 0.3522 \| 0.1485 \| 0.0982 \| 0.1131 \| 0.2967 \| 0.2342 \| 0.4388 \| 0.4825 \| 0.3028 \| 0.3743 \| 0.6903 \| 0.4095 \| 0.711 \| 0.1621 \| 0.4939 \| 0.0666 \| 0.3308 \| 0.0441 \| 0.4322 \| 0.1937 \| 0.4445 \| \| 0.9433 \| 60.0 \| 6390 \| 1.1778 \| 0.2039 \| 0.3913 \| 0.1795 \| 0.1085 \| 0.1387 \| 0.3213 \| 0.2627 \| 0.4721 \| 0.5035 \| 0.3112 \| 0.3827 \| 0.7032 \| 0.4448 \| 0.7448 \| 0.2013 \| 0.5245 \| 0.0995 \| 0.3678 \| 0.0554 \| 0.422 \| 0.2184 \| 0.4583 \| \| 0.9539 \| 60.9953 \| 6496 \| 1.1710 \| 0.2142 \| 0.3998 \| 0.1909 \| 0.0873 \| 0.1439 \| 0.3343 \| 0.258 \| 0.4688 \| 0.5159 \| 0.3227 \| 0.3985 \| 0.71 \| 0.4632 \| 0.7448 \| 0.2376 \| 0.5286 \| 0.0976 \| 0.3645 \| 0.0512 \| 0.4661 \| 0.2214 \| 0.4758 \| \| 0.9323 \| 62.0 \| 6603 \| 1.1525 \| 0.2241 \| 0.4084 \| 0.2017 \| 0.1758 \| 0.1621 \| 0.3357 \| 0.2827 \| 0.4755 \| 0.5187 \| 0.3303 \| 0.3996 \| 0.7289 \| 0.4692 \| 0.7413 \| 0.2387 \| 0.5367 \| 0.1032 \| 0.3635 \| 0.0586 \| 0.4542 \| 0.2508 \| 0.4976 \| \| 0.9312 \| 62.9953 \| 6709 \| 1.1400 \| 0.2201 \| 0.4184 \| 0.1825 \| 0.2621 \| 0.1752 \| 0.3242 \| 0.2887 \| 0.4916 \| 0.531 \| 0.4282 \| 0.4101 \| 0.7258 \| 0.4556 \| 0.7413 \| 0.2209 \| 0.5469 \| 0.0957 \| 0.3716 \| 0.0663 \| 0.4898 \| 0.2622 \| 0.5052 \| \| 0.9131 \| 64.0 \| 6816 \| 1.1468 \| 0.2239 \| 0.4165 \| 0.2042 \| 0.2835 \| 0.1687 \| 0.3288 \| 0.2852 \| 0.4906 \| 0.5291 \| 0.4339 \| 0.4064 \| 0.7331 \| 0.4363 \| 0.7302 \| 0.2399 \| 0.5612 \| 0.1086 \| 0.3754 \| 0.0774 \| 0.4847 \| 0.2575 \| 0.4938 \| \| 0.924 \| 64.9953 \| 6922 \| 1.1554 \| 0.2301 \| 0.4351 \| 0.2124 \| 0.239 \| 0.1666 \| 0.357 \| 0.2763 \| 0.4864 \| 0.5214 \| 0.3761 \| 0.398 \| 0.7455 \| 0.4637 \| 0.725 \| 0.2383 \| 0.5327 \| 0.1101 \| 0.3545 \| 0.09 \| 0.5017 \| 0.2487 \| 0.4934 \| \| 0.9114 \| 66.0 \| 7029 \| 1.1486 \| 0.2231 \| 0.4351 \| 0.2016 \| 0.2949 \| 0.1585 \| 0.3553 \| 0.2745 \| 0.4848 \| 0.5247 \| 0.426 \| 0.4126 \| 0.7391 \| 0.4658 \| 0.7366 \| 0.2095 \| 0.5347 \| 0.1104 \| 0.3768 \| 0.0835 \| 0.4932 \| 0.2465 \| 0.482 \| \| 0.9218 \| 66.9953 \| 7135 \| 1.1245 \| 0.2343 \| 0.4329 \| 0.2067 \| 0.2009 \| 0.1752 \| 0.3804 \| 0.2929 \| 0.4957 \| 0.5349 \| 0.3876 \| 0.4202 \| 0.7486 \| 0.4825 \| 0.7494 \| 0.2458 \| 0.549 \| 0.1214 \| 0.3915 \| 0.0671 \| 0.4831 \| 0.2549 \| 0.5014 \| \| 0.8886 \| 68.0 \| 7242 \| 1.1260 \| 0.236 \| 0.4351 \| 0.2162 \| 0.2133 \| 0.1619 \| 0.3728 \| 0.2844 \| 0.4972 \| 0.5283 \| 0.3972 \| 0.3923 \| 0.7581 \| 0.4756 \| 0.7273 \| 0.2387 \| 0.549 \| 0.1187 \| 0.391 \| 0.0872 \| 0.4678 \| 0.2601 \| 0.5062 \| \| 0.898 \| 68.9953 \| 7348 \| 1.1272 \| 0.2419 \| 0.4601 \| 0.2178 \| 0.2485 \| 0.1736 \| 0.3688 \| 0.2919 \| 0.4958 \| 0.5333 \| 0.3847 \| 0.41 \| 0.7582 \| 0.4948 \| 0.7314 \| 0.2347 \| 0.5388 \| 0.1286 \| 0.3938 \| 0.0871 \| 0.5 \| 0.264 \| 0.5024 \| \| 0.88 \| 70.0 \| 7455 \| 1.1621 \| 0.2398 \| 0.4532 \| 0.2123 \| 0.2639 \| 0.1676 \| 0.3743 \| 0.2785 \| 0.4865 \| 0.5151 \| 0.3999 \| 0.3942 \| 0.7292 \| 0.4905 \| 0.7215 \| 0.256 \| 0.5408 \| 0.1072 \| 0.3526 \| 0.0876 \| 0.4746 \| 0.2574 \| 0.4858 \| \| 0.8868 \| 70.9953 \| 7561 \| 1.1089 \| 0.2574 \| 0.4713 \| 0.2332 \| 0.2675 \| 0.1874 \| 0.392 \| 0.2957 \| 0.5025 \| 0.5356 \| 0.4084 \| 0.4319 \| 0.7417 \| 0.5181 \| 0.7413 \| 0.2715 \| 0.5571 \| 0.13 \| 0.3844 \| 0.1029 \| 0.5017 \| 0.2642 \| 0.4934 \| \| 0.876 \| 72.0 \| 7668 \| 1.1296 \| 0.2469 \| 0.4705 \| 0.2256 \| 0.2927 \| 0.1699 \| 0.3973 \| 0.2881 \| 0.4892 \| 0.5235 \| 0.4206 \| 0.4026 \| 0.7447 \| 0.5116 \| 0.7314 \| 0.2526 \| 0.5653 \| 0.1131 \| 0.3739 \| 0.1002 \| 0.4542 \| 0.257 \| 0.4924 \| \| 0.8786 \| 72.9953 \| 7774 \| 1.1189 \| 0.2503 \| 0.4775 \| 0.2262 \| 0.2579 \| 0.1729 \| 0.4036 \| 0.2811 \| 0.4968 \| 0.5319 \| 0.4364 \| 0.4133 \| 0.7461 \| 0.5128 \| 0.7308 \| 0.2551 \| 0.549 \| 0.1207 \| 0.3915 \| 0.0923 \| 0.4915 \| 0.2707 \| 0.4967 \| \| 0.8646 \| 74.0 \| 7881 \| 1.1545 \| 0.2503 \| 0.4782 \| 0.2289 \| 0.2576 \| 0.1648 \| 0.4083 \| 0.2792 \| 0.4817 \| 0.5187 \| 0.4232 \| 0.3924 \| 0.7514 \| 0.5224 \| 0.7198 \| 0.2514 \| 0.5224 \| 0.1166 \| 0.3635 \| 0.1066 \| 0.5051 \| 0.2547 \| 0.4825 \| \| 0.8624 \| 74.9953 \| 7987 \| 1.1148 \| 0.2612 \| 0.4931 \| 0.2276 \| 0.2794 \| 0.1776 \| 0.4119 \| 0.2917 \| 0.4974 \| 0.5361 \| 0.4242 \| 0.4151 \| 0.7662 \| 0.5376 \| 0.7401 \| 0.2444 \| 0.5633 \| 0.1307 \| 0.3848 \| 0.1186 \| 0.5119 \| 0.275 \| 0.4806 \| \| 0.838 \| 76.0 \| 8094 \| 1.0995 \| 0.2687 \| 0.493 \| 0.2477 \| 0.2754 \| 0.1831 \| 0.4188 \| 0.303 \| 0.497 \| 0.5302 \| 0.3972 \| 0.406 \| 0.7602 \| 0.5425 \| 0.7349 \| 0.2761 \| 0.5367 \| 0.1306 \| 0.3991 \| 0.1082 \| 0.4915 \| 0.2861 \| 0.4886 \| \| 0.8483 \| 76.9953 \| 8200 \| 1.1110 \| 0.2698 \| 0.4914 \| 0.2511 \| 0.1734 \| 0.1845 \| 0.4311 \| 0.2965 \| 0.4996 \| 0.532 \| 0.405 \| 0.4092 \| 0.7608 \| 0.5616 \| 0.7477 \| 0.2642 \| 0.5367 \| 0.135 \| 0.3924 \| 0.1092 \| 0.4966 \| 0.2791 \| 0.4867 \| \| 0.8413 \| 78.0 \| 8307 \| 1.1064 \| 0.2644 \| 0.4912 \| 0.2444 \| 0.1873 \| 0.1831 \| 0.4319 \| 0.2896 \| 0.4983 \| 0.5336 \| 0.3939 \| 0.4117 \| 0.7562 \| 0.5319 \| 0.7343 \| 0.2637 \| 0.5265 \| 0.1325 \| 0.4095 \| 0.1126 \| 0.5 \| 0.2812 \| 0.4976 \| \| 0.8424 \| 78.9953 \| 8413 \| 1.1112 \| 0.2682 \| 0.496 \| 0.2498 \| 0.1394 \| 0.184 \| 0.4279 \| 0.2966 \| 0.4985 \| 0.5272 \| 0.358 \| 0.4079 \| 0.7394 \| 0.552 \| 0.7395 \| 0.2704 \| 0.5429 \| 0.1313 \| 0.3815 \| 0.1094 \| 0.5 \| 0.2779 \| 0.472 \| \| 0.8277 \| 80.0 \| 8520 \| 1.1451 \| 0.2504 \| 0.4758 \| 0.2206 \| 0.2244 \| 0.1643 \| 0.4229 \| 0.2734 \| 0.4878 \| 0.517 \| 0.3697 \| 0.3882 \| 0.7498 \| 0.5308 \| 0.7308 \| 0.2379 \| 0.5347 \| 0.1187 \| 0.373 \| 0.0919 \| 0.4678 \| 0.2725 \| 0.4787 \| \| 0.8269 \| 80.9953 \| 8626 \| 1.1064 \| 0.2697 \| 0.5029 \| 0.2497 \| 0.2324 \| 0.1932 \| 0.4296 \| 0.2832 \| 0.5024 \| 0.5379 \| 0.3857 \| 0.4293 \| 0.7615 \| 0.5475 \| 0.7424 \| 0.2713 \| 0.5673 \| 0.1481 \| 0.3953 \| 0.1 \| 0.4898 \| 0.2818 \| 0.4948 \| \| 0.8159 \| 82.0 \| 8733 \| 1.1055 \| 0.2668 \| 0.5022 \| 0.2361 \| 0.3082 \| 0.1789 \| 0.4284 \| 0.2955 \| 0.4975 \| 0.5315 \| 0.4478 \| 0.4038 \| 0.7377 \| 0.5396 \| 0.7267 \| 0.2607 \| 0.5429 \| 0.1491 \| 0.4024 \| 0.1042 \| 0.5051 \| 0.2805 \| 0.4806 \| \| 0.8195 \| 82.9953 \| 8839 \| 1.1321 \| 0.2674 \| 0.505 \| 0.2418 \| 0.3007 \| 0.1856 \| 0.4311 \| 0.289 \| 0.4912 \| 0.528 \| 0.4278 \| 0.4039 \| 0.7507 \| 0.5368 \| 0.7267 \| 0.2728 \| 0.5633 \| 0.1336 \| 0.3863 \| 0.1169 \| 0.4898 \| 0.2772 \| 0.4739 \| \| 0.8138 \| 84.0 \| 8946 \| 1.1110 \| 0.2731 \| 0.5061 \| 0.2498 \| 0.2881 \| 0.192 \| 0.44 \| 0.2935 \| 0.503 \| 0.5382 \| 0.4484 \| 0.4141 \| 0.7625 \| 0.5537 \| 0.7407 \| 0.272 \| 0.5796 \| 0.1368 \| 0.3863 \| 0.1223 \| 0.5 \| 0.2808 \| 0.4844 \| \| 0.8113 \| 84.9953 \| 9052 \| 1.1105 \| 0.2722 \| 0.5054 \| 0.255 \| 0.2724 \| 0.1862 \| 0.4388 \| 0.2906 \| 0.4969 \| 0.536 \| 0.4272 \| 0.412 \| 0.7586 \| 0.5428 \| 0.7314 \| 0.2666 \| 0.5592 \| 0.1484 \| 0.4033 \| 0.125 \| 0.5051 \| 0.2784 \| 0.481 \| \| 0.808 \| 86.0 \| 9159 \| 1.1130 \| 0.2733 \| 0.5056 \| 0.253 \| 0.2557 \| 0.1873 \| 0.4403 \| 0.2926 \| 0.502 \| 0.5359 \| 0.4107 \| 0.4037 \| 0.7613 \| 0.5414 \| 0.7384 \| 0.2814 \| 0.5612 \| 0.1421 \| 0.3948 \| 0.1221 \| 0.5085 \| 0.2798 \| 0.4768 \| \| 0.8052 \| 86.9953 \| 9265 \| 1.1015 \| 0.2786 \| 0.5087 \| 0.2612 \| 0.2996 \| 0.192 \| 0.4411 \| 0.2995 \| 0.5082 \| 0.5401 \| 0.4446 \| 0.4152 \| 0.7531 \| 0.555 \| 0.7326 \| 0.2882 \| 0.5653 \| 0.1422 \| 0.4076 \| 0.1207 \| 0.5119 \| 0.2867 \| 0.4834 \| \| 0.7967 \| 88.0 \| 9372 \| 1.1301 \| 0.2689 \| 0.5019 \| 0.2442 \| 0.3262 \| 0.1843 \| 0.4353 \| 0.2939 \| 0.4921 \| 0.5253 \| 0.452 \| 0.3952 \| 0.7619 \| 0.5423 \| 0.7279 \| 0.2769 \| 0.5612 \| 0.1295 \| 0.3668 \| 0.1215 \| 0.4983 \| 0.2745 \| 0.472 \| \| 0.7837 \| 88.9953 \| 9478 \| 1.1018 \| 0.276 \| 0.5116 \| 0.2489 \| 0.2803 \| 0.1919 \| 0.4484 \| 0.304 \| 0.5015 \| 0.5301 \| 0.401 \| 0.4065 \| 0.7623 \| 0.5529 \| 0.7308 \| 0.2728 \| 0.549 \| 0.153 \| 0.4014 \| 0.1207 \| 0.4898 \| 0.2806 \| 0.4796 \| \| 0.7865 \| 90.0 \| 9585 \| 1.1292 \| 0.2712 \| 0.5082 \| 0.2462 \| 0.2705 \| 0.1855 \| 0.4462 \| 0.2898 \| 0.494 \| 0.524 \| 0.3992 \| 0.3985 \| 0.7521 \| 0.5493 \| 0.7267 \| 0.2821 \| 0.5429 \| 0.127 \| 0.3673 \| 0.1233 \| 0.5085 \| 0.2742 \| 0.4744 \| \| 0.7882 \| 90.9953 \| 9691 \| 1.1164 \| 0.2766 \| 0.5191 \| 0.2477 \| 0.3024 \| 0.1952 \| 0.4425 \| 0.296 \| 0.5001 \| 0.529 \| 0.4275 \| 0.4097 \| 0.751 \| 0.5528 \| 0.7308 \| 0.2731 \| 0.5347 \| 0.1424 \| 0.3848 \| 0.1298 \| 0.5119 \| 0.2849 \| 0.4829 \| \| 0.7887 \| 92.0 \| 9798 \| 1.1070 \| 0.2776 \| 0.5169 \| 0.2512 \| 0.2553 \| 0.1923 \| 0.4504 \| 0.2999 \| 0.5033 \| 0.5333 \| 0.4005 \| 0.4078 \| 0.7665 \| 0.5547 \| 0.7343 \| 0.2719 \| 0.5449 \| 0.1517 \| 0.3962 \| 0.1237 \| 0.5068 \| 0.2861 \| 0.4844 \| \| 0.7905 \| 92.9953 \| 9904 \| 1.0951 \| 0.2753 \| 0.5169 \| 0.2526 \| 0.2849 \| 0.1916 \| 0.4366 \| 0.2928 \| 0.504 \| 0.54 \| 0.4511 \| 0.411 \| 0.7577 \| 0.5602 \| 0.7337 \| 0.2639 \| 0.549 \| 0.1435 \| 0.4019 \| 0.1204 \| 0.5339 \| 0.2884 \| 0.4815 \| \| 0.7743 \| 94.0 \| 10011 \| 1.0933 \| 0.2819 \| 0.521 \| 0.2597 \| 0.2878 \| 0.1949 \| 0.4462 \| 0.2988 \| 0.5049 \| 0.541 \| 0.4443 \| 0.4101 \| 0.772 \| 0.572 \| 0.7378 \| 0.2717 \| 0.551 \| 0.1474 \| 0.4043 \| 0.1252 \| 0.5254 \| 0.2933 \| 0.4867 \| \| 0.7762 \| 94.9953 \| 10117 \| 1.1082 \| 0.2785 \| 0.5219 \| 0.2596 \| 0.2847 \| 0.1917 \| 0.4507 \| 0.2935 \| 0.4993 \| 0.5339 \| 0.4327 \| 0.4051 \| 0.7669 \| 0.5695 \| 0.7355 \| 0.2691 \| 0.5531 \| 0.1402 \| 0.3834 \| 0.1218 \| 0.5169 \| 0.2921 \| 0.4806 \| \| 0.7618 \| 96.0 \| 10224 \| 1.1062 \| 0.2774 \| 0.5204 \| 0.2555 \| 0.287 \| 0.1876 \| 0.4483 \| 0.2982 \| 0.5005 \| 0.532 \| 0.436 \| 0.4003 \| 0.7614 \| 0.5632 \| 0.7326 \| 0.2667 \| 0.5408 \| 0.1435 \| 0.3938 \| 0.1251 \| 0.5153 \| 0.2886 \| 0.4777 \| \| 0.783 \| 96.9953 \| 10330 \| 1.1082 \| 0.279 \| 0.523 \| 0.2572 \| 0.284 \| 0.1949 \| 0.4486 \| 0.302 \| 0.4993 \| 0.5334 \| 0.4331 \| 0.4071 \| 0.7629 \| 0.5593 \| 0.7308 \| 0.2744 \| 0.5531 \| 0.1447 \| 0.3915 \| 0.1238 \| 0.5085 \| 0.293 \| 0.4834 \| \| 0.7681 \| 98.0 \| 10437 \| 1.0984 \| 0.2819 \| 0.5263 \| 0.257 \| 0.288 \| 0.1948 \| 0.4534 \| 0.304 \| 0.503 \| 0.5368 \| 0.4351 \| 0.413 \| 0.7653 \| 0.5622 \| 0.7326 \| 0.277 \| 0.549 \| 0.1479 \| 0.3957 \| 0.129 \| 0.5169 \| 0.2933 \| 0.4896 \| \| 0.7742 \| 98.9953 \| 10543 \| 1.1026 \| 0.2806 \| 0.5263 \| 0.2569 \| 0.2889 \| 0.1948 \| 0.4543 \| 0.3014 \| 0.5012 \| 0.5341 \| 0.4352 \| 0.4077 \| 0.7639 \| 0.5568 \| 0.732 \| 0.2749 \| 0.549 \| 0.1484 \| 0.3948 \| 0.1271 \| 0.5102 \| 0.2957 \| 0.4848 \| \| 0.7654 \| 99.5305 \| 10600 \| 1.1028 \| 0.2808 \| 0.5257 \| 0.2589 \| 0.2879 \| 0.1953 \| 0.4546 \| 0.3004 \| 0.5007 \| 0.534 \| 0.4339 \| 0.4079 \| 0.7629 \| 0.5566 \| 0.7331 \| 0.2761 \| 0.549 \| 0.1484 \| 0.3943 \| 0.1271 \| 0.5085 \| 0.2956 \| 0.4853 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
sekhharr/detr_finetuned_v11_last_checkpoint	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sekhharr/detr_finetuned_v11_bestlast_checkpoint	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]
Yaroslava270602/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6906 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.6507 \| 0.04 \| 100 \| 2.1677 \| \| 1.8892 \| 0.08 \| 200 \| 1.6329 \| \| 1.638 \| 0.12 \| 300 \| 1.5517 \| \| 1.4457 \| 0.16 \| 400 \| 1.4176 \| \| 1.3489 \| 0.2 \| 500 \| 1.2559 \| \| 1.277 \| 0.24 \| 600 \| 1.2726 \| \| 1.2948 \| 0.28 \| 700 \| 1.2169 \| \| 1.1878 \| 0.32 \| 800 \| 1.1680 \| \| 1.1781 \| 0.36 \| 900 \| 1.1291 \| \| 1.1747 \| 0.4 \| 1000 \| 1.1146 \| \| 1.1966 \| 0.44 \| 1100 \| 1.1399 \| \| 1.1641 \| 0.48 \| 1200 \| 1.0844 \| \| 1.128 \| 0.52 \| 1300 \| 1.1119 \| \| 1.1191 \| 0.56 \| 1400 \| 1.0528 \| \| 1.1435 \| 0.6 \| 1500 \| 1.0689 \| \| 1.1657 \| 0.64 \| 1600 \| 1.1484 \| \| 1.1727 \| 0.68 \| 1700 \| 1.0764 \| \| 1.1085 \| 0.72 \| 1800 \| 1.0391 \| \| 1.0579 \| 0.76 \| 1900 \| 1.0012 \| \| 1.0935 \| 0.8 \| 2000 \| 1.0160 \| \| 1.054 \| 0.84 \| 2100 \| 0.9796 \| \| 1.0486 \| 0.88 \| 2200 \| 0.9508 \| \| 1.0472 \| 0.92 \| 2300 \| 0.9869 \| \| 1.032 \| 0.96 \| 2400 \| 0.9655 \| \| 1.0313 \| 1.0 \| 2500 \| 0.9484 \| \| 1.0096 \| 1.04 \| 2600 \| 0.9676 \| \| 1.0279 \| 1.08 \| 2700 \| 1.0771 \| \| 1.027 \| 1.12 \| 2800 \| 0.9912 \| \| 1.0415 \| 1.16 \| 2900 \| 0.9882 \| \| 1.003 \| 1.2 \| 3000 \| 0.9579 \| \| 1.0084 \| 1.24 \| 3100 \| 0.9288 \| \| 0.9353 \| 1.28 \| 3200 \| 0.9278 \| \| 0.9514 \| 1.32 \| 3300 \| 0.8915 \| \| 0.9452 \| 1.36 \| 3400 \| 0.8904 \| \| 0.9312 \| 1.4 \| 3500 \| 0.8925 \| \| 0.9256 \| 1.44 \| 3600 \| 0.8729 \| \| 0.8861 \| 1.48 \| 3700 \| 0.8655 \| \| 0.9043 \| 1.52 \| 3800 \| 0.8977 \| \| 0.8935 \| 1.56 \| 3900 \| 0.8679 \| \| 0.8974 \| 1.6 \| 4000 \| 0.8908 \| \| 0.9342 \| 1.64 \| 4100 \| 0.8742 \| \| 0.889 \| 1.68 \| 4200 \| 0.8534 \| \| 0.8998 \| 1.72 \| 4300 \| 0.8409 \| \| 0.8727 \| 1.76 \| 4400 \| 0.8333 \| \| 0.8728 \| 1.8 \| 4500 \| 0.8386 \| \| 0.8525 \| 1.84 \| 4600 \| 0.8152 \| \| 0.8709 \| 1.88 \| 4700 \| 0.8146 \| \| 0.8694 \| 1.92 \| 4800 \| 0.8245 \| \| 0.8663 \| 1.96 \| 4900 \| 0.8216 \| \| 0.8442 \| 2.0 \| 5000 \| 0.8019 \| \| 0.8256 \| 2.04 \| 5100 \| 0.8022 \| \| 0.8385 \| 2.08 \| 5200 \| 0.7938 \| \| 0.7995 \| 2.12 \| 5300 \| 0.7958 \| \| 0.8217 \| 2.16 \| 5400 \| 0.7962 \| \| 0.8432 \| 2.2 \| 5500 \| 0.7772 \| \| 0.8228 \| 2.24 \| 5600 \| 0.7857 \| \| 0.8283 \| 2.28 \| 5700 \| 0.7982 \| \| 0.772 \| 2.32 \| 5800 \| 0.7969 \| \| 0.8019 \| 2.36 \| 5900 \| 0.7902 \| \| 0.7805 \| 2.4 \| 6000 \| 0.7782 \| \| 0.802 \| 2.44 \| 6100 \| 0.7681 \| \| 0.8483 \| 2.48 \| 6200 \| 0.7722 \| \| 0.802 \| 2.52 \| 6300 \| 0.7673 \| \| 0.8064 \| 2.56 \| 6400 \| 0.7603 \| \| 0.7638 \| 2.6 \| 6500 \| 0.7475 \| \| 0.7727 \| 2.64 \| 6600 \| 0.7515 \| \| 0.801 \| 2.68 \| 6700 \| 0.7523 \| \| 0.8022 \| 2.72 \| 6800 \| 0.7519 \| \| 0.8074 \| 2.76 \| 6900 \| 0.7555 \| \| 0.7951 \| 2.8 \| 7000 \| 0.7450 \| \| 0.8125 \| 2.84 \| 7100 \| 0.7476 \| \| 0.8085 \| 2.88 \| 7200 \| 0.7505 \| \| 0.7959 \| 2.92 \| 7300 \| 0.7432 \| \| 0.7668 \| 2.96 \| 7400 \| 0.7454 \| \| 0.7666 \| 3.0 \| 7500 \| 0.7419 \| \| 0.7422 \| 3.04 \| 7600 \| 0.7284 \| \| 0.7713 \| 3.08 \| 7700 \| 0.7418 \| \| 0.7296 \| 3.12 \| 7800 \| 0.7274 \| \| 0.7468 \| 3.16 \| 7900 \| 0.7224 \| \| 0.7767 \| 3.2 \| 8000 \| 0.7268 \| \| 0.7526 \| 3.24 \| 8100 \| 0.7210 \| \| 0.7328 \| 3.28 \| 8200 \| 0.7139 \| \| 0.7626 \| 3.32 \| 8300 \| 0.7142 \| \| 0.7515 \| 3.36 \| 8400 \| 0.7102 \| \| 0.7141 \| 3.4 \| 8500 \| 0.7100 \| \| 0.7068 \| 3.44 \| 8600 \| 0.7097 \| \| 0.7274 \| 3.48 \| 8700 \| 0.7018 \| \| 0.7458 \| 3.52 \| 8800 \| 0.7041 \| \| 0.7205 \| 3.56 \| 8900 \| 0.7065 \| \| 0.7643 \| 3.6 \| 9000 \| 0.6985 \| \| 0.6968 \| 3.64 \| 9100 \| 0.6983 \| \| 0.7111 \| 3.68 \| 9200 \| 0.6982 \| \| 0.7229 \| 3.72 \| 9300 \| 0.6920 \| \| 0.7466 \| 3.76 \| 9400 \| 0.6959 \| \| 0.7126 \| 3.8 \| 9500 \| 0.6925 \| \| 0.739 \| 3.84 \| 9600 \| 0.6869 \| \| 0.7449 \| 3.88 \| 9700 \| 0.6939 \| \| 0.7139 \| 3.92 \| 9800 \| 0.6893 \| \| 0.7216 \| 3.96 \| 9900 \| 0.6895 \| \| 0.6942 \| 4.0 \| 10000 \| 0.6906 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # jozhang97-deta-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.7663 - Map: 0.2022 - Map 50: 0.4588 - Map 75: 0.1509 - Map Small: 0.0948 - Map Medium: 0.1223 - Map Large: 0.2882 - Mar 1: 0.2396 - Mar 10: 0.405 - Mar 100: 0.4238 - Mar Small: 0.2134 - Mar Medium: 0.3177 - Mar Large: 0.5501 - Map Coverall: 0.5051 - Mar 100 Coverall: 0.6628 - Map Face Shield: 0.1207 - Mar 100 Face Shield: 0.3371 - Map Gloves: 0.0983 - Mar 100 Gloves: 0.3115 - Map Goggles: 0.1325 - Mar 100 Goggles: 0.431 - Map Mask: 0.1545 - Mar 100 Mask: 0.3768 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 12.2099 \| 0.9953 \| 106 \| 3.7120 \| 0.0067 \| 0.0314 \| 0.0003 \| 0.0 \| 0.0006 \| 0.0068 \| 0.0122 \| 0.0353 \| 0.0421 \| 0.0 \| 0.0067 \| 0.0451 \| 0.0332 \| 0.2049 \| 0.0001 \| 0.0057 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 3.4288 \| 2.0 \| 213 \| 3.8665 \| 0.0117 \| 0.0403 \| 0.0038 \| 0.0 \| 0.0004 \| 0.0118 \| 0.0199 \| 0.0448 \| 0.0505 \| 0.0 \| 0.0015 \| 0.0592 \| 0.0577 \| 0.2348 \| 0.001 \| 0.0143 \| 0.0 \| 0.0021 \| 0.0 \| 0.0 \| 0.0 \| 0.0011 \| \| 3.5031 \| 2.9953 \| 319 \| 3.5816 \| 0.014 \| 0.0448 \| 0.0075 \| 0.0 \| 0.0003 \| 0.0143 \| 0.0236 \| 0.0424 \| 0.0486 \| 0.0 \| 0.0015 \| 0.0659 \| 0.0693 \| 0.2238 \| 0.0001 \| 0.0071 \| 0.0 \| 0.0031 \| 0.0004 \| 0.0024 \| 0.0001 \| 0.0068 \| \| 3.3269 \| 4.0 \| 426 \| 3.1994 \| 0.02 \| 0.0594 \| 0.0065 \| 0.0 \| 0.0002 \| 0.0203 \| 0.0242 \| 0.0567 \| 0.066 \| 0.0004 \| 0.0027 \| 0.1003 \| 0.0991 \| 0.2768 \| 0.0002 \| 0.0271 \| 0.0 \| 0.0078 \| 0.0 \| 0.0095 \| 0.0004 \| 0.0085 \| \| 3.1804 \| 4.9953 \| 532 \| 3.0309 \| 0.0158 \| 0.0494 \| 0.0066 \| 0.0 \| 0.0017 \| 0.0151 \| 0.0262 \| 0.0535 \| 0.064 \| 0.0004 \| 0.0092 \| 0.1022 \| 0.0713 \| 0.2451 \| 0.001 \| 0.0343 \| 0.0001 \| 0.0104 \| 0.0 \| 0.0 \| 0.0068 \| 0.0299 \| \| 2.9763 \| 6.0 \| 639 \| 2.7981 \| 0.0281 \| 0.0939 \| 0.0086 \| 0.0019 \| 0.0023 \| 0.03 \| 0.0354 \| 0.0831 \| 0.098 \| 0.0147 \| 0.0284 \| 0.1149 \| 0.1311 \| 0.3433 \| 0.0006 \| 0.05 \| 0.0 \| 0.0052 \| 0.0001 \| 0.0024 \| 0.009 \| 0.0893 \| \| 2.7978 \| 6.9953 \| 745 \| 2.6720 \| 0.0344 \| 0.0999 \| 0.0131 \| 0.0017 \| 0.0036 \| 0.0338 \| 0.0498 \| 0.109 \| 0.1197 \| 0.0263 \| 0.0547 \| 0.1193 \| 0.1616 \| 0.372 \| 0.005 \| 0.0957 \| 0.0 \| 0.0177 \| 0.0004 \| 0.0333 \| 0.0047 \| 0.0797 \| \| 2.6679 \| 8.0 \| 852 \| 2.6906 \| 0.0363 \| 0.0999 \| 0.0137 \| 0.0146 \| 0.0065 \| 0.0366 \| 0.0438 \| 0.0947 \| 0.104 \| 0.0321 \| 0.0332 \| 0.1264 \| 0.1681 \| 0.3415 \| 0.0083 \| 0.0886 \| 0.0004 \| 0.0167 \| 0.0002 \| 0.0119 \| 0.0047 \| 0.0616 \| \| 2.6637 \| 8.9953 \| 958 \| 2.6518 \| 0.0319 \| 0.1009 \| 0.012 \| 0.0032 \| 0.005 \| 0.0309 \| 0.0462 \| 0.1171 \| 0.133 \| 0.0089 \| 0.0649 \| 0.1368 \| 0.1457 \| 0.4006 \| 0.0048 \| 0.0943 \| 0.0001 \| 0.0167 \| 0.0004 \| 0.0381 \| 0.0086 \| 0.1153 \| \| 2.6412 \| 10.0 \| 1065 \| 2.5417 \| 0.0443 \| 0.12 \| 0.0164 \| 0.0548 \| 0.0015 \| 0.0465 \| 0.0567 \| 0.1178 \| 0.1316 \| 0.0591 \| 0.0452 \| 0.171 \| 0.2142 \| 0.4183 \| 0.0031 \| 0.0929 \| 0.0002 \| 0.0281 \| 0.0006 \| 0.0381 \| 0.0032 \| 0.0808 \| \| 2.6155 \| 10.9953 \| 1171 \| 2.7743 \| 0.0356 \| 0.1122 \| 0.0216 \| 0.0031 \| 0.0049 \| 0.0435 \| 0.0527 \| 0.1254 \| 0.1399 \| 0.0239 \| 0.0623 \| 0.1768 \| 0.1562 \| 0.3909 \| 0.0054 \| 0.08 \| 0.0005 \| 0.0396 \| 0.0015 \| 0.0714 \| 0.0142 \| 0.1175 \| \| 2.5621 \| 12.0 \| 1278 \| 2.5292 \| 0.0473 \| 0.1309 \| 0.0258 \| 0.003 \| 0.0027 \| 0.0567 \| 0.06 \| 0.1265 \| 0.1404 \| 0.0107 \| 0.042 \| 0.2137 \| 0.2226 \| 0.4561 \| 0.0038 \| 0.0629 \| 0.0014 \| 0.038 \| 0.0044 \| 0.0643 \| 0.0045 \| 0.0808 \| \| 2.4871 \| 12.9953 \| 1384 \| 2.5235 \| 0.0418 \| 0.1211 \| 0.0183 \| 0.01 \| 0.0017 \| 0.0451 \| 0.0555 \| 0.1225 \| 0.1389 \| 0.0219 \| 0.0412 \| 0.2163 \| 0.1994 \| 0.4287 \| 0.0021 \| 0.0757 \| 0.0014 \| 0.063 \| 0.0018 \| 0.0595 \| 0.0041 \| 0.0678 \| \| 2.4874 \| 14.0 \| 1491 \| 2.4597 \| 0.0585 \| 0.148 \| 0.0377 \| 0.0142 \| 0.0047 \| 0.0656 \| 0.0746 \| 0.1555 \| 0.1698 \| 0.0353 \| 0.0689 \| 0.2626 \| 0.2705 \| 0.4659 \| 0.0064 \| 0.13 \| 0.0012 \| 0.0562 \| 0.0058 \| 0.0929 \| 0.0087 \| 0.104 \| \| 2.4629 \| 14.9953 \| 1597 \| 2.3910 \| 0.0587 \| 0.1563 \| 0.0333 \| 0.0039 \| 0.0092 \| 0.0703 \| 0.086 \| 0.1896 \| 0.1994 \| 0.0238 \| 0.0976 \| 0.299 \| 0.2543 \| 0.4994 \| 0.0115 \| 0.1971 \| 0.0022 \| 0.0448 \| 0.003 \| 0.1238 \| 0.0225 \| 0.1316 \| \| 2.4231 \| 16.0 \| 1704 \| 2.6214 \| 0.0538 \| 0.1494 \| 0.0256 \| 0.0069 \| 0.0102 \| 0.0676 \| 0.0821 \| 0.1857 \| 0.2013 \| 0.0119 \| 0.0926 \| 0.3454 \| 0.2271 \| 0.489 \| 0.0093 \| 0.1914 \| 0.0023 \| 0.0656 \| 0.0038 \| 0.1095 \| 0.0266 \| 0.1508 \| \| 2.4166 \| 16.9953 \| 1810 \| 2.5325 \| 0.0632 \| 0.1615 \| 0.0332 \| 0.0075 \| 0.0096 \| 0.0749 \| 0.0829 \| 0.1743 \| 0.1932 \| 0.0202 \| 0.0914 \| 0.3148 \| 0.2769 \| 0.4756 \| 0.0044 \| 0.1657 \| 0.0038 \| 0.0651 \| 0.0065 \| 0.1286 \| 0.0244 \| 0.1311 \| \| 2.3794 \| 18.0 \| 1917 \| 2.4702 \| 0.0843 \| 0.1921 \| 0.0629 \| 0.0217 \| 0.0134 \| 0.0982 \| 0.1035 \| 0.2074 \| 0.2204 \| 0.0226 \| 0.1022 \| 0.3572 \| 0.3649 \| 0.5555 \| 0.0108 \| 0.1957 \| 0.0031 \| 0.0911 \| 0.0143 \| 0.1286 \| 0.0283 \| 0.1311 \| \| 2.3384 \| 18.9953 \| 2023 \| 2.5070 \| 0.0737 \| 0.1736 \| 0.0519 \| 0.0007 \| 0.012 \| 0.0866 \| 0.0936 \| 0.1942 \| 0.2156 \| 0.0032 \| 0.1048 \| 0.3206 \| 0.32 \| 0.5494 \| 0.0125 \| 0.2029 \| 0.0058 \| 0.0932 \| 0.0054 \| 0.0976 \| 0.0249 \| 0.135 \| \| 2.3538 \| 20.0 \| 2130 \| 2.5906 \| 0.0647 \| 0.1794 \| 0.0375 \| 0.0033 \| 0.0198 \| 0.0829 \| 0.1073 \| 0.2141 \| 0.2289 \| 0.0059 \| 0.1278 \| 0.3508 \| 0.2457 \| 0.5171 \| 0.0264 \| 0.2214 \| 0.0041 \| 0.1016 \| 0.0081 \| 0.1214 \| 0.0395 \| 0.1831 \| \| 2.5129 \| 20.9953 \| 2236 \| 2.9187 \| 0.0672 \| 0.1692 \| 0.0533 \| 0.0005 \| 0.0144 \| 0.0905 \| 0.1011 \| 0.2206 \| 0.2352 \| 0.002 \| 0.1177 \| 0.4044 \| 0.2685 \| 0.5421 \| 0.028 \| 0.2929 \| 0.0083 \| 0.1083 \| 0.0016 \| 0.0881 \| 0.0297 \| 0.1446 \| \| 2.7734 \| 22.0 \| 2343 \| 3.1307 \| 0.0512 \| 0.1436 \| 0.0257 \| 0.0042 \| 0.0134 \| 0.0861 \| 0.0949 \| 0.2129 \| 0.2249 \| 0.0077 \| 0.1169 \| 0.3711 \| 0.1908 \| 0.5091 \| 0.0348 \| 0.27 \| 0.0037 \| 0.0625 \| 0.0057 \| 0.1333 \| 0.0212 \| 0.1497 \| \| 2.9978 \| 22.9953 \| 2449 \| 3.1732 \| 0.0459 \| 0.1255 \| 0.027 \| 0.0093 \| 0.0144 \| 0.069 \| 0.0842 \| 0.2302 \| 0.2463 \| 0.0128 \| 0.1446 \| 0.3827 \| 0.1654 \| 0.5201 \| 0.0227 \| 0.2414 \| 0.0066 \| 0.0807 \| 0.0138 \| 0.1905 \| 0.021 \| 0.1989 \| \| 3.3046 \| 24.0 \| 2556 \| 3.4681 \| 0.0339 \| 0.1034 \| 0.0174 \| 0.0159 \| 0.0106 \| 0.0538 \| 0.065 \| 0.2138 \| 0.2341 \| 0.1286 \| 0.1402 \| 0.3069 \| 0.1219 \| 0.5024 \| 0.0072 \| 0.1929 \| 0.0037 \| 0.0823 \| 0.0128 \| 0.1738 \| 0.0241 \| 0.2192 \| \| 3.0429 \| 24.9953 \| 2662 \| 2.8145 \| 0.0577 \| 0.1646 \| 0.0351 \| 0.0084 \| 0.0234 \| 0.0838 \| 0.0969 \| 0.2569 \| 0.2758 \| 0.0238 \| 0.167 \| 0.4128 \| 0.1906 \| 0.5646 \| 0.0341 \| 0.2957 \| 0.0068 \| 0.0885 \| 0.0075 \| 0.2071 \| 0.0493 \| 0.2232 \| \| 2.4662 \| 26.0 \| 2769 \| 2.2661 \| 0.0914 \| 0.2143 \| 0.0758 \| 0.0178 \| 0.0174 \| 0.1372 \| 0.127 \| 0.2461 \| 0.2609 \| 0.0259 \| 0.167 \| 0.3821 \| 0.3735 \| 0.5579 \| 0.0195 \| 0.2086 \| 0.0065 \| 0.0958 \| 0.0086 \| 0.2071 \| 0.0491 \| 0.235 \| \| 2.1285 \| 26.9953 \| 2875 \| 2.2159 \| 0.0968 \| 0.2383 \| 0.0584 \| 0.0347 \| 0.0279 \| 0.1258 \| 0.1285 \| 0.2658 \| 0.2884 \| 0.0488 \| 0.1964 \| 0.4154 \| 0.374 \| 0.5579 \| 0.0305 \| 0.28 \| 0.0103 \| 0.1161 \| 0.0161 \| 0.2643 \| 0.053 \| 0.2237 \| \| 2.1347 \| 28.0 \| 2982 \| 2.5509 \| 0.0706 \| 0.1836 \| 0.0441 \| 0.0144 \| 0.0222 \| 0.1024 \| 0.1252 \| 0.2744 \| 0.2974 \| 0.033 \| 0.2016 \| 0.403 \| 0.253 \| 0.5677 \| 0.0291 \| 0.26 \| 0.0164 \| 0.138 \| 0.0144 \| 0.2833 \| 0.0399 \| 0.2379 \| \| 2.3668 \| 28.9953 \| 3088 \| 2.4054 \| 0.0966 \| 0.2499 \| 0.0643 \| 0.0773 \| 0.0414 \| 0.1119 \| 0.1484 \| 0.2965 \| 0.3169 \| 0.1885 \| 0.2178 \| 0.4012 \| 0.3196 \| 0.5811 \| 0.0377 \| 0.2757 \| 0.0233 \| 0.1432 \| 0.0304 \| 0.2905 \| 0.0722 \| 0.2938 \| \| 2.2677 \| 30.0 \| 3195 \| 2.1192 \| 0.1068 \| 0.2461 \| 0.0794 \| 0.036 \| 0.0276 \| 0.1437 \| 0.1434 \| 0.3102 \| 0.3274 \| 0.1926 \| 0.2175 \| 0.4496 \| 0.3972 \| 0.5927 \| 0.0419 \| 0.3186 \| 0.0113 \| 0.1453 \| 0.0285 \| 0.2976 \| 0.055 \| 0.2831 \| \| 2.2587 \| 30.9953 \| 3301 \| 2.6246 \| 0.0757 \| 0.2145 \| 0.0386 \| 0.0579 \| 0.0372 \| 0.0801 \| 0.1214 \| 0.2851 \| 0.3075 \| 0.2843 \| 0.2145 \| 0.3503 \| 0.2378 \| 0.536 \| 0.0198 \| 0.2543 \| 0.0159 \| 0.15 \| 0.0357 \| 0.3048 \| 0.0695 \| 0.2927 \| \| 2.7778 \| 32.0 \| 3408 \| 2.6193 \| 0.0877 \| 0.226 \| 0.0504 \| 0.0423 \| 0.0431 \| 0.0964 \| 0.1302 \| 0.2978 \| 0.3203 \| 0.1287 \| 0.232 \| 0.3622 \| 0.2707 \| 0.5768 \| 0.0352 \| 0.2486 \| 0.0169 \| 0.1594 \| 0.0482 \| 0.3167 \| 0.0675 \| 0.3 \| \| 2.2752 \| 32.9953 \| 3514 \| 2.2670 \| 0.1165 \| 0.286 \| 0.0836 \| 0.0218 \| 0.0533 \| 0.1523 \| 0.1711 \| 0.3059 \| 0.3261 \| 0.0799 \| 0.2451 \| 0.4439 \| 0.4075 \| 0.5909 \| 0.0485 \| 0.2957 \| 0.0223 \| 0.1656 \| 0.0207 \| 0.2857 \| 0.0834 \| 0.2927 \| \| 2.1109 \| 34.0 \| 3621 \| 2.1783 \| 0.1182 \| 0.2898 \| 0.0878 \| 0.0207 \| 0.0406 \| 0.1695 \| 0.1547 \| 0.3043 \| 0.3242 \| 0.0858 \| 0.2155 \| 0.4605 \| 0.402 \| 0.5762 \| 0.0411 \| 0.2757 \| 0.0214 \| 0.1807 \| 0.0475 \| 0.3 \| 0.079 \| 0.2881 \| \| 2.22 \| 34.9953 \| 3727 \| 2.2399 \| 0.1203 \| 0.2683 \| 0.1016 \| 0.0223 \| 0.0404 \| 0.1578 \| 0.1656 \| 0.323 \| 0.3393 \| 0.2078 \| 0.2267 \| 0.4161 \| 0.4218 \| 0.6262 \| 0.0401 \| 0.3057 \| 0.0241 \| 0.1609 \| 0.0327 \| 0.3143 \| 0.0826 \| 0.2893 \| \| 2.5775 \| 36.0 \| 3834 \| 3.4084 \| 0.0927 \| 0.2324 \| 0.0649 \| 0.022 \| 0.0379 \| 0.115 \| 0.1397 \| 0.278 \| 0.298 \| 0.093 \| 0.2007 \| 0.3901 \| 0.2973 \| 0.5427 \| 0.0292 \| 0.2414 \| 0.0228 \| 0.125 \| 0.0481 \| 0.3 \| 0.0661 \| 0.2808 \| \| 2.9257 \| 36.9953 \| 3940 \| 2.5430 \| 0.1051 \| 0.2535 \| 0.0731 \| 0.0469 \| 0.0389 \| 0.1393 \| 0.1607 \| 0.317 \| 0.3411 \| 0.1976 \| 0.2365 \| 0.4454 \| 0.3591 \| 0.5909 \| 0.0259 \| 0.2857 \| 0.0213 \| 0.2104 \| 0.0442 \| 0.3262 \| 0.075 \| 0.2921 \| \| 2.1766 \| 38.0 \| 4047 \| 2.2615 \| 0.1232 \| 0.2757 \| 0.1013 \| 0.0573 \| 0.0413 \| 0.154 \| 0.1625 \| 0.3287 \| 0.3527 \| 0.2073 \| 0.24 \| 0.4692 \| 0.4301 \| 0.6073 \| 0.0324 \| 0.3143 \| 0.0249 \| 0.2042 \| 0.0528 \| 0.3357 \| 0.0756 \| 0.3023 \| \| 2.2174 \| 38.9953 \| 4153 \| 2.1064 \| 0.139 \| 0.3199 \| 0.1186 \| 0.0442 \| 0.0476 \| 0.1704 \| 0.1744 \| 0.3165 \| 0.3317 \| 0.2105 \| 0.2171 \| 0.4155 \| 0.4647 \| 0.6122 \| 0.0287 \| 0.2343 \| 0.051 \| 0.2016 \| 0.05 \| 0.3071 \| 0.1007 \| 0.3034 \| \| 2.0069 \| 40.0 \| 4260 \| 2.1137 \| 0.1274 \| 0.2796 \| 0.1086 \| 0.0711 \| 0.0423 \| 0.1375 \| 0.1705 \| 0.3207 \| 0.3397 \| 0.1597 \| 0.225 \| 0.4317 \| 0.4744 \| 0.6293 \| 0.0293 \| 0.2443 \| 0.0292 \| 0.2146 \| 0.021 \| 0.3048 \| 0.0831 \| 0.3056 \| \| 2.0335 \| 40.9953 \| 4366 \| 2.0880 \| 0.1278 \| 0.2884 \| 0.1103 \| 0.0374 \| 0.0416 \| 0.1395 \| 0.1631 \| 0.3068 \| 0.3302 \| 0.1615 \| 0.2089 \| 0.4282 \| 0.4782 \| 0.628 \| 0.0342 \| 0.2357 \| 0.0179 \| 0.1745 \| 0.0397 \| 0.3571 \| 0.0689 \| 0.2554 \| \| 2.1166 \| 42.0 \| 4473 \| 2.1062 \| 0.1303 \| 0.299 \| 0.1098 \| 0.0324 \| 0.0445 \| 0.1594 \| 0.1709 \| 0.3222 \| 0.3467 \| 0.1433 \| 0.2403 \| 0.4547 \| 0.45 \| 0.6012 \| 0.0586 \| 0.2814 \| 0.0363 \| 0.2208 \| 0.0355 \| 0.3381 \| 0.0708 \| 0.2921 \| \| 1.9831 \| 42.9953 \| 4579 \| 2.0591 \| 0.142 \| 0.3266 \| 0.1181 \| 0.0548 \| 0.0532 \| 0.166 \| 0.1881 \| 0.3438 \| 0.3595 \| 0.1447 \| 0.249 \| 0.4546 \| 0.4789 \| 0.6268 \| 0.0469 \| 0.2686 \| 0.0471 \| 0.2255 \| 0.0494 \| 0.3476 \| 0.0877 \| 0.3288 \| \| 2.0249 \| 44.0 \| 4686 \| 2.1750 \| 0.1331 \| 0.3023 \| 0.108 \| 0.0487 \| 0.0453 \| 0.1746 \| 0.1843 \| 0.3345 \| 0.3565 \| 0.1692 \| 0.2419 \| 0.449 \| 0.4406 \| 0.6226 \| 0.065 \| 0.3257 \| 0.0396 \| 0.2234 \| 0.0421 \| 0.3119 \| 0.0783 \| 0.2989 \| \| 2.1806 \| 44.9953 \| 4792 \| 2.1668 \| 0.1299 \| 0.3103 \| 0.1009 \| 0.0523 \| 0.0368 \| 0.1587 \| 0.1604 \| 0.3055 \| 0.3313 \| 0.1054 \| 0.2044 \| 0.4652 \| 0.4617 \| 0.6165 \| 0.0563 \| 0.27 \| 0.0277 \| 0.2089 \| 0.0341 \| 0.2833 \| 0.0697 \| 0.278 \| \| 1.9865 \| 46.0 \| 4899 \| 2.1022 \| 0.1337 \| 0.3124 \| 0.1006 \| 0.0331 \| 0.0573 \| 0.1585 \| 0.177 \| 0.332 \| 0.3522 \| 0.121 \| 0.2542 \| 0.4359 \| 0.451 \| 0.6159 \| 0.0228 \| 0.2386 \| 0.0341 \| 0.2339 \| 0.0467 \| 0.3524 \| 0.1137 \| 0.3203 \| \| 2.0517 \| 46.9953 \| 5005 \| 2.0253 \| 0.1437 \| 0.3449 \| 0.1205 \| 0.038 \| 0.058 \| 0.1937 \| 0.1831 \| 0.3327 \| 0.3535 \| 0.1973 \| 0.2473 \| 0.4626 \| 0.4515 \| 0.6201 \| 0.0507 \| 0.2729 \| 0.0345 \| 0.2255 \| 0.0846 \| 0.3452 \| 0.0969 \| 0.304 \| \| 1.8315 \| 48.0 \| 5112 \| 1.9870 \| 0.1528 \| 0.3572 \| 0.1134 \| 0.0298 \| 0.0525 \| 0.2276 \| 0.1953 \| 0.3464 \| 0.3668 \| 0.1622 \| 0.2418 \| 0.5026 \| 0.4801 \| 0.6305 \| 0.0633 \| 0.2929 \| 0.0491 \| 0.2354 \| 0.0861 \| 0.3714 \| 0.0855 \| 0.304 \| \| 1.8105 \| 48.9953 \| 5218 \| 1.9702 \| 0.1436 \| 0.3227 \| 0.1212 \| 0.0437 \| 0.0429 \| 0.2046 \| 0.1911 \| 0.3314 \| 0.3517 \| 0.1959 \| 0.2283 \| 0.4668 \| 0.4903 \| 0.6372 \| 0.0616 \| 0.27 \| 0.0348 \| 0.2479 \| 0.066 \| 0.331 \| 0.0653 \| 0.2723 \| \| 1.7928 \| 50.0 \| 5325 \| 1.9007 \| 0.1549 \| 0.3631 \| 0.1243 \| 0.0413 \| 0.0563 \| 0.2183 \| 0.2026 \| 0.3488 \| 0.369 \| 0.13 \| 0.2439 \| 0.4907 \| 0.4838 \| 0.6476 \| 0.0679 \| 0.2914 \| 0.0585 \| 0.2495 \| 0.0713 \| 0.3571 \| 0.0931 \| 0.2994 \| \| 1.7696 \| 50.9953 \| 5431 \| 1.9786 \| 0.1511 \| 0.3631 \| 0.1196 \| 0.0232 \| 0.0595 \| 0.2346 \| 0.204 \| 0.3601 \| 0.3848 \| 0.1566 \| 0.2821 \| 0.4908 \| 0.4767 \| 0.6287 \| 0.0758 \| 0.3171 \| 0.0484 \| 0.25 \| 0.0748 \| 0.4214 \| 0.0801 \| 0.3068 \| \| 1.7579 \| 52.0 \| 5538 \| 1.9172 \| 0.1628 \| 0.3659 \| 0.132 \| 0.0341 \| 0.0698 \| 0.2224 \| 0.2133 \| 0.3753 \| 0.3968 \| 0.2334 \| 0.2767 \| 0.5034 \| 0.498 \| 0.6524 \| 0.0869 \| 0.3343 \| 0.0472 \| 0.2646 \| 0.0795 \| 0.4024 \| 0.1023 \| 0.3305 \| \| 1.7493 \| 52.9953 \| 5644 \| 1.8806 \| 0.1599 \| 0.3797 \| 0.1159 \| 0.0522 \| 0.0727 \| 0.2175 \| 0.2111 \| 0.3677 \| 0.3969 \| 0.2264 \| 0.2769 \| 0.5069 \| 0.4723 \| 0.6482 \| 0.0713 \| 0.3314 \| 0.0662 \| 0.2792 \| 0.066 \| 0.3833 \| 0.1239 \| 0.3424 \| \| 1.7134 \| 54.0 \| 5751 \| 1.9045 \| 0.1663 \| 0.3599 \| 0.1239 \| 0.0372 \| 0.0754 \| 0.2379 \| 0.2115 \| 0.3648 \| 0.3911 \| 0.1374 \| 0.2659 \| 0.522 \| 0.4841 \| 0.6476 \| 0.0674 \| 0.2829 \| 0.0588 \| 0.2807 \| 0.1056 \| 0.4095 \| 0.1155 \| 0.335 \| \| 1.7611 \| 54.9953 \| 5857 \| 1.9231 \| 0.1606 \| 0.3967 \| 0.1162 \| 0.0462 \| 0.0722 \| 0.2079 \| 0.2059 \| 0.3553 \| 0.3789 \| 0.1245 \| 0.2652 \| 0.4963 \| 0.4928 \| 0.639 \| 0.0981 \| 0.3429 \| 0.0482 \| 0.2406 \| 0.0709 \| 0.3643 \| 0.093 \| 0.3079 \| \| 1.7949 \| 56.0 \| 5964 \| 1.8811 \| 0.182 \| 0.4156 \| 0.1361 \| 0.0724 \| 0.0814 \| 0.2389 \| 0.2195 \| 0.3801 \| 0.3956 \| 0.1577 \| 0.2722 \| 0.4984 \| 0.4928 \| 0.6366 \| 0.1112 \| 0.3386 \| 0.0831 \| 0.2812 \| 0.1114 \| 0.4119 \| 0.1113 \| 0.3096 \| \| 1.7328 \| 56.9953 \| 6070 \| 1.8393 \| 0.1802 \| 0.4138 \| 0.143 \| 0.0649 \| 0.0869 \| 0.2511 \| 0.2184 \| 0.3899 \| 0.4079 \| 0.2281 \| 0.2913 \| 0.5215 \| 0.502 \| 0.6512 \| 0.1311 \| 0.3471 \| 0.0729 \| 0.263 \| 0.0834 \| 0.4357 \| 0.1115 \| 0.3424 \| \| 1.6581 \| 58.0 \| 6177 \| 1.8911 \| 0.1818 \| 0.4149 \| 0.1458 \| 0.0567 \| 0.0851 \| 0.254 \| 0.2101 \| 0.3745 \| 0.3931 \| 0.1613 \| 0.266 \| 0.5025 \| 0.4957 \| 0.6512 \| 0.1287 \| 0.3443 \| 0.0735 \| 0.2505 \| 0.083 \| 0.3905 \| 0.1282 \| 0.3288 \| \| 1.6535 \| 58.9953 \| 6283 \| 1.8845 \| 0.1712 \| 0.4046 \| 0.1308 \| 0.0742 \| 0.0783 \| 0.2418 \| 0.2009 \| 0.3816 \| 0.3972 \| 0.2855 \| 0.2882 \| 0.4836 \| 0.5077 \| 0.6518 \| 0.0892 \| 0.3229 \| 0.0578 \| 0.2589 \| 0.0898 \| 0.4286 \| 0.1113 \| 0.3237 \| \| 1.6606 \| 60.0 \| 6390 \| 1.9009 \| 0.1779 \| 0.4172 \| 0.1297 \| 0.0798 \| 0.0946 \| 0.2376 \| 0.2137 \| 0.3647 \| 0.3862 \| 0.235 \| 0.2811 \| 0.4988 \| 0.4882 \| 0.6354 \| 0.0948 \| 0.2729 \| 0.0739 \| 0.2854 \| 0.1031 \| 0.4 \| 0.1297 \| 0.3373 \| \| 1.6766 \| 60.9953 \| 6496 \| 1.9100 \| 0.18 \| 0.4238 \| 0.1385 \| 0.092 \| 0.087 \| 0.2338 \| 0.2233 \| 0.384 \| 0.3989 \| 0.2325 \| 0.2852 \| 0.4949 \| 0.4817 \| 0.636 \| 0.0971 \| 0.3171 \| 0.0831 \| 0.2714 \| 0.1194 \| 0.4429 \| 0.1185 \| 0.3271 \| \| 1.7227 \| 62.0 \| 6603 \| 1.8943 \| 0.173 \| 0.3979 \| 0.1294 \| 0.0935 \| 0.0739 \| 0.2254 \| 0.2172 \| 0.3674 \| 0.389 \| 0.1763 \| 0.2647 \| 0.5074 \| 0.5011 \| 0.6463 \| 0.1096 \| 0.3286 \| 0.0731 \| 0.2854 \| 0.0727 \| 0.3786 \| 0.1087 \| 0.3062 \| \| 1.6737 \| 62.9953 \| 6709 \| 1.9753 \| 0.1645 \| 0.398 \| 0.1159 \| 0.0841 \| 0.0849 \| 0.2382 \| 0.2065 \| 0.3693 \| 0.3867 \| 0.2084 \| 0.2771 \| 0.4862 \| 0.4483 \| 0.6171 \| 0.0934 \| 0.3114 \| 0.0869 \| 0.3005 \| 0.0684 \| 0.3738 \| 0.1256 \| 0.3305 \| \| 1.6768 \| 64.0 \| 6816 \| 1.8531 \| 0.1759 \| 0.401 \| 0.1408 \| 0.065 \| 0.0851 \| 0.2592 \| 0.2178 \| 0.3635 \| 0.3872 \| 0.1646 \| 0.2774 \| 0.5118 \| 0.5142 \| 0.6463 \| 0.0906 \| 0.3114 \| 0.062 \| 0.2844 \| 0.0843 \| 0.3571 \| 0.1284 \| 0.3367 \| \| 1.6543 \| 64.9953 \| 6922 \| 1.8840 \| 0.1828 \| 0.4139 \| 0.1364 \| 0.0964 \| 0.0979 \| 0.2575 \| 0.2195 \| 0.3841 \| 0.4027 \| 0.1731 \| 0.3073 \| 0.504 \| 0.4805 \| 0.6409 \| 0.1276 \| 0.3529 \| 0.088 \| 0.2969 \| 0.093 \| 0.3881 \| 0.1252 \| 0.335 \| \| 1.6153 \| 66.0 \| 7029 \| 1.9622 \| 0.1684 \| 0.3804 \| 0.1298 \| 0.0652 \| 0.0743 \| 0.235 \| 0.2143 \| 0.3699 \| 0.391 \| 0.1739 \| 0.2697 \| 0.4861 \| 0.4784 \| 0.6293 \| 0.1049 \| 0.33 \| 0.0461 \| 0.2745 \| 0.0881 \| 0.4024 \| 0.1244 \| 0.3186 \| \| 1.6143 \| 66.9953 \| 7135 \| 1.8685 \| 0.178 \| 0.394 \| 0.1375 \| 0.0621 \| 0.0948 \| 0.2429 \| 0.2178 \| 0.3883 \| 0.4114 \| 0.2767 \| 0.2987 \| 0.5148 \| 0.5007 \| 0.6372 \| 0.113 \| 0.3529 \| 0.0771 \| 0.2932 \| 0.0741 \| 0.4238 \| 0.1252 \| 0.3497 \| \| 1.6326 \| 68.0 \| 7242 \| 1.8852 \| 0.1745 \| 0.3983 \| 0.1289 \| 0.0568 \| 0.0764 \| 0.23 \| 0.2163 \| 0.3856 \| 0.4044 \| 0.2097 \| 0.2825 \| 0.5231 \| 0.5052 \| 0.6482 \| 0.1178 \| 0.3514 \| 0.0672 \| 0.2849 \| 0.0624 \| 0.3905 \| 0.1196 \| 0.3469 \| \| 1.6507 \| 68.9953 \| 7348 \| 1.8130 \| 0.1821 \| 0.4003 \| 0.1556 \| 0.06 \| 0.1034 \| 0.2663 \| 0.2157 \| 0.3933 \| 0.4077 \| 0.3075 \| 0.2933 \| 0.5294 \| 0.5087 \| 0.6579 \| 0.1021 \| 0.3529 \| 0.0749 \| 0.2969 \| 0.0992 \| 0.4024 \| 0.1256 \| 0.3282 \| \| 1.5577 \| 70.0 \| 7455 \| 1.8646 \| 0.1775 \| 0.3934 \| 0.1375 \| 0.0802 \| 0.0721 \| 0.2704 \| 0.2248 \| 0.386 \| 0.4115 \| 0.2884 \| 0.2959 \| 0.5086 \| 0.5029 \| 0.6518 \| 0.0973 \| 0.3357 \| 0.0637 \| 0.2979 \| 0.0962 \| 0.4262 \| 0.1273 \| 0.3458 \| \| 1.5784 \| 70.9953 \| 7561 \| 1.7817 \| 0.1912 \| 0.4163 \| 0.1575 \| 0.0783 \| 0.0846 \| 0.2848 \| 0.229 \| 0.3986 \| 0.4184 \| 0.3431 \| 0.2889 \| 0.532 \| 0.5187 \| 0.6695 \| 0.1152 \| 0.3429 \| 0.0723 \| 0.3005 \| 0.0953 \| 0.4214 \| 0.1546 \| 0.3576 \| \| 1.506 \| 72.0 \| 7668 \| 1.7696 \| 0.1938 \| 0.4236 \| 0.1464 \| 0.0803 \| 0.1105 \| 0.2592 \| 0.2337 \| 0.3895 \| 0.4151 \| 0.1821 \| 0.2993 \| 0.5415 \| 0.5214 \| 0.6634 \| 0.0966 \| 0.34 \| 0.0775 \| 0.2932 \| 0.1302 \| 0.4262 \| 0.1434 \| 0.3525 \| \| 1.5384 \| 72.9953 \| 7774 \| 1.8206 \| 0.189 \| 0.4304 \| 0.1388 \| 0.0836 \| 0.0839 \| 0.2802 \| 0.2353 \| 0.3889 \| 0.4041 \| 0.2535 \| 0.272 \| 0.5363 \| 0.5046 \| 0.6591 \| 0.109 \| 0.3357 \| 0.0785 \| 0.2922 \| 0.1227 \| 0.4 \| 0.1301 \| 0.3333 \| \| 1.5022 \| 74.0 \| 7881 \| 1.8055 \| 0.197 \| 0.4341 \| 0.1495 \| 0.1022 \| 0.0883 \| 0.2747 \| 0.2256 \| 0.3898 \| 0.4084 \| 0.1913 \| 0.2789 \| 0.5424 \| 0.5225 \| 0.661 \| 0.1332 \| 0.34 \| 0.0687 \| 0.2891 \| 0.1366 \| 0.4286 \| 0.1239 \| 0.3232 \| \| 1.5157 \| 74.9953 \| 7987 \| 1.7750 \| 0.1992 \| 0.4404 \| 0.1541 \| 0.104 \| 0.0937 \| 0.2516 \| 0.2332 \| 0.3996 \| 0.4154 \| 0.1952 \| 0.2843 \| 0.5587 \| 0.5311 \| 0.6762 \| 0.1331 \| 0.36 \| 0.0869 \| 0.2964 \| 0.1116 \| 0.4071 \| 0.1332 \| 0.3373 \| \| 1.4439 \| 76.0 \| 8094 \| 1.8431 \| 0.1877 \| 0.4295 \| 0.1374 \| 0.0729 \| 0.0844 \| 0.2654 \| 0.225 \| 0.3975 \| 0.4176 \| 0.2597 \| 0.2999 \| 0.544 \| 0.5215 \| 0.6579 \| 0.1322 \| 0.3471 \| 0.0673 \| 0.2875 \| 0.0936 \| 0.4643 \| 0.1241 \| 0.3311 \| \| 1.4989 \| 76.9953 \| 8200 \| 1.8236 \| 0.1907 \| 0.4343 \| 0.1425 \| 0.055 \| 0.095 \| 0.2776 \| 0.2183 \| 0.3861 \| 0.4059 \| 0.1827 \| 0.2745 \| 0.5308 \| 0.5238 \| 0.6634 \| 0.1084 \| 0.33 \| 0.0754 \| 0.2849 \| 0.1091 \| 0.4 \| 0.1368 \| 0.3514 \| \| 1.4759 \| 78.0 \| 8307 \| 1.7953 \| 0.1973 \| 0.4484 \| 0.15 \| 0.077 \| 0.0949 \| 0.2868 \| 0.2369 \| 0.402 \| 0.4161 \| 0.1753 \| 0.2936 \| 0.5428 \| 0.5239 \| 0.6634 \| 0.124 \| 0.3514 \| 0.0865 \| 0.3109 \| 0.0945 \| 0.3976 \| 0.1574 \| 0.3571 \| \| 1.4302 \| 78.9953 \| 8413 \| 1.8257 \| 0.1921 \| 0.4446 \| 0.1476 \| 0.0569 \| 0.1067 \| 0.2706 \| 0.2263 \| 0.3944 \| 0.4135 \| 0.17 \| 0.2982 \| 0.5529 \| 0.508 \| 0.6591 \| 0.1226 \| 0.36 \| 0.0782 \| 0.3021 \| 0.1114 \| 0.3976 \| 0.1403 \| 0.3486 \| \| 1.4879 \| 80.0 \| 8520 \| 1.8216 \| 0.1977 \| 0.4478 \| 0.1511 \| 0.0607 \| 0.1093 \| 0.28 \| 0.233 \| 0.4025 \| 0.4213 \| 0.1947 \| 0.3143 \| 0.5392 \| 0.5125 \| 0.6573 \| 0.1274 \| 0.3514 \| 0.087 \| 0.3177 \| 0.1197 \| 0.4286 \| 0.1421 \| 0.3514 \| \| 1.4674 \| 80.9953 \| 8626 \| 1.8194 \| 0.186 \| 0.4463 \| 0.1374 \| 0.056 \| 0.0953 \| 0.28 \| 0.2289 \| 0.3882 \| 0.4068 \| 0.1807 \| 0.2878 \| 0.5349 \| 0.5061 \| 0.6457 \| 0.1236 \| 0.3371 \| 0.0662 \| 0.276 \| 0.0917 \| 0.4143 \| 0.1424 \| 0.361 \| \| 1.4603 \| 82.0 \| 8733 \| 1.7888 \| 0.1925 \| 0.4437 \| 0.142 \| 0.0727 \| 0.0973 \| 0.2871 \| 0.2257 \| 0.39 \| 0.4054 \| 0.1866 \| 0.2849 \| 0.5352 \| 0.5014 \| 0.6463 \| 0.1343 \| 0.33 \| 0.0787 \| 0.2995 \| 0.1028 \| 0.4 \| 0.1455 \| 0.3514 \| \| 1.4798 \| 82.9953 \| 8839 \| 1.8245 \| 0.1922 \| 0.4473 \| 0.1358 \| 0.0696 \| 0.1126 \| 0.2677 \| 0.2258 \| 0.3993 \| 0.415 \| 0.1864 \| 0.3067 \| 0.5338 \| 0.5 \| 0.6427 \| 0.1361 \| 0.3314 \| 0.0868 \| 0.3073 \| 0.0965 \| 0.4286 \| 0.1414 \| 0.365 \| \| 1.4253 \| 84.0 \| 8946 \| 1.7753 \| 0.1932 \| 0.4601 \| 0.1409 \| 0.0801 \| 0.1169 \| 0.2778 \| 0.2316 \| 0.4027 \| 0.4172 \| 0.1949 \| 0.3178 \| 0.5316 \| 0.5108 \| 0.6567 \| 0.124 \| 0.3329 \| 0.0831 \| 0.2937 \| 0.0979 \| 0.4452 \| 0.1501 \| 0.3576 \| \| 1.4397 \| 84.9953 \| 9052 \| 1.7778 \| 0.2023 \| 0.4698 \| 0.1484 \| 0.0747 \| 0.1264 \| 0.2903 \| 0.2312 \| 0.4076 \| 0.423 \| 0.2089 \| 0.3229 \| 0.5092 \| 0.5011 \| 0.6494 \| 0.1172 \| 0.3129 \| 0.107 \| 0.3214 \| 0.1282 \| 0.45 \| 0.158 \| 0.3814 \| \| 1.4086 \| 86.0 \| 9159 \| 1.7550 \| 0.2015 \| 0.472 \| 0.1626 \| 0.0926 \| 0.1138 \| 0.2819 \| 0.2308 \| 0.4085 \| 0.4275 \| 0.2033 \| 0.3209 \| 0.5665 \| 0.5092 \| 0.6591 \| 0.1319 \| 0.3543 \| 0.0894 \| 0.3042 \| 0.1295 \| 0.4571 \| 0.1473 \| 0.3627 \| \| 1.4261 \| 86.9953 \| 9265 \| 1.7907 \| 0.1997 \| 0.4662 \| 0.1461 \| 0.1078 \| 0.1194 \| 0.2946 \| 0.2402 \| 0.3993 \| 0.4178 \| 0.1766 \| 0.3128 \| 0.5568 \| 0.5156 \| 0.661 \| 0.1338 \| 0.3371 \| 0.0888 \| 0.301 \| 0.1181 \| 0.431 \| 0.1423 \| 0.3588 \| \| 1.3943 \| 88.0 \| 9372 \| 1.7906 \| 0.1891 \| 0.4431 \| 0.133 \| 0.0591 \| 0.1053 \| 0.2925 \| 0.234 \| 0.4004 \| 0.419 \| 0.1942 \| 0.3249 \| 0.5366 \| 0.5019 \| 0.6488 \| 0.1097 \| 0.3271 \| 0.0877 \| 0.3052 \| 0.1014 \| 0.4524 \| 0.1446 \| 0.3616 \| \| 1.4016 \| 88.9953 \| 9478 \| 1.7760 \| 0.1929 \| 0.4462 \| 0.1424 \| 0.0828 \| 0.1114 \| 0.2675 \| 0.238 \| 0.4045 \| 0.4254 \| 0.2069 \| 0.3236 \| 0.5476 \| 0.5031 \| 0.6616 \| 0.121 \| 0.3329 \| 0.0897 \| 0.3104 \| 0.098 \| 0.4548 \| 0.1525 \| 0.3672 \| \| 1.3955 \| 90.0 \| 9585 \| 1.7786 \| 0.1955 \| 0.4452 \| 0.1534 \| 0.0952 \| 0.1147 \| 0.2789 \| 0.2383 \| 0.3979 \| 0.4189 \| 0.2118 \| 0.3166 \| 0.5502 \| 0.4977 \| 0.6378 \| 0.1173 \| 0.3286 \| 0.0946 \| 0.312 \| 0.1176 \| 0.4405 \| 0.1502 \| 0.3757 \| \| 1.4014 \| 90.9953 \| 9691 \| 1.7644 \| 0.1975 \| 0.4627 \| 0.1508 \| 0.0865 \| 0.1134 \| 0.2852 \| 0.2394 \| 0.4042 \| 0.4219 \| 0.1963 \| 0.3221 \| 0.5521 \| 0.5035 \| 0.6549 \| 0.1326 \| 0.3371 \| 0.0991 \| 0.3193 \| 0.1154 \| 0.4429 \| 0.1367 \| 0.3554 \| \| 1.3626 \| 92.0 \| 9798 \| 1.7705 \| 0.1993 \| 0.4752 \| 0.143 \| 0.07 \| 0.1152 \| 0.2855 \| 0.2361 \| 0.4022 \| 0.4221 \| 0.1801 \| 0.3227 \| 0.5575 \| 0.5064 \| 0.6555 \| 0.1307 \| 0.3486 \| 0.0898 \| 0.299 \| 0.1215 \| 0.4429 \| 0.1481 \| 0.3644 \| \| 1.3655 \| 92.9953 \| 9904 \| 1.7689 \| 0.2081 \| 0.4735 \| 0.1425 \| 0.0975 \| 0.1234 \| 0.2855 \| 0.2413 \| 0.4007 \| 0.422 \| 0.198 \| 0.3091 \| 0.5544 \| 0.5014 \| 0.6616 \| 0.1282 \| 0.3386 \| 0.0918 \| 0.3073 \| 0.1587 \| 0.4381 \| 0.1603 \| 0.3644 \| \| 1.3913 \| 94.0 \| 10011 \| 1.7834 \| 0.2003 \| 0.4624 \| 0.149 \| 0.0951 \| 0.1082 \| 0.2823 \| 0.2407 \| 0.4025 \| 0.4227 \| 0.2077 \| 0.2991 \| 0.5416 \| 0.4993 \| 0.6628 \| 0.1198 \| 0.3371 \| 0.1071 \| 0.3208 \| 0.1209 \| 0.419 \| 0.1545 \| 0.3734 \| \| 1.4071 \| 94.9953 \| 10117 \| 1.7609 \| 0.2046 \| 0.4761 \| 0.1521 \| 0.0954 \| 0.1232 \| 0.2968 \| 0.2392 \| 0.4064 \| 0.4251 \| 0.1986 \| 0.3225 \| 0.5536 \| 0.4975 \| 0.6579 \| 0.1359 \| 0.3614 \| 0.1034 \| 0.3068 \| 0.1342 \| 0.4238 \| 0.1522 \| 0.3757 \| \| 1.3651 \| 96.0 \| 10224 \| 1.7628 \| 0.2016 \| 0.4717 \| 0.1487 \| 0.0995 \| 0.1203 \| 0.2895 \| 0.2381 \| 0.3965 \| 0.416 \| 0.1919 \| 0.3111 \| 0.5508 \| 0.5079 \| 0.664 \| 0.1225 \| 0.3371 \| 0.0941 \| 0.3005 \| 0.1301 \| 0.4095 \| 0.1533 \| 0.3689 \| \| 1.3568 \| 96.9953 \| 10330 \| 1.7858 \| 0.2008 \| 0.4706 \| 0.15 \| 0.0885 \| 0.1223 \| 0.286 \| 0.2415 \| 0.3952 \| 0.4149 \| 0.2044 \| 0.31 \| 0.5375 \| 0.5041 \| 0.6604 \| 0.1152 \| 0.3214 \| 0.0946 \| 0.3052 \| 0.135 \| 0.4214 \| 0.1552 \| 0.3661 \| \| 1.3502 \| 98.0 \| 10437 \| 1.7613 \| 0.2041 \| 0.4599 \| 0.1525 \| 0.0904 \| 0.1249 \| 0.2908 \| 0.2395 \| 0.4097 \| 0.4292 \| 0.2154 \| 0.3233 \| 0.553 \| 0.5051 \| 0.664 \| 0.127 \| 0.3557 \| 0.1024 \| 0.3177 \| 0.1333 \| 0.4357 \| 0.1528 \| 0.3729 \| \| 1.3658 \| 98.9953 \| 10543 \| 1.7623 \| 0.2016 \| 0.4641 \| 0.1501 \| 0.0908 \| 0.1219 \| 0.2895 \| 0.2398 \| 0.4019 \| 0.4208 \| 0.2099 \| 0.3157 \| 0.5456 \| 0.5034 \| 0.6598 \| 0.1234 \| 0.3314 \| 0.095 \| 0.3115 \| 0.1326 \| 0.4262 \| 0.1536 \| 0.3751 \| \| 1.3272 \| 99.5305 \| 10600 \| 1.7663 \| 0.2022 \| 0.4588 \| 0.1509 \| 0.0948 \| 0.1223 \| 0.2882 \| 0.2396 \| 0.405 \| 0.4238 \| 0.2134 \| 0.3177 \| 0.5501 \| 0.5051 \| 0.6628 \| 0.1207 \| 0.3371 \| 0.0983 \| 0.3115 \| 0.1325 \| 0.431 \| 0.1545 \| 0.3768 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]
henrik895/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.6061 \| 0.4 \| 500 \| 2.5979 \| \| 2.2323 \| 0.8 \| 1000 \| 2.0724 \| \| 1.9294 \| 1.2 \| 1500 \| 1.8745 \| \| 1.8162 \| 1.6 \| 2000 \| 1.7907 \| \| 1.7529 \| 2.0 \| 2500 \| 1.7604 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accesories", "bags", "clothing", "shoes" ]
KevinLe/detr_output	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr_output This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9858 \| 6.37 \| 1000 \| 0.9322 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
myshkin/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.5674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7444 \| 1.0 \| 1250 \| 5.5674 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
vision-tf/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8164 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.05 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2214 \| 0.01 \| 25 \| 2.3644 \| \| 2.21 \| 0.01 \| 50 \| 2.1607 \| \| 2.0636 \| 0.01 \| 75 \| 2.1884 \| \| 2.1722 \| 0.02 \| 100 \| 2.3081 \| \| 2.3687 \| 0.03 \| 125 \| 1.9743 \| \| 2.0154 \| 0.03 \| 150 \| 1.9790 \| \| 1.8828 \| 0.04 \| 175 \| 1.9207 \| \| 1.9719 \| 0.04 \| 200 \| 1.8296 \| \| 1.7973 \| 0.04 \| 225 \| 1.8404 \| \| 1.687 \| 0.05 \| 250 \| 1.8164 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
cheesebird/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2294 \| 0.1 \| 250 \| 1.5375 \| \| 1.4499 \| 0.2 \| 500 \| 1.3111 \| \| 1.2584 \| 0.3 \| 750 \| 1.1323 \| \| 1.1306 \| 0.4 \| 1000 \| 1.0789 \| \| 1.1112 \| 0.5 \| 1250 \| 1.0349 \| \| 1.0478 \| 0.6 \| 1500 \| 0.9710 \| \| 0.9709 \| 0.7 \| 1750 \| 0.9510 \| \| 0.96 \| 0.8 \| 2000 \| 0.9061 \| \| 0.9417 \| 0.9 \| 2250 \| 0.8761 \| \| 0.939 \| 1.0 \| 2500 \| 0.8684 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
AnettSand/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0269 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4399 \| 0.64 \| 100 \| 1.5428 \| \| 1.4239 \| 1.27 \| 200 \| 1.2347 \| \| 1.2024 \| 1.91 \| 300 \| 1.0768 \| \| 1.099 \| 2.55 \| 400 \| 1.0269 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u2o06hbj) # hustvl-yolos-small-finetuned-10k-cppe5 This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.4674 - Map: 0.3363 - Map 50: 0.6506 - Map 75: 0.2992 - Map Small: 0.2695 - Map Medium: 0.2282 - Map Large: 0.4791 - Mar 1: 0.3441 - Mar 10: 0.4988 - Mar 100: 0.5186 - Mar Small: 0.3192 - Mar Medium: 0.3884 - Mar Large: 0.6982 - Map Coverall: 0.607 - Mar 100 Coverall: 0.7716 - Map Face Shield: 0.3854 - Mar 100 Face Shield: 0.5883 - Map Gloves: 0.2283 - Mar 100 Gloves: 0.4093 - Map Goggles: 0.1228 - Mar 100 Goggles: 0.3319 - Map Mask: 0.3379 - Mar 100 Mask: 0.4916 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 1.9479 \| 1.0 \| 107 \| 1.6080 \| 0.15 \| 0.3271 \| 0.1142 \| 0.0227 \| 0.0603 \| 0.1779 \| 0.1663 \| 0.3089 \| 0.3414 \| 0.098 \| 0.2108 \| 0.3973 \| 0.499 \| 0.7191 \| 0.048 \| 0.2467 \| 0.0276 \| 0.3377 \| 0.0156 \| 0.0764 \| 0.1598 \| 0.3268 \| \| 1.4786 \| 2.0 \| 214 \| 1.4219 \| 0.1899 \| 0.3864 \| 0.1627 \| 0.0539 \| 0.0911 \| 0.2756 \| 0.2423 \| 0.4081 \| 0.4299 \| 0.0864 \| 0.3003 \| 0.5424 \| 0.5552 \| 0.7951 \| 0.1065 \| 0.4933 \| 0.0707 \| 0.326 \| 0.042 \| 0.2028 \| 0.1752 \| 0.3324 \| \| 1.3842 \| 3.0 \| 321 \| 1.3797 \| 0.2174 \| 0.4379 \| 0.1755 \| 0.0584 \| 0.1132 \| 0.326 \| 0.2616 \| 0.4277 \| 0.4505 \| 0.2266 \| 0.3179 \| 0.61 \| 0.5588 \| 0.7698 \| 0.1542 \| 0.475 \| 0.0952 \| 0.35 \| 0.0469 \| 0.2764 \| 0.2321 \| 0.3816 \| \| 1.2986 \| 4.0 \| 428 \| 1.3926 \| 0.1978 \| 0.4118 \| 0.1625 \| 0.0355 \| 0.0902 \| 0.3168 \| 0.2456 \| 0.4139 \| 0.4312 \| 0.181 \| 0.3023 \| 0.5948 \| 0.56 \| 0.7568 \| 0.1293 \| 0.455 \| 0.1054 \| 0.3574 \| 0.0389 \| 0.2681 \| 0.1553 \| 0.319 \| \| 1.233 \| 5.0 \| 535 \| 1.3301 \| 0.2271 \| 0.5034 \| 0.1694 \| 0.0505 \| 0.1223 \| 0.3357 \| 0.2595 \| 0.4181 \| 0.4355 \| 0.205 \| 0.304 \| 0.5722 \| 0.5531 \| 0.766 \| 0.2005 \| 0.475 \| 0.1371 \| 0.3647 \| 0.0537 \| 0.2486 \| 0.1912 \| 0.3229 \| \| 1.1644 \| 6.0 \| 642 \| 1.2556 \| 0.2497 \| 0.5164 \| 0.1981 \| 0.0579 \| 0.1447 \| 0.3657 \| 0.2915 \| 0.4434 \| 0.4598 \| 0.1981 \| 0.3224 \| 0.6256 \| 0.5888 \| 0.7753 \| 0.1927 \| 0.4933 \| 0.1338 \| 0.373 \| 0.0736 \| 0.2667 \| 0.2597 \| 0.3905 \| \| 1.1229 \| 7.0 \| 749 \| 1.2388 \| 0.234 \| 0.4963 \| 0.1996 \| 0.1279 \| 0.1367 \| 0.3492 \| 0.2638 \| 0.4433 \| 0.4576 \| 0.2508 \| 0.3912 \| 0.6242 \| 0.5616 \| 0.7895 \| 0.1503 \| 0.4883 \| 0.1564 \| 0.373 \| 0.043 \| 0.2736 \| 0.2589 \| 0.3637 \| \| 1.0939 \| 8.0 \| 856 \| 1.2988 \| 0.2343 \| 0.5195 \| 0.1773 \| 0.0506 \| 0.1319 \| 0.3447 \| 0.2687 \| 0.3976 \| 0.4065 \| 0.1377 \| 0.2645 \| 0.5857 \| 0.5806 \| 0.7494 \| 0.1693 \| 0.4083 \| 0.1114 \| 0.3059 \| 0.0683 \| 0.2069 \| 0.2419 \| 0.362 \| \| 1.0571 \| 9.0 \| 963 \| 1.2067 \| 0.2604 \| 0.5346 \| 0.2265 \| 0.0671 \| 0.1565 \| 0.383 \| 0.3001 \| 0.4545 \| 0.4655 \| 0.1538 \| 0.3488 \| 0.625 \| 0.6037 \| 0.7586 \| 0.2 \| 0.49 \| 0.1749 \| 0.3858 \| 0.0758 \| 0.3097 \| 0.2477 \| 0.3832 \| \| 1.0535 \| 10.0 \| 1070 \| 1.2278 \| 0.2534 \| 0.5112 \| 0.2324 \| 0.0556 \| 0.1444 \| 0.3914 \| 0.3011 \| 0.4593 \| 0.4806 \| 0.1686 \| 0.3462 \| 0.6597 \| 0.6104 \| 0.7821 \| 0.1993 \| 0.525 \| 0.1614 \| 0.3956 \| 0.0553 \| 0.3014 \| 0.2407 \| 0.3989 \| \| 0.9948 \| 11.0 \| 1177 \| 1.2097 \| 0.2653 \| 0.5317 \| 0.2346 \| 0.2441 \| 0.1608 \| 0.3892 \| 0.3125 \| 0.4766 \| 0.4903 \| 0.2854 \| 0.3756 \| 0.643 \| 0.5968 \| 0.7648 \| 0.2183 \| 0.5533 \| 0.1797 \| 0.3936 \| 0.0557 \| 0.3306 \| 0.276 \| 0.4089 \| \| 0.9766 \| 12.0 \| 1284 \| 1.2409 \| 0.258 \| 0.5423 \| 0.2228 \| 0.1649 \| 0.1503 \| 0.3925 \| 0.2986 \| 0.4553 \| 0.4667 \| 0.2045 \| 0.3461 \| 0.6238 \| 0.5955 \| 0.7667 \| 0.2124 \| 0.5267 \| 0.1877 \| 0.3613 \| 0.0559 \| 0.3 \| 0.2382 \| 0.3788 \| \| 0.9642 \| 13.0 \| 1391 \| 1.2483 \| 0.2733 \| 0.5497 \| 0.2377 \| 0.1841 \| 0.1614 \| 0.401 \| 0.3115 \| 0.4527 \| 0.4643 \| 0.2125 \| 0.3235 \| 0.6653 \| 0.6113 \| 0.7821 \| 0.2527 \| 0.55 \| 0.1924 \| 0.3544 \| 0.0694 \| 0.2639 \| 0.2406 \| 0.3709 \| \| 0.9632 \| 14.0 \| 1498 \| 1.2315 \| 0.2728 \| 0.544 \| 0.2363 \| 0.0893 \| 0.1691 \| 0.3978 \| 0.308 \| 0.4605 \| 0.4713 \| 0.1705 \| 0.345 \| 0.6593 \| 0.6124 \| 0.7753 \| 0.2228 \| 0.4933 \| 0.1615 \| 0.3475 \| 0.0861 \| 0.325 \| 0.2814 \| 0.4151 \| \| 0.9342 \| 15.0 \| 1605 \| 1.2185 \| 0.2843 \| 0.5699 \| 0.2436 \| 0.2202 \| 0.1798 \| 0.4107 \| 0.313 \| 0.4628 \| 0.4732 \| 0.2514 \| 0.3365 \| 0.6666 \| 0.6132 \| 0.7858 \| 0.2452 \| 0.54 \| 0.1971 \| 0.3578 \| 0.0855 \| 0.2736 \| 0.2807 \| 0.4089 \| \| 0.9118 \| 16.0 \| 1712 \| 1.2268 \| 0.2759 \| 0.5763 \| 0.2359 \| 0.1335 \| 0.1694 \| 0.4276 \| 0.3045 \| 0.4556 \| 0.4712 \| 0.2254 \| 0.3336 \| 0.6805 \| 0.6205 \| 0.8 \| 0.2553 \| 0.53 \| 0.161 \| 0.349 \| 0.0765 \| 0.2833 \| 0.2661 \| 0.3939 \| \| 0.9031 \| 17.0 \| 1819 \| 1.2224 \| 0.2794 \| 0.5673 \| 0.2443 \| 0.2481 \| 0.1638 \| 0.41 \| 0.3079 \| 0.4683 \| 0.4888 \| 0.275 \| 0.3562 \| 0.6834 \| 0.6053 \| 0.8012 \| 0.2647 \| 0.5367 \| 0.1652 \| 0.3735 \| 0.1022 \| 0.3542 \| 0.2594 \| 0.3782 \| \| 0.8556 \| 18.0 \| 1926 \| 1.1809 \| 0.2933 \| 0.5872 \| 0.265 \| 0.2545 \| 0.1984 \| 0.3951 \| 0.3132 \| 0.4787 \| 0.496 \| 0.2908 \| 0.3581 \| 0.6792 \| 0.5871 \| 0.7981 \| 0.2753 \| 0.5567 \| 0.1937 \| 0.3971 \| 0.0936 \| 0.2903 \| 0.3171 \| 0.438 \| \| 0.8221 \| 19.0 \| 2033 \| 1.2087 \| 0.2939 \| 0.5973 \| 0.249 \| 0.0952 \| 0.1777 \| 0.4381 \| 0.312 \| 0.4685 \| 0.4845 \| 0.133 \| 0.3389 \| 0.6883 \| 0.6256 \| 0.8111 \| 0.2686 \| 0.5233 \| 0.1739 \| 0.3603 \| 0.1 \| 0.3069 \| 0.3015 \| 0.4207 \| \| 0.8202 \| 20.0 \| 2140 \| 1.2266 \| 0.2934 \| 0.5939 \| 0.2522 \| 0.1408 \| 0.1867 \| 0.4349 \| 0.3234 \| 0.4769 \| 0.4945 \| 0.1756 \| 0.3613 \| 0.6994 \| 0.617 \| 0.7975 \| 0.3123 \| 0.5533 \| 0.1686 \| 0.3637 \| 0.0951 \| 0.3486 \| 0.2741 \| 0.4095 \| \| 0.7954 \| 21.0 \| 2247 \| 1.2556 \| 0.2905 \| 0.6004 \| 0.2432 \| 0.1632 \| 0.1839 \| 0.438 \| 0.3135 \| 0.4719 \| 0.4865 \| 0.2116 \| 0.3531 \| 0.6842 \| 0.6094 \| 0.7741 \| 0.2976 \| 0.56 \| 0.1763 \| 0.3574 \| 0.089 \| 0.3069 \| 0.2802 \| 0.4341 \| \| 0.7868 \| 22.0 \| 2354 \| 1.2723 \| 0.2717 \| 0.559 \| 0.2363 \| 0.1494 \| 0.1689 \| 0.411 \| 0.293 \| 0.4624 \| 0.4771 \| 0.1901 \| 0.3415 \| 0.6822 \| 0.5933 \| 0.7735 \| 0.3068 \| 0.5717 \| 0.1651 \| 0.3358 \| 0.0718 \| 0.3236 \| 0.2213 \| 0.381 \| \| 0.7827 \| 23.0 \| 2461 \| 1.2710 \| 0.2957 \| 0.6092 \| 0.2317 \| 0.2667 \| 0.1878 \| 0.4432 \| 0.3189 \| 0.4785 \| 0.4928 \| 0.2995 \| 0.3684 \| 0.6803 \| 0.6028 \| 0.7802 \| 0.3233 \| 0.5533 \| 0.1795 \| 0.3843 \| 0.1219 \| 0.3333 \| 0.2511 \| 0.4128 \| \| 0.7795 \| 24.0 \| 2568 \| 1.2305 \| 0.3039 \| 0.6021 \| 0.2739 \| 0.2054 \| 0.2003 \| 0.4373 \| 0.3229 \| 0.4902 \| 0.5068 \| 0.2637 \| 0.3887 \| 0.6889 \| 0.6232 \| 0.766 \| 0.2872 \| 0.575 \| 0.1954 \| 0.3838 \| 0.1011 \| 0.3528 \| 0.3125 \| 0.4564 \| \| 0.7524 \| 25.0 \| 2675 \| 1.2481 \| 0.2897 \| 0.5968 \| 0.2423 \| 0.2147 \| 0.1875 \| 0.4391 \| 0.3168 \| 0.4866 \| 0.5029 \| 0.2532 \| 0.3815 \| 0.689 \| 0.598 \| 0.779 \| 0.2973 \| 0.58 \| 0.1842 \| 0.3833 \| 0.1061 \| 0.3417 \| 0.2627 \| 0.4307 \| \| 0.7446 \| 26.0 \| 2782 \| 1.2481 \| 0.2894 \| 0.5835 \| 0.2479 \| 0.16 \| 0.182 \| 0.441 \| 0.3083 \| 0.4857 \| 0.496 \| 0.1905 \| 0.3783 \| 0.6953 \| 0.6217 \| 0.7765 \| 0.2919 \| 0.5733 \| 0.1736 \| 0.3652 \| 0.068 \| 0.3319 \| 0.292 \| 0.433 \| \| 0.7203 \| 27.0 \| 2889 \| 1.2720 \| 0.2871 \| 0.5801 \| 0.2338 \| 0.2113 \| 0.1852 \| 0.4258 \| 0.3107 \| 0.48 \| 0.4955 \| 0.2698 \| 0.3781 \| 0.6844 \| 0.6106 \| 0.7698 \| 0.2779 \| 0.5633 \| 0.2001 \| 0.3819 \| 0.0842 \| 0.3333 \| 0.2625 \| 0.4291 \| \| 0.7239 \| 28.0 \| 2996 \| 1.2166 \| 0.3043 \| 0.621 \| 0.256 \| 0.1515 \| 0.1926 \| 0.4491 \| 0.3286 \| 0.4874 \| 0.5037 \| 0.2054 \| 0.3733 \| 0.6882 \| 0.6196 \| 0.7728 \| 0.3166 \| 0.58 \| 0.1917 \| 0.3799 \| 0.095 \| 0.3319 \| 0.2985 \| 0.4536 \| \| 0.6987 \| 29.0 \| 3103 \| 1.2685 \| 0.3008 \| 0.6003 \| 0.256 \| 0.2512 \| 0.1964 \| 0.4285 \| 0.3231 \| 0.4964 \| 0.5118 \| 0.3015 \| 0.3921 \| 0.6779 \| 0.6247 \| 0.7716 \| 0.2988 \| 0.5933 \| 0.1999 \| 0.3819 \| 0.0914 \| 0.3514 \| 0.2895 \| 0.4609 \| \| 0.6776 \| 30.0 \| 3210 \| 1.2834 \| 0.2976 \| 0.6053 \| 0.2574 \| 0.1637 \| 0.1951 \| 0.4457 \| 0.3311 \| 0.4801 \| 0.4952 \| 0.2704 \| 0.3691 \| 0.6739 \| 0.6165 \| 0.787 \| 0.28 \| 0.59 \| 0.194 \| 0.3652 \| 0.1218 \| 0.3236 \| 0.2759 \| 0.4101 \| \| 0.6695 \| 31.0 \| 3317 \| 1.2599 \| 0.2957 \| 0.5942 \| 0.268 \| 0.1376 \| 0.1935 \| 0.4377 \| 0.3115 \| 0.4812 \| 0.4988 \| 0.2589 \| 0.3643 \| 0.6915 \| 0.6154 \| 0.7852 \| 0.2669 \| 0.585 \| 0.2035 \| 0.3936 \| 0.0936 \| 0.2917 \| 0.2991 \| 0.4385 \| \| 0.6829 \| 32.0 \| 3424 \| 1.3085 \| 0.2938 \| 0.5904 \| 0.2481 \| 0.2294 \| 0.1851 \| 0.4296 \| 0.311 \| 0.4789 \| 0.497 \| 0.2771 \| 0.3744 \| 0.6878 \| 0.622 \| 0.7741 \| 0.3067 \| 0.5867 \| 0.1709 \| 0.3505 \| 0.1139 \| 0.3681 \| 0.2557 \| 0.4056 \| \| 0.6632 \| 33.0 \| 3531 \| 1.2422 \| 0.2996 \| 0.6001 \| 0.2578 \| 0.1768 \| 0.1909 \| 0.4405 \| 0.3214 \| 0.4901 \| 0.5081 \| 0.2888 \| 0.3812 \| 0.6588 \| 0.6126 \| 0.7728 \| 0.2943 \| 0.5967 \| 0.2104 \| 0.4064 \| 0.0948 \| 0.3208 \| 0.2858 \| 0.4436 \| \| 0.6518 \| 34.0 \| 3638 \| 1.2245 \| 0.3093 \| 0.611 \| 0.2619 \| 0.1701 \| 0.2059 \| 0.4612 \| 0.3295 \| 0.4808 \| 0.498 \| 0.2206 \| 0.3699 \| 0.6679 \| 0.6104 \| 0.7809 \| 0.3062 \| 0.5633 \| 0.2068 \| 0.3794 \| 0.1203 \| 0.3222 \| 0.303 \| 0.4441 \| \| 0.6649 \| 35.0 \| 3745 \| 1.2282 \| 0.3067 \| 0.621 \| 0.267 \| 0.2252 \| 0.2014 \| 0.4684 \| 0.3313 \| 0.4843 \| 0.5019 \| 0.2741 \| 0.3718 \| 0.6808 \| 0.5992 \| 0.7796 \| 0.3058 \| 0.5617 \| 0.2148 \| 0.4064 \| 0.1257 \| 0.3194 \| 0.2883 \| 0.4425 \| \| 0.6373 \| 36.0 \| 3852 \| 1.3044 \| 0.3085 \| 0.6184 \| 0.2823 \| 0.248 \| 0.2014 \| 0.4489 \| 0.3186 \| 0.4898 \| 0.5107 \| 0.2978 \| 0.3889 \| 0.6776 \| 0.6084 \| 0.7784 \| 0.3151 \| 0.59 \| 0.2058 \| 0.3985 \| 0.113 \| 0.3153 \| 0.3 \| 0.4715 \| \| 0.6139 \| 37.0 \| 3959 \| 1.2725 \| 0.3138 \| 0.6336 \| 0.2796 \| 0.1862 \| 0.203 \| 0.4697 \| 0.3262 \| 0.4928 \| 0.5143 \| 0.2357 \| 0.387 \| 0.6958 \| 0.6116 \| 0.7802 \| 0.3125 \| 0.5667 \| 0.2204 \| 0.3961 \| 0.1065 \| 0.3625 \| 0.3181 \| 0.4659 \| \| 0.6087 \| 38.0 \| 4066 \| 1.2936 \| 0.3072 \| 0.6272 \| 0.2668 \| 0.2027 \| 0.2048 \| 0.4465 \| 0.3151 \| 0.4832 \| 0.5024 \| 0.2461 \| 0.3726 \| 0.6757 \| 0.6071 \| 0.7981 \| 0.3166 \| 0.575 \| 0.1965 \| 0.3814 \| 0.1308 \| 0.3375 \| 0.2848 \| 0.4201 \| \| 0.613 \| 39.0 \| 4173 \| 1.2992 \| 0.3233 \| 0.6431 \| 0.3037 \| 0.1967 \| 0.2164 \| 0.4755 \| 0.3249 \| 0.4887 \| 0.5027 \| 0.2438 \| 0.3761 \| 0.6875 \| 0.6188 \| 0.7753 \| 0.332 \| 0.5717 \| 0.2113 \| 0.3873 \| 0.1314 \| 0.325 \| 0.3232 \| 0.4542 \| \| 0.6009 \| 40.0 \| 4280 \| 1.3210 \| 0.3105 \| 0.6141 \| 0.2801 \| 0.196 \| 0.1976 \| 0.446 \| 0.3223 \| 0.4704 \| 0.4859 \| 0.2349 \| 0.3542 \| 0.6723 \| 0.6392 \| 0.7722 \| 0.3152 \| 0.5433 \| 0.1995 \| 0.3672 \| 0.0986 \| 0.2847 \| 0.3001 \| 0.462 \| \| 0.5766 \| 41.0 \| 4387 \| 1.2828 \| 0.3157 \| 0.6377 \| 0.2786 \| 0.2062 \| 0.2068 \| 0.4597 \| 0.3266 \| 0.4825 \| 0.5013 \| 0.2481 \| 0.3722 \| 0.6859 \| 0.6119 \| 0.7753 \| 0.3294 \| 0.5683 \| 0.2074 \| 0.4 \| 0.1285 \| 0.3139 \| 0.3013 \| 0.4492 \| \| 0.5692 \| 42.0 \| 4494 \| 1.3361 \| 0.3209 \| 0.6257 \| 0.2844 \| 0.2785 \| 0.2123 \| 0.4655 \| 0.3311 \| 0.4922 \| 0.5103 \| 0.3317 \| 0.3787 \| 0.6923 \| 0.6219 \| 0.7802 \| 0.3437 \| 0.5767 \| 0.1992 \| 0.3897 \| 0.1221 \| 0.3347 \| 0.3177 \| 0.4704 \| \| 0.5563 \| 43.0 \| 4601 \| 1.2864 \| 0.3284 \| 0.6405 \| 0.2969 \| 0.2098 \| 0.2261 \| 0.4568 \| 0.3312 \| 0.5031 \| 0.5202 \| 0.2639 \| 0.399 \| 0.6864 \| 0.6278 \| 0.7821 \| 0.3447 \| 0.5733 \| 0.2249 \| 0.4186 \| 0.1179 \| 0.3486 \| 0.327 \| 0.4782 \| \| 0.5581 \| 44.0 \| 4708 \| 1.2884 \| 0.3309 \| 0.6319 \| 0.3104 \| 0.2022 \| 0.2285 \| 0.4627 \| 0.337 \| 0.5046 \| 0.5237 \| 0.268 \| 0.4064 \| 0.6992 \| 0.6404 \| 0.7944 \| 0.353 \| 0.595 \| 0.2145 \| 0.3877 \| 0.1283 \| 0.3639 \| 0.3185 \| 0.4777 \| \| 0.5464 \| 45.0 \| 4815 \| 1.3207 \| 0.3205 \| 0.6256 \| 0.2849 \| 0.1818 \| 0.2114 \| 0.4495 \| 0.3295 \| 0.493 \| 0.5136 \| 0.2564 \| 0.3824 \| 0.7016 \| 0.6279 \| 0.787 \| 0.3335 \| 0.5867 \| 0.2226 \| 0.4064 \| 0.1144 \| 0.3194 \| 0.304 \| 0.4687 \| \| 0.5445 \| 46.0 \| 4922 \| 1.2675 \| 0.3266 \| 0.6518 \| 0.295 \| 0.2526 \| 0.2217 \| 0.4655 \| 0.3354 \| 0.4991 \| 0.5164 \| 0.3 \| 0.3932 \| 0.6916 \| 0.626 \| 0.7883 \| 0.3552 \| 0.5817 \| 0.2189 \| 0.4054 \| 0.1285 \| 0.3347 \| 0.3045 \| 0.4721 \| \| 0.5247 \| 47.0 \| 5029 \| 1.3173 \| 0.3311 \| 0.6464 \| 0.2924 \| 0.2393 \| 0.226 \| 0.4541 \| 0.339 \| 0.4983 \| 0.5182 \| 0.2804 \| 0.3948 \| 0.6897 \| 0.6356 \| 0.784 \| 0.3544 \| 0.6 \| 0.2171 \| 0.4034 \| 0.1408 \| 0.3333 \| 0.3077 \| 0.4704 \| \| 0.5302 \| 48.0 \| 5136 \| 1.2731 \| 0.326 \| 0.631 \| 0.2795 \| 0.2521 \| 0.2174 \| 0.4665 \| 0.3267 \| 0.4931 \| 0.5104 \| 0.2953 \| 0.3882 \| 0.6879 \| 0.6296 \| 0.784 \| 0.3588 \| 0.585 \| 0.1986 \| 0.3961 \| 0.1284 \| 0.3333 \| 0.3147 \| 0.4536 \| \| 0.5149 \| 49.0 \| 5243 \| 1.2684 \| 0.3152 \| 0.6229 \| 0.2801 \| 0.2283 \| 0.2075 \| 0.4572 \| 0.3264 \| 0.4836 \| 0.5022 \| 0.281 \| 0.3707 \| 0.6804 \| 0.6224 \| 0.7772 \| 0.3308 \| 0.5433 \| 0.2041 \| 0.4078 \| 0.1049 \| 0.3153 \| 0.3136 \| 0.4676 \| \| 0.5118 \| 50.0 \| 5350 \| 1.3018 \| 0.3252 \| 0.6248 \| 0.305 \| 0.2196 \| 0.2206 \| 0.4821 \| 0.3287 \| 0.4858 \| 0.5015 \| 0.2654 \| 0.3794 \| 0.6771 \| 0.6159 \| 0.7691 \| 0.3332 \| 0.5717 \| 0.2209 \| 0.3961 \| 0.1375 \| 0.3167 \| 0.3185 \| 0.4542 \| \| 0.502 \| 51.0 \| 5457 \| 1.2466 \| 0.3319 \| 0.653 \| 0.2901 \| 0.2399 \| 0.2178 \| 0.4883 \| 0.3388 \| 0.4905 \| 0.5081 \| 0.289 \| 0.3748 \| 0.6816 \| 0.626 \| 0.7772 \| 0.357 \| 0.5783 \| 0.2109 \| 0.402 \| 0.1552 \| 0.3208 \| 0.3103 \| 0.462 \| \| 0.506 \| 52.0 \| 5564 \| 1.2553 \| 0.3153 \| 0.64 \| 0.2721 \| 0.24 \| 0.2077 \| 0.4769 \| 0.3287 \| 0.4818 \| 0.4982 \| 0.2784 \| 0.372 \| 0.6928 \| 0.6264 \| 0.766 \| 0.3397 \| 0.5717 \| 0.2087 \| 0.3956 \| 0.1133 \| 0.3111 \| 0.2885 \| 0.4464 \| \| 0.5066 \| 53.0 \| 5671 \| 1.3476 \| 0.3352 \| 0.6334 \| 0.2955 \| 0.2544 \| 0.2323 \| 0.4958 \| 0.3383 \| 0.4952 \| 0.5163 \| 0.3038 \| 0.3849 \| 0.7196 \| 0.6251 \| 0.7759 \| 0.3453 \| 0.5733 \| 0.2273 \| 0.4162 \| 0.1552 \| 0.3319 \| 0.3231 \| 0.4844 \| \| 0.4992 \| 54.0 \| 5778 \| 1.3008 \| 0.3303 \| 0.6458 \| 0.292 \| 0.1557 \| 0.2268 \| 0.4735 \| 0.3312 \| 0.4849 \| 0.5011 \| 0.228 \| 0.3711 \| 0.691 \| 0.611 \| 0.7673 \| 0.3633 \| 0.5567 \| 0.2247 \| 0.3995 \| 0.1374 \| 0.2986 \| 0.3149 \| 0.4832 \| \| 0.4791 \| 55.0 \| 5885 \| 1.3185 \| 0.3348 \| 0.6544 \| 0.2884 \| 0.2444 \| 0.2309 \| 0.4804 \| 0.3387 \| 0.4981 \| 0.5153 \| 0.2973 \| 0.3873 \| 0.7085 \| 0.6284 \| 0.7784 \| 0.3575 \| 0.595 \| 0.2163 \| 0.4034 \| 0.1542 \| 0.3278 \| 0.3175 \| 0.4721 \| \| 0.4628 \| 56.0 \| 5992 \| 1.2985 \| 0.3266 \| 0.6258 \| 0.2912 \| 0.216 \| 0.2286 \| 0.4531 \| 0.3407 \| 0.4845 \| 0.5015 \| 0.2562 \| 0.3845 \| 0.6729 \| 0.6301 \| 0.7747 \| 0.3721 \| 0.5833 \| 0.2105 \| 0.402 \| 0.1221 \| 0.3097 \| 0.2983 \| 0.438 \| \| 0.4568 \| 57.0 \| 6099 \| 1.2744 \| 0.3368 \| 0.6356 \| 0.3091 \| 0.2112 \| 0.2331 \| 0.4793 \| 0.3421 \| 0.4889 \| 0.5086 \| 0.2813 \| 0.3778 \| 0.691 \| 0.6273 \| 0.7741 \| 0.3848 \| 0.58 \| 0.2022 \| 0.4078 \| 0.1421 \| 0.2972 \| 0.3276 \| 0.4838 \| \| 0.4508 \| 58.0 \| 6206 \| 1.3367 \| 0.3387 \| 0.6541 \| 0.3073 \| 0.256 \| 0.2365 \| 0.4842 \| 0.3444 \| 0.4925 \| 0.5096 \| 0.3095 \| 0.3798 \| 0.6947 \| 0.6259 \| 0.7735 \| 0.3822 \| 0.5767 \| 0.2121 \| 0.4005 \| 0.1487 \| 0.3125 \| 0.3246 \| 0.4849 \| \| 0.4476 \| 59.0 \| 6313 \| 1.2988 \| 0.3422 \| 0.6574 \| 0.3041 \| 0.2575 \| 0.2349 \| 0.4833 \| 0.3421 \| 0.4973 \| 0.5173 \| 0.3087 \| 0.3878 \| 0.7027 \| 0.6439 \| 0.7858 \| 0.3656 \| 0.5683 \| 0.239 \| 0.4191 \| 0.1374 \| 0.3333 \| 0.325 \| 0.4799 \| \| 0.4418 \| 60.0 \| 6420 \| 1.3153 \| 0.336 \| 0.6532 \| 0.3026 \| 0.2298 \| 0.2296 \| 0.4988 \| 0.342 \| 0.4952 \| 0.5178 \| 0.2729 \| 0.3987 \| 0.6979 \| 0.617 \| 0.7735 \| 0.3692 \| 0.5767 \| 0.2324 \| 0.4152 \| 0.1367 \| 0.3347 \| 0.3246 \| 0.4888 \| \| 0.4255 \| 61.0 \| 6527 \| 1.3474 \| 0.3351 \| 0.6402 \| 0.3107 \| 0.2307 \| 0.2303 \| 0.4998 \| 0.3483 \| 0.5026 \| 0.5188 \| 0.2808 \| 0.3929 \| 0.7016 \| 0.6216 \| 0.7741 \| 0.3898 \| 0.5933 \| 0.2099 \| 0.3975 \| 0.1265 \| 0.3361 \| 0.3277 \| 0.4927 \| \| 0.4398 \| 62.0 \| 6634 \| 1.3079 \| 0.3235 \| 0.6414 \| 0.2716 \| 0.203 \| 0.2249 \| 0.4655 \| 0.3377 \| 0.4969 \| 0.5139 \| 0.2522 \| 0.3896 \| 0.691 \| 0.6164 \| 0.7716 \| 0.3404 \| 0.575 \| 0.2371 \| 0.4216 \| 0.1347 \| 0.3347 \| 0.2889 \| 0.4665 \| \| 0.4373 \| 63.0 \| 6741 \| 1.3848 \| 0.3227 \| 0.6411 \| 0.2715 \| 0.2586 \| 0.2268 \| 0.4554 \| 0.338 \| 0.4883 \| 0.5056 \| 0.2983 \| 0.3867 \| 0.6765 \| 0.6107 \| 0.7599 \| 0.3369 \| 0.59 \| 0.2328 \| 0.4044 \| 0.1322 \| 0.3153 \| 0.3011 \| 0.4587 \| \| 0.4287 \| 64.0 \| 6848 \| 1.3676 \| 0.3218 \| 0.6456 \| 0.2789 \| 0.2563 \| 0.2225 \| 0.4598 \| 0.3365 \| 0.4915 \| 0.5088 \| 0.3148 \| 0.3826 \| 0.6923 \| 0.5985 \| 0.7549 \| 0.3578 \| 0.5667 \| 0.2206 \| 0.4142 \| 0.1304 \| 0.3417 \| 0.3018 \| 0.4665 \| \| 0.4085 \| 65.0 \| 6955 \| 1.3785 \| 0.3343 \| 0.6465 \| 0.2967 \| 0.2321 \| 0.2383 \| 0.4732 \| 0.3415 \| 0.4994 \| 0.514 \| 0.2836 \| 0.3928 \| 0.6854 \| 0.6048 \| 0.763 \| 0.3561 \| 0.5817 \| 0.2355 \| 0.4078 \| 0.1399 \| 0.3319 \| 0.335 \| 0.4855 \| \| 0.4018 \| 66.0 \| 7062 \| 1.3817 \| 0.3259 \| 0.6478 \| 0.279 \| 0.2321 \| 0.227 \| 0.4848 \| 0.3364 \| 0.4897 \| 0.5103 \| 0.2796 \| 0.3899 \| 0.6871 \| 0.6028 \| 0.7605 \| 0.3393 \| 0.57 \| 0.2203 \| 0.4015 \| 0.1491 \| 0.3306 \| 0.318 \| 0.4888 \| \| 0.4005 \| 67.0 \| 7169 \| 1.3791 \| 0.3305 \| 0.6483 \| 0.2948 \| 0.2344 \| 0.225 \| 0.4871 \| 0.3358 \| 0.4895 \| 0.5095 \| 0.2826 \| 0.3829 \| 0.6944 \| 0.6162 \| 0.7654 \| 0.3629 \| 0.5683 \| 0.2217 \| 0.3941 \| 0.1194 \| 0.3222 \| 0.3324 \| 0.4972 \| \| 0.4 \| 68.0 \| 7276 \| 1.3844 \| 0.3413 \| 0.6686 \| 0.2994 \| 0.2306 \| 0.2315 \| 0.4953 \| 0.3424 \| 0.4996 \| 0.5178 \| 0.2897 \| 0.3871 \| 0.7112 \| 0.618 \| 0.7728 \| 0.3626 \| 0.58 \| 0.2307 \| 0.4137 \| 0.1373 \| 0.3208 \| 0.3581 \| 0.5017 \| \| 0.3961 \| 69.0 \| 7383 \| 1.3469 \| 0.334 \| 0.6535 \| 0.2764 \| 0.2806 \| 0.2249 \| 0.4906 \| 0.3449 \| 0.5058 \| 0.5229 \| 0.3374 \| 0.3884 \| 0.7273 \| 0.6161 \| 0.7778 \| 0.3543 \| 0.585 \| 0.2291 \| 0.4088 \| 0.1308 \| 0.3514 \| 0.3396 \| 0.4916 \| \| 0.3817 \| 70.0 \| 7490 \| 1.3834 \| 0.328 \| 0.6577 \| 0.286 \| 0.2902 \| 0.2249 \| 0.4676 \| 0.3436 \| 0.4968 \| 0.5121 \| 0.3332 \| 0.3912 \| 0.6866 \| 0.6162 \| 0.7593 \| 0.3558 \| 0.5717 \| 0.2175 \| 0.4093 \| 0.1222 \| 0.3361 \| 0.3282 \| 0.4844 \| \| 0.3851 \| 71.0 \| 7597 \| 1.3745 \| 0.3268 \| 0.6354 \| 0.281 \| 0.2392 \| 0.2237 \| 0.4644 \| 0.3386 \| 0.4965 \| 0.5132 \| 0.3088 \| 0.3918 \| 0.685 \| 0.6184 \| 0.7654 \| 0.3537 \| 0.575 \| 0.2253 \| 0.4039 \| 0.1238 \| 0.3528 \| 0.313 \| 0.4687 \| \| 0.3773 \| 72.0 \| 7704 \| 1.3953 \| 0.3325 \| 0.6475 \| 0.2841 \| 0.2436 \| 0.2233 \| 0.4887 \| 0.3397 \| 0.4968 \| 0.5134 \| 0.3069 \| 0.3873 \| 0.6961 \| 0.6198 \| 0.7673 \| 0.3492 \| 0.575 \| 0.2159 \| 0.4088 \| 0.1404 \| 0.325 \| 0.3375 \| 0.4911 \| \| 0.3709 \| 73.0 \| 7811 \| 1.3560 \| 0.3299 \| 0.6478 \| 0.2891 \| 0.2394 \| 0.2246 \| 0.4904 \| 0.3415 \| 0.4962 \| 0.5133 \| 0.2887 \| 0.3893 \| 0.6974 \| 0.6232 \| 0.7648 \| 0.3428 \| 0.5567 \| 0.2255 \| 0.4074 \| 0.13 \| 0.3444 \| 0.3281 \| 0.4933 \| \| 0.386 \| 74.0 \| 7918 \| 1.3967 \| 0.3336 \| 0.6619 \| 0.2908 \| 0.2852 \| 0.23 \| 0.4811 \| 0.3439 \| 0.4963 \| 0.5149 \| 0.3293 \| 0.3951 \| 0.6897 \| 0.6098 \| 0.7636 \| 0.3737 \| 0.5967 \| 0.2245 \| 0.401 \| 0.1329 \| 0.3319 \| 0.3271 \| 0.4816 \| \| 0.3584 \| 75.0 \| 8025 \| 1.3931 \| 0.3342 \| 0.6622 \| 0.3032 \| 0.2654 \| 0.2288 \| 0.4765 \| 0.3417 \| 0.495 \| 0.513 \| 0.3084 \| 0.3815 \| 0.7017 \| 0.6155 \| 0.7784 \| 0.3649 \| 0.57 \| 0.2321 \| 0.402 \| 0.1249 \| 0.3222 \| 0.3336 \| 0.4922 \| \| 0.3481 \| 76.0 \| 8132 \| 1.3925 \| 0.3368 \| 0.6585 \| 0.2895 \| 0.269 \| 0.2329 \| 0.4833 \| 0.348 \| 0.5001 \| 0.5193 \| 0.3074 \| 0.3935 \| 0.7015 \| 0.6196 \| 0.7821 \| 0.3493 \| 0.5783 \| 0.2393 \| 0.4118 \| 0.1409 \| 0.3292 \| 0.3348 \| 0.495 \| \| 0.3512 \| 77.0 \| 8239 \| 1.3984 \| 0.3323 \| 0.6513 \| 0.2849 \| 0.2895 \| 0.2248 \| 0.4827 \| 0.3393 \| 0.4967 \| 0.5136 \| 0.3289 \| 0.382 \| 0.7079 \| 0.6188 \| 0.7772 \| 0.339 \| 0.5783 \| 0.2318 \| 0.4074 \| 0.1319 \| 0.3208 \| 0.3402 \| 0.4844 \| \| 0.3366 \| 78.0 \| 8346 \| 1.4160 \| 0.3414 \| 0.6618 \| 0.3016 \| 0.263 \| 0.2376 \| 0.4876 \| 0.3428 \| 0.4978 \| 0.5138 \| 0.3062 \| 0.3873 \| 0.6903 \| 0.6113 \| 0.7704 \| 0.3714 \| 0.595 \| 0.2483 \| 0.4093 \| 0.131 \| 0.3056 \| 0.3449 \| 0.4888 \| \| 0.3278 \| 79.0 \| 8453 \| 1.4257 \| 0.3367 \| 0.6401 \| 0.3062 \| 0.2399 \| 0.2326 \| 0.4775 \| 0.3408 \| 0.4939 \| 0.5115 \| 0.2817 \| 0.3838 \| 0.6977 \| 0.6067 \| 0.771 \| 0.3648 \| 0.5717 \| 0.2445 \| 0.4108 \| 0.1211 \| 0.3069 \| 0.3463 \| 0.4972 \| \| 0.3225 \| 80.0 \| 8560 \| 1.3995 \| 0.3355 \| 0.6592 \| 0.2994 \| 0.2158 \| 0.2357 \| 0.4778 \| 0.3447 \| 0.496 \| 0.5105 \| 0.2617 \| 0.3863 \| 0.6889 \| 0.6033 \| 0.7562 \| 0.3597 \| 0.5817 \| 0.2395 \| 0.4039 \| 0.1327 \| 0.3153 \| 0.3423 \| 0.4955 \| \| 0.3197 \| 81.0 \| 8667 \| 1.3828 \| 0.3368 \| 0.6525 \| 0.3008 \| 0.239 \| 0.2342 \| 0.48 \| 0.3525 \| 0.503 \| 0.5196 \| 0.2863 \| 0.3951 \| 0.6975 \| 0.6113 \| 0.7735 \| 0.3803 \| 0.5817 \| 0.2268 \| 0.4029 \| 0.1239 \| 0.3347 \| 0.3415 \| 0.505 \| \| 0.3119 \| 82.0 \| 8774 \| 1.3821 \| 0.3367 \| 0.6504 \| 0.313 \| 0.2674 \| 0.2299 \| 0.4826 \| 0.3486 \| 0.4961 \| 0.5121 \| 0.309 \| 0.3872 \| 0.692 \| 0.6122 \| 0.763 \| 0.3691 \| 0.58 \| 0.2295 \| 0.4108 \| 0.1337 \| 0.3097 \| 0.3391 \| 0.4972 \| \| 0.3022 \| 83.0 \| 8881 \| 1.4340 \| 0.3378 \| 0.6458 \| 0.2983 \| 0.2439 \| 0.2336 \| 0.4668 \| 0.3417 \| 0.4952 \| 0.5138 \| 0.2866 \| 0.3936 \| 0.6793 \| 0.6111 \| 0.7636 \| 0.3687 \| 0.5783 \| 0.2383 \| 0.4206 \| 0.1301 \| 0.3139 \| 0.341 \| 0.4927 \| \| 0.3011 \| 84.0 \| 8988 \| 1.4571 \| 0.3335 \| 0.6497 \| 0.2893 \| 0.2644 \| 0.2254 \| 0.4719 \| 0.3399 \| 0.4901 \| 0.5069 \| 0.3052 \| 0.3811 \| 0.6801 \| 0.6158 \| 0.7654 \| 0.3622 \| 0.5783 \| 0.2278 \| 0.4029 \| 0.1355 \| 0.3069 \| 0.3261 \| 0.481 \| \| 0.2972 \| 85.0 \| 9095 \| 1.4258 \| 0.3338 \| 0.6461 \| 0.2916 \| 0.2397 \| 0.2283 \| 0.4751 \| 0.3449 \| 0.499 \| 0.516 \| 0.2907 \| 0.3911 \| 0.6938 \| 0.606 \| 0.7574 \| 0.3745 \| 0.6017 \| 0.2265 \| 0.4025 \| 0.1277 \| 0.3264 \| 0.3343 \| 0.4922 \| \| 0.2859 \| 86.0 \| 9202 \| 1.4684 \| 0.329 \| 0.6399 \| 0.2879 \| 0.2591 \| 0.2221 \| 0.4732 \| 0.3452 \| 0.4957 \| 0.5163 \| 0.3076 \| 0.3911 \| 0.6931 \| 0.6055 \| 0.766 \| 0.3602 \| 0.5983 \| 0.2173 \| 0.3936 \| 0.1339 \| 0.3306 \| 0.328 \| 0.4927 \| \| 0.2921 \| 87.0 \| 9309 \| 1.4191 \| 0.3332 \| 0.646 \| 0.2982 \| 0.2655 \| 0.2265 \| 0.477 \| 0.3415 \| 0.5005 \| 0.5196 \| 0.3141 \| 0.3895 \| 0.7048 \| 0.6053 \| 0.7679 \| 0.3804 \| 0.6133 \| 0.2223 \| 0.402 \| 0.1294 \| 0.3278 \| 0.3286 \| 0.4872 \| \| 0.2788 \| 88.0 \| 9416 \| 1.4109 \| 0.3327 \| 0.6484 \| 0.2959 \| 0.2407 \| 0.2284 \| 0.4755 \| 0.3454 \| 0.498 \| 0.5162 \| 0.2875 \| 0.3884 \| 0.7014 \| 0.6128 \| 0.771 \| 0.3555 \| 0.585 \| 0.2313 \| 0.4025 \| 0.1274 \| 0.3333 \| 0.3366 \| 0.4894 \| \| 0.2808 \| 89.0 \| 9523 \| 1.4585 \| 0.3333 \| 0.6453 \| 0.3076 \| 0.2643 \| 0.2279 \| 0.4756 \| 0.3423 \| 0.4977 \| 0.5168 \| 0.3141 \| 0.388 \| 0.6946 \| 0.6058 \| 0.7673 \| 0.3719 \| 0.5967 \| 0.2322 \| 0.4118 \| 0.1281 \| 0.3139 \| 0.3286 \| 0.4944 \| \| 0.2747 \| 90.0 \| 9630 \| 1.4490 \| 0.3338 \| 0.6433 \| 0.2976 \| 0.2402 \| 0.2257 \| 0.4754 \| 0.3455 \| 0.4997 \| 0.5175 \| 0.2859 \| 0.3942 \| 0.6893 \| 0.6087 \| 0.7667 \| 0.3793 \| 0.595 \| 0.2274 \| 0.4083 \| 0.1186 \| 0.3264 \| 0.3347 \| 0.4911 \| \| 0.2528 \| 91.0 \| 9737 \| 1.4493 \| 0.3362 \| 0.6504 \| 0.2926 \| 0.2687 \| 0.2288 \| 0.4798 \| 0.342 \| 0.5006 \| 0.5198 \| 0.3141 \| 0.3965 \| 0.6867 \| 0.6044 \| 0.7704 \| 0.389 \| 0.6 \| 0.2308 \| 0.4103 \| 0.1191 \| 0.3222 \| 0.3375 \| 0.4961 \| \| 0.2593 \| 92.0 \| 9844 \| 1.4320 \| 0.334 \| 0.6486 \| 0.2993 \| 0.2461 \| 0.2263 \| 0.4702 \| 0.3423 \| 0.4986 \| 0.517 \| 0.2961 \| 0.3884 \| 0.6883 \| 0.6089 \| 0.7679 \| 0.3741 \| 0.5917 \| 0.2337 \| 0.4078 \| 0.1149 \| 0.3208 \| 0.3382 \| 0.4966 \| \| 0.2685 \| 93.0 \| 9951 \| 1.4475 \| 0.3357 \| 0.6478 \| 0.2992 \| 0.2672 \| 0.2258 \| 0.4738 \| 0.3425 \| 0.4978 \| 0.5165 \| 0.3123 \| 0.3859 \| 0.7037 \| 0.6143 \| 0.771 \| 0.3774 \| 0.5883 \| 0.2311 \| 0.4132 \| 0.1173 \| 0.325 \| 0.3386 \| 0.4849 \| \| 0.2618 \| 94.0 \| 10058 \| 1.4451 \| 0.3385 \| 0.6593 \| 0.2988 \| 0.2673 \| 0.2324 \| 0.4768 \| 0.3457 \| 0.5009 \| 0.519 \| 0.316 \| 0.3898 \| 0.6985 \| 0.6104 \| 0.7716 \| 0.387 \| 0.5967 \| 0.2296 \| 0.4039 \| 0.1281 \| 0.3278 \| 0.3373 \| 0.495 \| \| 0.2513 \| 95.0 \| 10165 \| 1.4426 \| 0.3403 \| 0.6552 \| 0.313 \| 0.2645 \| 0.2336 \| 0.4795 \| 0.3429 \| 0.5006 \| 0.5179 \| 0.3109 \| 0.391 \| 0.6934 \| 0.6134 \| 0.7735 \| 0.3953 \| 0.595 \| 0.2311 \| 0.4025 \| 0.1209 \| 0.3278 \| 0.3408 \| 0.4911 \| \| 0.2596 \| 96.0 \| 10272 \| 1.4438 \| 0.338 \| 0.6557 \| 0.3033 \| 0.2669 \| 0.2288 \| 0.4786 \| 0.344 \| 0.5005 \| 0.5189 \| 0.317 \| 0.3915 \| 0.6914 \| 0.6119 \| 0.7722 \| 0.3964 \| 0.595 \| 0.2265 \| 0.4054 \| 0.1177 \| 0.3347 \| 0.3374 \| 0.4872 \| \| 0.2363 \| 97.0 \| 10379 \| 1.4447 \| 0.3379 \| 0.6526 \| 0.3044 \| 0.2687 \| 0.2297 \| 0.4771 \| 0.3437 \| 0.5013 \| 0.5196 \| 0.32 \| 0.3893 \| 0.6969 \| 0.608 \| 0.7735 \| 0.3861 \| 0.5917 \| 0.2335 \| 0.4083 \| 0.122 \| 0.3319 \| 0.3396 \| 0.4927 \| \| 0.2346 \| 98.0 \| 10486 \| 1.4613 \| 0.3366 \| 0.6512 \| 0.299 \| 0.267 \| 0.2288 \| 0.4809 \| 0.3425 \| 0.5006 \| 0.5201 \| 0.3166 \| 0.391 \| 0.6999 \| 0.6083 \| 0.7722 \| 0.3887 \| 0.5933 \| 0.2285 \| 0.4088 \| 0.1186 \| 0.3319 \| 0.3388 \| 0.4944 \| \| 0.2452 \| 99.0 \| 10593 \| 1.4607 \| 0.3362 \| 0.6498 \| 0.3001 \| 0.2676 \| 0.2286 \| 0.4805 \| 0.344 \| 0.4982 \| 0.5179 \| 0.3168 \| 0.3889 \| 0.6997 \| 0.6076 \| 0.7716 \| 0.3863 \| 0.585 \| 0.2264 \| 0.4049 \| 0.124 \| 0.3347 \| 0.3368 \| 0.4933 \| \| 0.2287 \| 100.0 \| 10700 \| 1.4674 \| 0.3363 \| 0.6506 \| 0.2992 \| 0.2695 \| 0.2282 \| 0.4791 \| 0.3441 \| 0.4988 \| 0.5186 \| 0.3192 \| 0.3884 \| 0.6982 \| 0.607 \| 0.7716 \| 0.3854 \| 0.5883 \| 0.2283 \| 0.4093 \| 0.1228 \| 0.3319 \| 0.3379 \| 0.4916 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
leylaut/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0419 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0815 \| 1.0 \| 2500 \| 1.0419 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
kuchidareo/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 313 \| 5.5361 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
goouthy/detr_output	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr_output This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 8.3626 - eval_runtime: 131.87 - eval_samples_per_second: 7.583 - eval_steps_per_second: 0.948 - epoch: 1.6 - step: 500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
oskarkuuse/detr2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr2 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2191 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3967 \| 1.0 \| 1250 \| 1.5527 \| \| 1.7891 \| 2.0 \| 2500 \| 1.4216 \| \| 1.537 \| 3.0 \| 3750 \| 1.3198 \| \| 1.2472 \| 4.0 \| 5000 \| 1.2333 \| \| 1.1635 \| 5.0 \| 6250 \| 1.2191 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
asldlaskdlk/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2692 \| 1.0 \| 2500 \| 1.2485 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
alizeyn/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9499 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1069 \| 0.2 \| 500 \| 1.4327 \| \| 1.2885 \| 0.4 \| 1000 \| 1.1961 \| \| 1.1377 \| 0.6 \| 1500 \| 1.0482 \| \| 1.0395 \| 0.8 \| 2000 \| 0.9764 \| \| 1.0037 \| 1.0 \| 2500 \| 0.9499 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "2", "3", "1", "0" ]
depek/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8756 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7508 \| 0.16 \| 300 \| 1.7472 \| \| 1.412 \| 0.32 \| 600 \| 1.3611 \| \| 1.2549 \| 0.48 \| 900 \| 1.1682 \| \| 1.0701 \| 0.64 \| 1200 \| 1.0696 \| \| 0.9812 \| 0.8 \| 1500 \| 1.0103 \| \| 1.0346 \| 0.96 \| 1800 \| 0.9782 \| \| 1.014 \| 1.12 \| 2100 \| 0.9429 \| \| 0.9691 \| 1.28 \| 2400 \| 0.9248 \| \| 0.9749 \| 1.44 \| 2700 \| 0.8947 \| \| 0.9707 \| 1.6 \| 3000 \| 0.8887 \| \| 0.938 \| 1.76 \| 3300 \| 0.8889 \| \| 0.9477 \| 1.92 \| 3600 \| 0.8756 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
jbossenk/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6853 \| 0.32 \| 50 \| 1.8717 \| \| 1.6419 \| 0.64 \| 100 \| 1.4752 \| \| 1.4029 \| 0.96 \| 150 \| 1.2728 \| \| 1.2596 \| 1.27 \| 200 \| 1.1683 \| \| 1.1761 \| 1.59 \| 250 \| 1.0847 \| \| 1.106 \| 1.91 \| 300 \| 1.0396 \| \| 1.0586 \| 2.23 \| 350 \| 0.9886 \| \| 1.0258 \| 2.55 \| 400 \| 0.9653 \| \| 1.0151 \| 2.87 \| 450 \| 0.9448 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "clothing", "shoes", "bags", "accessories" ]
gregorrehand/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.5644 \| 0.4 \| 500 \| 2.4934 \| \| 2.1886 \| 0.8 \| 1000 \| 2.1209 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
Teele/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0407 \| 1.0 \| 3334 \| 0.9664 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
obednsiah/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.3956 \| 0.16 \| 50 \| 4.0045 \| \| 3.5668 \| 0.32 \| 100 \| 3.3500 \| \| 2.9609 \| 0.48 \| 150 \| 2.9119 \| \| 2.6387 \| 0.64 \| 200 \| 2.5813 \| \| 2.411 \| 0.8 \| 250 \| 2.4280 \| \| 2.3426 \| 0.96 \| 300 \| 2.3771 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
Naghma/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9572 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8945 \| 1.0 \| 2500 \| 1.9572 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessory", "top", "bottom", "shoe" ]
Karl-Erik/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2889 - eval_runtime: 145.9792 - eval_samples_per_second: 6.85 - eval_steps_per_second: 0.856 - epoch: 0.64 - step: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
RandomCatLover/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
AnetteHabanen/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
qubvel-hf/detr-resnet-50-finetuned-10k-cppe5-no-trainer	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 0.9865 - Map: 0.3192 - Map 50: 0.6187 - Map 75: 0.3039 - Map small: 0.271 - Map medium: 0.2307 - Map large: 0.4635 - Mar 1: 0.2947 - Mar 10: 0.4873 - Mar 100: 0.5005 - Mar small: 0.3421 - Mar medium: 0.4012 - Mar large: 0.6176 - Map Coverall: 0.5908 - Mar 100 Coverall: 0.6978 - Map Face Shield: 0.3773 - Mar 100 Face Shield: 0.6353 - Map Gloves: 0.2142 - Mar 100 Gloves: 0.3902 - Map Goggles: 0.1483 - Mar 100 Goggles: 0.3906 - Map Mask: 0.2653 - Mar 100 Mask: 0.3880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
RoblabWhGe/rescuedet-deformable-detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-sensetime-finetuned-firedetv7 This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the RoblabWhGe/FireDetDataset dataset. It achieves the following results on the evaluation set: - Loss: 0.9075 - Map: 0.3883 - Map 50: 0.7422 - Map 75: 0.3707 - Map Small: 0.2639 - Map Medium: 0.4432 - Map Large: 0.5149 - Mar 1: 0.1286 - Mar 10: 0.419 - Mar 100: 0.5325 - Mar Small: 0.3975 - Mar Medium: 0.5729 - Mar Large: 0.68 - Map Fire: 0.2322 - Mar 100 Fire: 0.4708 - Map Vehicle: 0.5547 - Mar 100 Vehicle: 0.6431 - Map Human: 0.3779 - Mar 100 Human: 0.4834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 300.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Fire \| Mar 100 Fire \| Map Vehicle \| Mar 100 Vehicle \| Map Human \| Mar 100 Human \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:--------:\|:------------:\|:-----------:\|:---------------:\|:---------:\|:-------------:\| \| 2.3092 \| 1.0 \| 674 \| 1.4485 \| 0.0919 \| 0.2097 \| 0.0678 \| 0.0306 \| 0.1068 \| 0.1693 \| 0.0613 \| 0.2179 \| 0.3221 \| 0.1226 \| 0.4115 \| 0.4564 \| 0.0289 \| 0.3272 \| 0.1826 \| 0.3908 \| 0.064 \| 0.2482 \| \| 1.4291 \| 2.0 \| 1348 \| 1.3315 \| 0.1676 \| 0.3524 \| 0.1505 \| 0.0696 \| 0.2179 \| 0.2573 \| 0.0816 \| 0.2714 \| 0.3792 \| 0.1992 \| 0.4641 \| 0.4846 \| 0.1022 \| 0.3921 \| 0.2843 \| 0.473 \| 0.1163 \| 0.2724 \| \| 1.3582 \| 3.0 \| 2022 \| 1.3595 \| 0.1631 \| 0.376 \| 0.1207 \| 0.0557 \| 0.2285 \| 0.2385 \| 0.0763 \| 0.2541 \| 0.3545 \| 0.1701 \| 0.4375 \| 0.4512 \| 0.1117 \| 0.3678 \| 0.2722 \| 0.4536 \| 0.1055 \| 0.2421 \| \| 1.325 \| 4.0 \| 2696 \| 1.3166 \| 0.1733 \| 0.3837 \| 0.1372 \| 0.0488 \| 0.2463 \| 0.2682 \| 0.0845 \| 0.2665 \| 0.3579 \| 0.1626 \| 0.4454 \| 0.4994 \| 0.1374 \| 0.3755 \| 0.2715 \| 0.4389 \| 0.111 \| 0.2592 \| \| 1.2992 \| 5.0 \| 3370 \| 1.2836 \| 0.2032 \| 0.4185 \| 0.1693 \| 0.0725 \| 0.279 \| 0.3113 \| 0.089 \| 0.2913 \| 0.389 \| 0.1825 \| 0.476 \| 0.5379 \| 0.12 \| 0.3659 \| 0.3327 \| 0.4907 \| 0.1569 \| 0.3104 \| \| 1.2567 \| 6.0 \| 4044 \| 1.2566 \| 0.1983 \| 0.441 \| 0.1491 \| 0.0793 \| 0.2675 \| 0.3082 \| 0.0858 \| 0.2833 \| 0.376 \| 0.2 \| 0.4517 \| 0.5109 \| 0.1268 \| 0.372 \| 0.3034 \| 0.4574 \| 0.1647 \| 0.2986 \| \| 1.2463 \| 7.0 \| 4718 \| 1.2504 \| 0.2 \| 0.4294 \| 0.1663 \| 0.0593 \| 0.2782 \| 0.2958 \| 0.0855 \| 0.2876 \| 0.3814 \| 0.1742 \| 0.4864 \| 0.4725 \| 0.1371 \| 0.3881 \| 0.3302 \| 0.4757 \| 0.1327 \| 0.2803 \| \| 1.2326 \| 8.0 \| 5392 \| 1.1854 \| 0.2271 \| 0.4778 \| 0.1937 \| 0.0831 \| 0.3006 \| 0.3862 \| 0.0957 \| 0.3085 \| 0.4177 \| 0.2162 \| 0.5003 \| 0.6019 \| 0.1627 \| 0.4278 \| 0.3514 \| 0.5103 \| 0.1672 \| 0.315 \| \| 1.2341 \| 9.0 \| 6066 \| 1.1747 \| 0.2402 \| 0.4989 \| 0.2152 \| 0.0883 \| 0.3169 \| 0.3904 \| 0.0998 \| 0.3214 \| 0.4236 \| 0.2155 \| 0.5053 \| 0.6261 \| 0.1607 \| 0.4196 \| 0.3735 \| 0.5206 \| 0.1864 \| 0.3304 \| \| 1.2104 \| 10.0 \| 6740 \| 1.2319 \| 0.2166 \| 0.4601 \| 0.1802 \| 0.0898 \| 0.2835 \| 0.3558 \| 0.092 \| 0.3095 \| 0.4034 \| 0.2244 \| 0.48 \| 0.5565 \| 0.1428 \| 0.4141 \| 0.3491 \| 0.5038 \| 0.158 \| 0.2922 \| \| 1.2041 \| 11.0 \| 7414 \| 1.1567 \| 0.2454 \| 0.5063 \| 0.2099 \| 0.0949 \| 0.3142 \| 0.3948 \| 0.0997 \| 0.3216 \| 0.4216 \| 0.237 \| 0.5019 \| 0.5881 \| 0.1653 \| 0.4139 \| 0.3698 \| 0.515 \| 0.2012 \| 0.3358 \| \| 1.1603 \| 12.0 \| 8088 \| 1.1399 \| 0.2389 \| 0.4934 \| 0.2012 \| 0.0844 \| 0.3131 \| 0.4267 \| 0.0994 \| 0.3272 \| 0.434 \| 0.2415 \| 0.5103 \| 0.6273 \| 0.1496 \| 0.4389 \| 0.3871 \| 0.5337 \| 0.18 \| 0.3295 \| \| 1.1662 \| 13.0 \| 8762 \| 1.1462 \| 0.2508 \| 0.5201 \| 0.2152 \| 0.1004 \| 0.3212 \| 0.4309 \| 0.1039 \| 0.3274 \| 0.432 \| 0.2385 \| 0.5088 \| 0.6173 \| 0.1727 \| 0.4271 \| 0.3674 \| 0.5271 \| 0.2124 \| 0.3419 \| \| 1.17 \| 14.0 \| 9436 \| 1.1814 \| 0.2356 \| 0.4893 \| 0.2009 \| 0.0751 \| 0.3165 \| 0.4019 \| 0.0988 \| 0.3158 \| 0.4128 \| 0.1977 \| 0.5091 \| 0.581 \| 0.1631 \| 0.4266 \| 0.3716 \| 0.5052 \| 0.172 \| 0.3067 \| \| 1.1494 \| 15.0 \| 10110 \| 1.1660 \| 0.2296 \| 0.4782 \| 0.1998 \| 0.0744 \| 0.2922 \| 0.4074 \| 0.1022 \| 0.3051 \| 0.4049 \| 0.1996 \| 0.4834 \| 0.5917 \| 0.1749 \| 0.4223 \| 0.3565 \| 0.4912 \| 0.1575 \| 0.3012 \| \| 1.1326 \| 16.0 \| 10784 \| 1.1720 \| 0.2421 \| 0.4943 \| 0.2137 \| 0.0865 \| 0.3114 \| 0.4159 \| 0.1035 \| 0.3222 \| 0.4235 \| 0.2065 \| 0.5105 \| 0.6135 \| 0.1738 \| 0.438 \| 0.3649 \| 0.5101 \| 0.1874 \| 0.3223 \| \| 1.1311 \| 17.0 \| 11458 \| 1.1736 \| 0.2324 \| 0.4753 \| 0.2005 \| 0.1042 \| 0.2986 \| 0.3579 \| 0.097 \| 0.3132 \| 0.4198 \| 0.2203 \| 0.5024 \| 0.5958 \| 0.1657 \| 0.4223 \| 0.338 \| 0.5052 \| 0.1936 \| 0.3319 \| \| 1.1184 \| 18.0 \| 12132 \| 1.1249 \| 0.2552 \| 0.5162 \| 0.2308 \| 0.1071 \| 0.318 \| 0.412 \| 0.1036 \| 0.3329 \| 0.4394 \| 0.2474 \| 0.5142 \| 0.6152 \| 0.1678 \| 0.4308 \| 0.3912 \| 0.5381 \| 0.2066 \| 0.3493 \| \| 1.12 \| 19.0 \| 12806 \| 1.1269 \| 0.259 \| 0.518 \| 0.2346 \| 0.1004 \| 0.3339 \| 0.426 \| 0.1034 \| 0.3352 \| 0.4395 \| 0.2364 \| 0.5295 \| 0.6315 \| 0.1738 \| 0.4404 \| 0.3945 \| 0.5386 \| 0.2088 \| 0.3396 \| \| 1.1243 \| 20.0 \| 13480 \| 1.1313 \| 0.2379 \| 0.5002 \| 0.1982 \| 0.1056 \| 0.3004 \| 0.3883 \| 0.0984 \| 0.3176 \| 0.4421 \| 0.273 \| 0.5127 \| 0.6014 \| 0.1418 \| 0.4307 \| 0.3832 \| 0.5523 \| 0.1887 \| 0.3433 \| \| 1.1015 \| 21.0 \| 14154 \| 1.1511 \| 0.2454 \| 0.5096 \| 0.2091 \| 0.1018 \| 0.3132 \| 0.3902 \| 0.1015 \| 0.3228 \| 0.4279 \| 0.242 \| 0.5172 \| 0.5663 \| 0.167 \| 0.435 \| 0.3837 \| 0.5237 \| 0.1855 \| 0.3251 \| \| 1.0967 \| 22.0 \| 14828 \| 1.1409 \| 0.2503 \| 0.5218 \| 0.2146 \| 0.11 \| 0.3064 \| 0.3983 \| 0.1038 \| 0.3227 \| 0.4392 \| 0.2592 \| 0.5007 \| 0.5895 \| 0.1586 \| 0.4338 \| 0.3879 \| 0.548 \| 0.2044 \| 0.3358 \| \| 1.0997 \| 23.0 \| 15502 \| 1.1347 \| 0.2475 \| 0.5142 \| 0.2037 \| 0.0897 \| 0.3175 \| 0.4267 \| 0.1053 \| 0.3245 \| 0.4281 \| 0.2301 \| 0.5028 \| 0.6142 \| 0.1533 \| 0.4098 \| 0.402 \| 0.5404 \| 0.1874 \| 0.3342 \| \| 1.0982 \| 24.0 \| 16176 \| 1.1326 \| 0.2603 \| 0.5254 \| 0.2328 \| 0.1091 \| 0.3282 \| 0.4279 \| 0.1088 \| 0.3287 \| 0.4347 \| 0.2373 \| 0.5205 \| 0.6112 \| 0.1577 \| 0.4108 \| 0.416 \| 0.548 \| 0.2071 \| 0.3452 \| \| 1.0832 \| 25.0 \| 16850 \| 1.1195 \| 0.2552 \| 0.5257 \| 0.214 \| 0.0971 \| 0.3179 \| 0.4274 \| 0.1047 \| 0.3254 \| 0.4318 \| 0.244 \| 0.5058 \| 0.6107 \| 0.1775 \| 0.4359 \| 0.3881 \| 0.5234 \| 0.1999 \| 0.336 \| \| 1.0823 \| 26.0 \| 17524 \| 1.1438 \| 0.2553 \| 0.5247 \| 0.2186 \| 0.0956 \| 0.3215 \| 0.4308 \| 0.1031 \| 0.3244 \| 0.4269 \| 0.2346 \| 0.4868 \| 0.6238 \| 0.1749 \| 0.4181 \| 0.4047 \| 0.5369 \| 0.1863 \| 0.3255 \| \| 1.0821 \| 27.0 \| 18198 \| 1.0968 \| 0.2709 \| 0.5451 \| 0.2287 \| 0.1078 \| 0.3398 \| 0.4644 \| 0.1124 \| 0.3393 \| 0.4453 \| 0.2423 \| 0.5328 \| 0.6554 \| 0.1925 \| 0.4444 \| 0.4101 \| 0.5461 \| 0.2101 \| 0.3455 \| \| 1.0744 \| 28.0 \| 18872 \| 1.0950 \| 0.2535 \| 0.5186 \| 0.2156 \| 0.0946 \| 0.3197 \| 0.4518 \| 0.1074 \| 0.3273 \| 0.4394 \| 0.2249 \| 0.5239 \| 0.6597 \| 0.1655 \| 0.4375 \| 0.3885 \| 0.5345 \| 0.2066 \| 0.3461 \| \| 1.0572 \| 29.0 \| 19546 \| 1.0748 \| 0.2704 \| 0.5723 \| 0.2239 \| 0.117 \| 0.3371 \| 0.4488 \| 0.1077 \| 0.3343 \| 0.4451 \| 0.2645 \| 0.5111 \| 0.6237 \| 0.1841 \| 0.4412 \| 0.4019 \| 0.5397 \| 0.2251 \| 0.3544 \| \| 1.0517 \| 30.0 \| 20220 \| 1.0936 \| 0.2707 \| 0.5561 \| 0.22 \| 0.1127 \| 0.346 \| 0.4501 \| 0.1074 \| 0.3336 \| 0.4343 \| 0.2522 \| 0.514 \| 0.6226 \| 0.1764 \| 0.4299 \| 0.4146 \| 0.531 \| 0.2211 \| 0.342 \| \| 1.0287 \| 31.0 \| 20894 \| 1.1122 \| 0.2551 \| 0.5578 \| 0.1931 \| 0.0958 \| 0.3256 \| 0.4326 \| 0.1055 \| 0.314 \| 0.4199 \| 0.2166 \| 0.5026 \| 0.6243 \| 0.175 \| 0.419 \| 0.3732 \| 0.5033 \| 0.217 \| 0.3375 \| \| 1.0528 \| 32.0 \| 21568 \| 1.0791 \| 0.2626 \| 0.5484 \| 0.2227 \| 0.1011 \| 0.3333 \| 0.4451 \| 0.1083 \| 0.3374 \| 0.4389 \| 0.2384 \| 0.5194 \| 0.6338 \| 0.1753 \| 0.4437 \| 0.4161 \| 0.5493 \| 0.1962 \| 0.3237 \| \| 1.0465 \| 33.0 \| 22242 \| 1.1751 \| 0.239 \| 0.486 \| 0.2122 \| 0.0887 \| 0.3104 \| 0.3949 \| 0.0997 \| 0.3183 \| 0.4192 \| 0.2304 \| 0.5026 \| 0.5903 \| 0.1521 \| 0.4148 \| 0.3879 \| 0.5103 \| 0.1769 \| 0.3323 \| \| 1.0499 \| 34.0 \| 22916 \| 1.0786 \| 0.27 \| 0.5536 \| 0.2241 \| 0.1222 \| 0.3381 \| 0.4472 \| 0.1075 \| 0.3362 \| 0.443 \| 0.2441 \| 0.5186 \| 0.623 \| 0.1742 \| 0.4458 \| 0.4103 \| 0.5412 \| 0.2256 \| 0.342 \| \| 1.0382 \| 35.0 \| 23590 \| 1.0990 \| 0.2602 \| 0.548 \| 0.2168 \| 0.117 \| 0.3275 \| 0.4423 \| 0.1062 \| 0.334 \| 0.4356 \| 0.2532 \| 0.514 \| 0.6148 \| 0.1686 \| 0.4328 \| 0.4087 \| 0.5423 \| 0.2034 \| 0.3315 \| \| 1.0273 \| 36.0 \| 24264 \| 1.0926 \| 0.2579 \| 0.531 \| 0.2222 \| 0.0956 \| 0.3419 \| 0.4373 \| 0.1066 \| 0.3281 \| 0.4336 \| 0.2441 \| 0.5092 \| 0.6179 \| 0.164 \| 0.4586 \| 0.4031 \| 0.5199 \| 0.2065 \| 0.3224 \| \| 1.0475 \| 37.0 \| 24938 \| 1.0754 \| 0.2714 \| 0.5592 \| 0.2288 \| 0.1325 \| 0.3444 \| 0.4221 \| 0.108 \| 0.3471 \| 0.4535 \| 0.2888 \| 0.5277 \| 0.5865 \| 0.1639 \| 0.4355 \| 0.4265 \| 0.5631 \| 0.2238 \| 0.3621 \| \| 1.0262 \| 38.0 \| 25612 \| 1.0378 \| 0.2858 \| 0.5729 \| 0.2508 \| 0.1226 \| 0.3592 \| 0.4762 \| 0.1121 \| 0.346 \| 0.4607 \| 0.2832 \| 0.5275 \| 0.6548 \| 0.1827 \| 0.4504 \| 0.4407 \| 0.5652 \| 0.234 \| 0.3663 \| \| 1.0155 \| 39.0 \| 26286 \| 1.0401 \| 0.2859 \| 0.5747 \| 0.2428 \| 0.139 \| 0.3549 \| 0.4554 \| 0.1085 \| 0.3526 \| 0.4676 \| 0.287 \| 0.5385 \| 0.6314 \| 0.1631 \| 0.4501 \| 0.4445 \| 0.5819 \| 0.25 \| 0.3709 \| \| 1.0218 \| 40.0 \| 26960 \| 1.0469 \| 0.2879 \| 0.579 \| 0.2464 \| 0.1251 \| 0.3659 \| 0.4663 \| 0.1097 \| 0.3513 \| 0.4581 \| 0.2504 \| 0.5374 \| 0.6601 \| 0.1818 \| 0.4428 \| 0.4499 \| 0.5784 \| 0.232 \| 0.353 \| \| 1.0164 \| 41.0 \| 27634 \| 1.0488 \| 0.2941 \| 0.5835 \| 0.2629 \| 0.1398 \| 0.3747 \| 0.467 \| 0.1087 \| 0.3535 \| 0.4651 \| 0.2803 \| 0.5441 \| 0.6476 \| 0.1734 \| 0.4346 \| 0.4556 \| 0.5822 \| 0.2532 \| 0.3784 \| \| 1.0253 \| 42.0 \| 28308 \| 1.0604 \| 0.2804 \| 0.564 \| 0.2487 \| 0.1337 \| 0.3414 \| 0.4713 \| 0.1098 \| 0.348 \| 0.4543 \| 0.2721 \| 0.5264 \| 0.6485 \| 0.1778 \| 0.4452 \| 0.4339 \| 0.5596 \| 0.2294 \| 0.3581 \| \| 1.0327 \| 43.0 \| 28982 \| 1.0828 \| 0.279 \| 0.5572 \| 0.2536 \| 0.1319 \| 0.34 \| 0.4657 \| 0.1122 \| 0.3483 \| 0.4495 \| 0.2686 \| 0.5173 \| 0.657 \| 0.1807 \| 0.454 \| 0.4318 \| 0.5477 \| 0.2246 \| 0.3469 \| \| 1.0131 \| 44.0 \| 29656 \| 1.0594 \| 0.286 \| 0.5791 \| 0.251 \| 0.1261 \| 0.3611 \| 0.469 \| 0.1097 \| 0.3506 \| 0.4566 \| 0.2701 \| 0.5418 \| 0.6371 \| 0.1899 \| 0.4445 \| 0.4354 \| 0.5686 \| 0.2326 \| 0.3567 \| \| 1.0171 \| 45.0 \| 30330 \| 1.0614 \| 0.2795 \| 0.5742 \| 0.2456 \| 0.1245 \| 0.3433 \| 0.4644 \| 0.1092 \| 0.3446 \| 0.453 \| 0.2668 \| 0.5302 \| 0.64 \| 0.1724 \| 0.4413 \| 0.4444 \| 0.5652 \| 0.2217 \| 0.3526 \| \| 1.0071 \| 46.0 \| 31004 \| 1.0887 \| 0.2823 \| 0.577 \| 0.2438 \| 0.1307 \| 0.3619 \| 0.4408 \| 0.1067 \| 0.3455 \| 0.4504 \| 0.2721 \| 0.5371 \| 0.6152 \| 0.1776 \| 0.4465 \| 0.4286 \| 0.5431 \| 0.2407 \| 0.3617 \| \| 1.0017 \| 47.0 \| 31678 \| 1.0296 \| 0.3015 \| 0.5872 \| 0.274 \| 0.1392 \| 0.3768 \| 0.4809 \| 0.111 \| 0.3651 \| 0.4698 \| 0.2732 \| 0.5505 \| 0.6646 \| 0.1891 \| 0.4563 \| 0.4573 \| 0.574 \| 0.2582 \| 0.379 \| \| 1.0108 \| 48.0 \| 32352 \| 1.0046 \| 0.3051 \| 0.6009 \| 0.2686 \| 0.1429 \| 0.3709 \| 0.4927 \| 0.1132 \| 0.3652 \| 0.4779 \| 0.2781 \| 0.5475 \| 0.6821 \| 0.1949 \| 0.4569 \| 0.4652 \| 0.5835 \| 0.2552 \| 0.3933 \| \| 0.9953 \| 49.0 \| 33026 \| 1.0458 \| 0.2879 \| 0.5832 \| 0.2558 \| 0.1334 \| 0.36 \| 0.4751 \| 0.1128 \| 0.3509 \| 0.4653 \| 0.2825 \| 0.5324 \| 0.6498 \| 0.1805 \| 0.4487 \| 0.4463 \| 0.5734 \| 0.237 \| 0.374 \| \| 0.988 \| 50.0 \| 33700 \| 1.0375 \| 0.2902 \| 0.5856 \| 0.2503 \| 0.1204 \| 0.3624 \| 0.494 \| 0.1163 \| 0.3502 \| 0.4562 \| 0.2493 \| 0.5332 \| 0.6619 \| 0.1916 \| 0.4489 \| 0.4473 \| 0.5621 \| 0.2316 \| 0.3575 \| \| 0.987 \| 51.0 \| 34374 \| 1.0530 \| 0.283 \| 0.5758 \| 0.239 \| 0.1114 \| 0.3675 \| 0.4677 \| 0.1086 \| 0.3487 \| 0.4554 \| 0.2579 \| 0.55 \| 0.6453 \| 0.1933 \| 0.4568 \| 0.4361 \| 0.5577 \| 0.2198 \| 0.3517 \| \| 0.9847 \| 52.0 \| 35048 \| 1.0420 \| 0.2985 \| 0.6017 \| 0.2538 \| 0.1318 \| 0.3745 \| 0.4696 \| 0.1135 \| 0.3602 \| 0.462 \| 0.269 \| 0.5293 \| 0.6626 \| 0.1892 \| 0.4373 \| 0.4604 \| 0.5771 \| 0.2458 \| 0.3716 \| \| 0.9899 \| 53.0 \| 35722 \| 1.0385 \| 0.2966 \| 0.6054 \| 0.2515 \| 0.1359 \| 0.365 \| 0.4984 \| 0.1129 \| 0.3599 \| 0.4646 \| 0.2667 \| 0.5349 \| 0.6759 \| 0.178 \| 0.4376 \| 0.4631 \| 0.5838 \| 0.2488 \| 0.3722 \| \| 0.9822 \| 54.0 \| 36396 \| 1.0391 \| 0.286 \| 0.5813 \| 0.2451 \| 0.1199 \| 0.3699 \| 0.4542 \| 0.1117 \| 0.3573 \| 0.4638 \| 0.2812 \| 0.5427 \| 0.6336 \| 0.1803 \| 0.4482 \| 0.4456 \| 0.5747 \| 0.2322 \| 0.3685 \| \| 0.9909 \| 55.0 \| 37070 \| 1.0179 \| 0.3014 \| 0.614 \| 0.2664 \| 0.137 \| 0.3692 \| 0.4866 \| 0.1095 \| 0.355 \| 0.4697 \| 0.2856 \| 0.538 \| 0.654 \| 0.1924 \| 0.4494 \| 0.4548 \| 0.5721 \| 0.2572 \| 0.3876 \| \| 0.9824 \| 56.0 \| 37744 \| 1.0204 \| 0.3041 \| 0.6131 \| 0.2713 \| 0.1378 \| 0.3752 \| 0.4993 \| 0.1176 \| 0.3626 \| 0.4726 \| 0.2844 \| 0.5382 \| 0.6799 \| 0.1936 \| 0.4484 \| 0.461 \| 0.5794 \| 0.2578 \| 0.39 \| \| 0.9846 \| 57.0 \| 38418 \| 1.0147 \| 0.3015 \| 0.6003 \| 0.2691 \| 0.1371 \| 0.371 \| 0.506 \| 0.1177 \| 0.3616 \| 0.4654 \| 0.2781 \| 0.534 \| 0.6706 \| 0.2033 \| 0.4593 \| 0.4513 \| 0.5634 \| 0.25 \| 0.3735 \| \| 0.9575 \| 58.0 \| 39092 \| 1.0353 \| 0.2965 \| 0.5951 \| 0.2558 \| 0.1284 \| 0.3816 \| 0.4743 \| 0.1115 \| 0.3576 \| 0.4725 \| 0.296 \| 0.5441 \| 0.658 \| 0.1754 \| 0.4413 \| 0.4674 \| 0.5962 \| 0.2465 \| 0.3801 \| \| 0.9534 \| 59.0 \| 39766 \| 1.0171 \| 0.3013 \| 0.6065 \| 0.2579 \| 0.1376 \| 0.3672 \| 0.4851 \| 0.114 \| 0.3641 \| 0.4706 \| 0.2906 \| 0.5278 \| 0.6724 \| 0.1951 \| 0.4507 \| 0.4612 \| 0.5817 \| 0.2477 \| 0.3795 \| \| 0.9647 \| 60.0 \| 40440 \| 1.0087 \| 0.3039 \| 0.6032 \| 0.2574 \| 0.1397 \| 0.3725 \| 0.4939 \| 0.1142 \| 0.3628 \| 0.4762 \| 0.2996 \| 0.5378 \| 0.6726 \| 0.1811 \| 0.4549 \| 0.4698 \| 0.5779 \| 0.2609 \| 0.3958 \| \| 0.955 \| 61.0 \| 41114 \| 1.0005 \| 0.3042 \| 0.6139 \| 0.2629 \| 0.139 \| 0.3747 \| 0.4977 \| 0.1137 \| 0.3612 \| 0.4819 \| 0.3019 \| 0.5541 \| 0.6822 \| 0.1966 \| 0.4724 \| 0.4632 \| 0.5792 \| 0.2528 \| 0.394 \| \| 0.9578 \| 62.0 \| 41788 \| 1.0119 \| 0.3033 \| 0.5942 \| 0.2689 \| 0.1347 \| 0.3767 \| 0.4916 \| 0.1172 \| 0.3611 \| 0.4738 \| 0.2781 \| 0.5484 \| 0.6538 \| 0.1957 \| 0.4496 \| 0.4595 \| 0.5801 \| 0.2547 \| 0.3918 \| \| 0.9479 \| 63.0 \| 42462 \| 0.9970 \| 0.3071 \| 0.6156 \| 0.2735 \| 0.1444 \| 0.3751 \| 0.5048 \| 0.1148 \| 0.3625 \| 0.4795 \| 0.2921 \| 0.5521 \| 0.6759 \| 0.2043 \| 0.4591 \| 0.4754 \| 0.6064 \| 0.2415 \| 0.373 \| \| 0.9451 \| 64.0 \| 43136 \| 0.9811 \| 0.3188 \| 0.631 \| 0.2818 \| 0.1653 \| 0.3851 \| 0.5075 \| 0.1188 \| 0.3737 \| 0.4914 \| 0.3064 \| 0.5666 \| 0.6819 \| 0.2076 \| 0.4673 \| 0.4899 \| 0.6165 \| 0.2589 \| 0.3903 \| \| 0.9343 \| 65.0 \| 43810 \| 0.9991 \| 0.3086 \| 0.6282 \| 0.2609 \| 0.1535 \| 0.3664 \| 0.4856 \| 0.1166 \| 0.3672 \| 0.4778 \| 0.2992 \| 0.5346 \| 0.666 \| 0.1954 \| 0.451 \| 0.4667 \| 0.5866 \| 0.2636 \| 0.3959 \| \| 0.9376 \| 66.0 \| 44484 \| 1.0005 \| 0.298 \| 0.6142 \| 0.2512 \| 0.1428 \| 0.3717 \| 0.4918 \| 0.1119 \| 0.3572 \| 0.4675 \| 0.2789 \| 0.5377 \| 0.6779 \| 0.1863 \| 0.4488 \| 0.4535 \| 0.5673 \| 0.2544 \| 0.3865 \| \| 0.9576 \| 67.0 \| 45158 \| 1.0466 \| 0.2933 \| 0.5895 \| 0.256 \| 0.1229 \| 0.374 \| 0.4706 \| 0.1142 \| 0.3558 \| 0.462 \| 0.2572 \| 0.5465 \| 0.6613 \| 0.2047 \| 0.4525 \| 0.4428 \| 0.5714 \| 0.2324 \| 0.3622 \| \| 0.9431 \| 68.0 \| 45832 \| 1.0323 \| 0.3069 \| 0.6177 \| 0.2644 \| 0.1538 \| 0.3787 \| 0.4848 \| 0.1148 \| 0.3627 \| 0.4683 \| 0.2848 \| 0.5395 \| 0.6538 \| 0.1913 \| 0.4484 \| 0.4545 \| 0.5606 \| 0.275 \| 0.3958 \| \| 0.9422 \| 69.0 \| 46506 \| 1.0326 \| 0.2959 \| 0.6173 \| 0.2394 \| 0.1413 \| 0.3551 \| 0.4762 \| 0.1125 \| 0.3596 \| 0.4676 \| 0.29 \| 0.5364 \| 0.6402 \| 0.1948 \| 0.458 \| 0.4394 \| 0.56 \| 0.2536 \| 0.3847 \| \| 0.9305 \| 70.0 \| 47180 \| 1.0143 \| 0.3075 \| 0.6141 \| 0.2725 \| 0.13 \| 0.381 \| 0.5014 \| 0.117 \| 0.3672 \| 0.4777 \| 0.2862 \| 0.5588 \| 0.6698 \| 0.2209 \| 0.4712 \| 0.4692 \| 0.5953 \| 0.2325 \| 0.3665 \| \| 0.9441 \| 71.0 \| 47854 \| 1.0367 \| 0.306 \| 0.615 \| 0.2636 \| 0.129 \| 0.381 \| 0.5 \| 0.1152 \| 0.3621 \| 0.467 \| 0.2636 \| 0.5479 \| 0.6694 \| 0.203 \| 0.4461 \| 0.4705 \| 0.5917 \| 0.2446 \| 0.3632 \| \| 0.9395 \| 72.0 \| 48528 \| 1.0061 \| 0.3082 \| 0.6214 \| 0.2676 \| 0.1601 \| 0.3709 \| 0.4941 \| 0.1118 \| 0.3664 \| 0.4769 \| 0.3152 \| 0.5422 \| 0.6463 \| 0.1871 \| 0.4447 \| 0.4611 \| 0.5833 \| 0.2764 \| 0.4027 \| \| 0.9271 \| 73.0 \| 49202 \| 0.9797 \| 0.3129 \| 0.6364 \| 0.2697 \| 0.1432 \| 0.378 \| 0.4921 \| 0.1185 \| 0.3713 \| 0.4883 \| 0.305 \| 0.5545 \| 0.6736 \| 0.2148 \| 0.4801 \| 0.4565 \| 0.5894 \| 0.2675 \| 0.3953 \| \| 0.9239 \| 74.0 \| 49876 \| 0.9843 \| 0.3237 \| 0.6409 \| 0.2844 \| 0.1558 \| 0.3953 \| 0.4989 \| 0.1209 \| 0.3791 \| 0.4926 \| 0.3094 \| 0.5589 \| 0.6721 \| 0.2118 \| 0.4772 \| 0.4719 \| 0.5843 \| 0.2874 \| 0.4162 \| \| 0.9236 \| 75.0 \| 50550 \| 0.9649 \| 0.3312 \| 0.6391 \| 0.3052 \| 0.1805 \| 0.397 \| 0.5042 \| 0.1225 \| 0.3792 \| 0.4966 \| 0.3223 \| 0.5652 \| 0.666 \| 0.2222 \| 0.481 \| 0.489 \| 0.5977 \| 0.2824 \| 0.4112 \| \| 0.9191 \| 76.0 \| 51224 \| 0.9947 \| 0.3198 \| 0.624 \| 0.2893 \| 0.1705 \| 0.3875 \| 0.4904 \| 0.1159 \| 0.3727 \| 0.4956 \| 0.3186 \| 0.5633 \| 0.6799 \| 0.2021 \| 0.4807 \| 0.4768 \| 0.591 \| 0.2805 \| 0.4149 \| \| 0.9025 \| 77.0 \| 51898 \| 1.0002 \| 0.3125 \| 0.6273 \| 0.2792 \| 0.152 \| 0.3829 \| 0.5067 \| 0.1155 \| 0.3671 \| 0.4826 \| 0.296 \| 0.5487 \| 0.6858 \| 0.1979 \| 0.4706 \| 0.4724 \| 0.5828 \| 0.2673 \| 0.3944 \| \| 0.9002 \| 78.0 \| 52572 \| 0.9713 \| 0.3269 \| 0.6441 \| 0.291 \| 0.1672 \| 0.3953 \| 0.5014 \| 0.1174 \| 0.3806 \| 0.4862 \| 0.2991 \| 0.5549 \| 0.6714 \| 0.2061 \| 0.4607 \| 0.4896 \| 0.5931 \| 0.285 \| 0.4048 \| \| 0.9161 \| 79.0 \| 53246 \| 0.9722 \| 0.3257 \| 0.6387 \| 0.2961 \| 0.1583 \| 0.4061 \| 0.4885 \| 0.1186 \| 0.3795 \| 0.4885 \| 0.3039 \| 0.5638 \| 0.6599 \| 0.205 \| 0.4591 \| 0.4971 \| 0.602 \| 0.275 \| 0.4044 \| \| 0.8976 \| 80.0 \| 53920 \| 0.9792 \| 0.3129 \| 0.6328 \| 0.2735 \| 0.1502 \| 0.3805 \| 0.4845 \| 0.1189 \| 0.3692 \| 0.4815 \| 0.3082 \| 0.5371 \| 0.6575 \| 0.2097 \| 0.462 \| 0.458 \| 0.5721 \| 0.271 \| 0.4105 \| \| 0.8937 \| 81.0 \| 54594 \| 0.9829 \| 0.3193 \| 0.6462 \| 0.287 \| 0.166 \| 0.3862 \| 0.4792 \| 0.1193 \| 0.3736 \| 0.4833 \| 0.3041 \| 0.5409 \| 0.6557 \| 0.205 \| 0.4603 \| 0.4706 \| 0.5853 \| 0.2823 \| 0.4043 \| \| 0.9052 \| 82.0 \| 55268 \| 0.9866 \| 0.328 \| 0.6347 \| 0.3024 \| 0.1747 \| 0.397 \| 0.4929 \| 0.1201 \| 0.375 \| 0.492 \| 0.3141 \| 0.5529 \| 0.6774 \| 0.2116 \| 0.4759 \| 0.4934 \| 0.5951 \| 0.2791 \| 0.405 \| \| 0.8972 \| 83.0 \| 55942 \| 0.9669 \| 0.3278 \| 0.6458 \| 0.2873 \| 0.1753 \| 0.399 \| 0.507 \| 0.1198 \| 0.3789 \| 0.4941 \| 0.3131 \| 0.5639 \| 0.6744 \| 0.2186 \| 0.481 \| 0.4884 \| 0.5974 \| 0.2766 \| 0.4039 \| \| 0.8934 \| 84.0 \| 56616 \| 0.9701 \| 0.323 \| 0.6436 \| 0.2876 \| 0.1568 \| 0.4018 \| 0.4937 \| 0.1203 \| 0.3776 \| 0.4865 \| 0.2984 \| 0.5618 \| 0.6769 \| 0.2167 \| 0.4705 \| 0.485 \| 0.599 \| 0.2674 \| 0.3901 \| \| 0.8985 \| 85.0 \| 57290 \| 0.9505 \| 0.3303 \| 0.6579 \| 0.29 \| 0.1764 \| 0.4045 \| 0.5082 \| 0.1221 \| 0.382 \| 0.4993 \| 0.3313 \| 0.5661 \| 0.684 \| 0.2114 \| 0.4853 \| 0.4987 \| 0.6056 \| 0.2808 \| 0.4069 \| \| 0.8829 \| 86.0 \| 57964 \| 0.9648 \| 0.3164 \| 0.6437 \| 0.2728 \| 0.1558 \| 0.3957 \| 0.4941 \| 0.1207 \| 0.3756 \| 0.4869 \| 0.3155 \| 0.5485 \| 0.6746 \| 0.216 \| 0.474 \| 0.4564 \| 0.5753 \| 0.2766 \| 0.4113 \| \| 0.8992 \| 87.0 \| 58638 \| 1.0045 \| 0.3058 \| 0.6105 \| 0.2732 \| 0.1346 \| 0.3941 \| 0.502 \| 0.1182 \| 0.3658 \| 0.4697 \| 0.2734 \| 0.5427 \| 0.6796 \| 0.1981 \| 0.454 \| 0.4597 \| 0.5773 \| 0.2596 \| 0.3778 \| \| 0.892 \| 88.0 \| 59312 \| 0.9759 \| 0.3218 \| 0.6415 \| 0.2847 \| 0.1782 \| 0.3844 \| 0.4847 \| 0.1169 \| 0.3754 \| 0.4861 \| 0.3259 \| 0.5415 \| 0.6484 \| 0.2071 \| 0.4643 \| 0.4796 \| 0.5873 \| 0.2787 \| 0.4068 \| \| 0.8904 \| 89.0 \| 59986 \| 0.9567 \| 0.3394 \| 0.6613 \| 0.3054 \| 0.1847 \| 0.4101 \| 0.494 \| 0.1215 \| 0.3884 \| 0.4955 \| 0.3268 \| 0.5552 \| 0.6596 \| 0.2218 \| 0.4668 \| 0.5035 \| 0.6069 \| 0.293 \| 0.4129 \| \| 0.8864 \| 90.0 \| 60660 \| 0.9877 \| 0.3248 \| 0.641 \| 0.2916 \| 0.1694 \| 0.3945 \| 0.4758 \| 0.1192 \| 0.3788 \| 0.4838 \| 0.2952 \| 0.5492 \| 0.6629 \| 0.212 \| 0.4621 \| 0.4807 \| 0.5912 \| 0.2818 \| 0.3981 \| \| 0.8781 \| 91.0 \| 61334 \| 0.9849 \| 0.3261 \| 0.6402 \| 0.2899 \| 0.1761 \| 0.4013 \| 0.4945 \| 0.1178 \| 0.3812 \| 0.4864 \| 0.3098 \| 0.5569 \| 0.6573 \| 0.2094 \| 0.4653 \| 0.491 \| 0.5913 \| 0.2778 \| 0.4026 \| \| 0.8904 \| 92.0 \| 62008 \| 0.9475 \| 0.3312 \| 0.6614 \| 0.2863 \| 0.1787 \| 0.3999 \| 0.5045 \| 0.119 \| 0.3828 \| 0.4958 \| 0.3147 \| 0.5539 \| 0.6744 \| 0.2215 \| 0.48 \| 0.4827 \| 0.5971 \| 0.2892 \| 0.4105 \| \| 0.8783 \| 93.0 \| 62682 \| 0.9780 \| 0.3273 \| 0.6475 \| 0.2883 \| 0.1683 \| 0.3992 \| 0.4984 \| 0.1206 \| 0.3815 \| 0.4892 \| 0.3067 \| 0.5612 \| 0.6683 \| 0.2066 \| 0.4677 \| 0.4963 \| 0.6042 \| 0.2789 \| 0.3957 \| \| 0.8868 \| 94.0 \| 63356 \| 0.9746 \| 0.3174 \| 0.6521 \| 0.2671 \| 0.1616 \| 0.3947 \| 0.497 \| 0.1175 \| 0.3728 \| 0.4736 \| 0.2928 \| 0.5429 \| 0.6784 \| 0.1966 \| 0.4493 \| 0.4861 \| 0.5936 \| 0.2694 \| 0.3778 \| \| 0.875 \| 95.0 \| 64030 \| 0.9725 \| 0.3269 \| 0.6426 \| 0.2893 \| 0.1774 \| 0.3963 \| 0.4761 \| 0.116 \| 0.3774 \| 0.4863 \| 0.303 \| 0.5487 \| 0.6627 \| 0.1941 \| 0.4545 \| 0.4957 \| 0.5964 \| 0.2909 \| 0.4079 \| \| 0.8652 \| 96.0 \| 64704 \| 0.9415 \| 0.337 \| 0.6644 \| 0.3015 \| 0.1829 \| 0.41 \| 0.5059 \| 0.12 \| 0.3925 \| 0.497 \| 0.3167 \| 0.5691 \| 0.676 \| 0.2253 \| 0.4811 \| 0.5043 \| 0.6047 \| 0.2815 \| 0.405 \| \| 0.857 \| 97.0 \| 65378 \| 0.9420 \| 0.3391 \| 0.671 \| 0.2965 \| 0.1913 \| 0.4082 \| 0.5038 \| 0.1183 \| 0.3907 \| 0.4944 \| 0.3169 \| 0.5608 \| 0.6833 \| 0.2217 \| 0.4691 \| 0.5028 \| 0.6008 \| 0.2927 \| 0.4132 \| \| 0.8668 \| 98.0 \| 66052 \| 0.9563 \| 0.3344 \| 0.6578 \| 0.2896 \| 0.1764 \| 0.4059 \| 0.4994 \| 0.119 \| 0.3849 \| 0.4944 \| 0.3103 \| 0.564 \| 0.6725 \| 0.2138 \| 0.4641 \| 0.493 \| 0.6013 \| 0.2963 \| 0.4178 \| \| 0.8671 \| 99.0 \| 66726 \| 0.9706 \| 0.3213 \| 0.6491 \| 0.2762 \| 0.1767 \| 0.3852 \| 0.5062 \| 0.1146 \| 0.3801 \| 0.4858 \| 0.3316 \| 0.5428 \| 0.6644 \| 0.1875 \| 0.4527 \| 0.4843 \| 0.5925 \| 0.2921 \| 0.4122 \| \| 0.8659 \| 100.0 \| 67400 \| 0.9395 \| 0.3433 \| 0.6648 \| 0.3058 \| 0.1904 \| 0.4175 \| 0.5218 \| 0.1219 \| 0.3938 \| 0.5023 \| 0.3281 \| 0.5733 \| 0.6826 \| 0.2194 \| 0.4728 \| 0.5166 \| 0.6155 \| 0.294 \| 0.4187 \| \| 0.8529 \| 101.0 \| 68074 \| 0.9567 \| 0.3379 \| 0.6574 \| 0.2999 \| 0.1795 \| 0.4112 \| 0.5067 \| 0.1203 \| 0.3837 \| 0.4982 \| 0.3237 \| 0.5676 \| 0.6709 \| 0.2113 \| 0.4636 \| 0.507 \| 0.6129 \| 0.2955 \| 0.418 \| \| 0.8539 \| 102.0 \| 68748 \| 0.9393 \| 0.342 \| 0.671 \| 0.2987 \| 0.1892 \| 0.4127 \| 0.5174 \| 0.1237 \| 0.39 \| 0.5016 \| 0.3248 \| 0.5696 \| 0.6834 \| 0.2155 \| 0.4684 \| 0.5162 \| 0.6145 \| 0.2941 \| 0.422 \| \| 0.8419 \| 103.0 \| 69422 \| 0.9411 \| 0.3433 \| 0.6691 \| 0.3064 \| 0.1895 \| 0.4056 \| 0.5045 \| 0.1204 \| 0.3904 \| 0.4993 \| 0.3258 \| 0.5585 \| 0.6776 \| 0.2156 \| 0.466 \| 0.5161 \| 0.6185 \| 0.2983 \| 0.4134 \| \| 0.8423 \| 104.0 \| 70096 \| 0.9401 \| 0.3462 \| 0.6765 \| 0.3175 \| 0.2003 \| 0.4128 \| 0.5127 \| 0.1215 \| 0.3925 \| 0.5013 \| 0.3409 \| 0.5576 \| 0.6768 \| 0.2206 \| 0.4736 \| 0.5086 \| 0.6039 \| 0.3096 \| 0.4263 \| \| 0.8428 \| 105.0 \| 70770 \| 0.9328 \| 0.3399 \| 0.6628 \| 0.2988 \| 0.1781 \| 0.4138 \| 0.5098 \| 0.1223 \| 0.3879 \| 0.498 \| 0.3273 \| 0.5627 \| 0.6661 \| 0.2248 \| 0.4817 \| 0.4986 \| 0.6008 \| 0.2962 \| 0.4116 \| \| 0.8431 \| 106.0 \| 71444 \| 0.9601 \| 0.3356 \| 0.6542 \| 0.3056 \| 0.1798 \| 0.4043 \| 0.4963 \| 0.1203 \| 0.385 \| 0.4944 \| 0.3128 \| 0.5512 \| 0.6908 \| 0.2008 \| 0.4548 \| 0.5057 \| 0.6147 \| 0.3003 \| 0.4137 \| \| 0.841 \| 107.0 \| 72118 \| 0.9473 \| 0.3371 \| 0.6537 \| 0.2891 \| 0.1764 \| 0.407 \| 0.534 \| 0.1239 \| 0.3885 \| 0.4983 \| 0.3059 \| 0.5687 \| 0.6988 \| 0.2091 \| 0.4691 \| 0.5072 \| 0.6121 \| 0.2951 \| 0.4136 \| \| 0.8371 \| 108.0 \| 72792 \| 0.9739 \| 0.3416 \| 0.6564 \| 0.3032 \| 0.1892 \| 0.4142 \| 0.509 \| 0.1229 \| 0.3927 \| 0.4974 \| 0.3211 \| 0.5685 \| 0.6692 \| 0.2085 \| 0.4636 \| 0.5161 \| 0.6155 \| 0.3002 \| 0.413 \| \| 0.843 \| 109.0 \| 73466 \| 0.9271 \| 0.3472 \| 0.6755 \| 0.3147 \| 0.204 \| 0.4107 \| 0.5176 \| 0.1226 \| 0.3944 \| 0.5048 \| 0.3402 \| 0.5614 \| 0.6771 \| 0.2184 \| 0.4894 \| 0.5158 \| 0.6062 \| 0.3074 \| 0.4187 \| \| 0.8392 \| 110.0 \| 74140 \| 0.9525 \| 0.3493 \| 0.672 \| 0.3123 \| 0.1894 \| 0.4279 \| 0.5139 \| 0.125 \| 0.3966 \| 0.5024 \| 0.3236 \| 0.5779 \| 0.6804 \| 0.2205 \| 0.4743 \| 0.5259 \| 0.6217 \| 0.3016 \| 0.4111 \| \| 0.8363 \| 111.0 \| 74814 \| 0.9500 \| 0.3372 \| 0.6655 \| 0.2969 \| 0.188 \| 0.4077 \| 0.5044 \| 0.123 \| 0.387 \| 0.4988 \| 0.3337 \| 0.5579 \| 0.6861 \| 0.2169 \| 0.4801 \| 0.5005 \| 0.6067 \| 0.2943 \| 0.4095 \| \| 0.8327 \| 112.0 \| 75488 \| 0.9441 \| 0.3476 \| 0.6802 \| 0.3042 \| 0.1958 \| 0.4161 \| 0.5233 \| 0.1214 \| 0.3935 \| 0.5041 \| 0.3348 \| 0.5717 \| 0.6777 \| 0.2349 \| 0.4849 \| 0.5061 \| 0.6109 \| 0.3019 \| 0.4165 \| \| 0.8383 \| 113.0 \| 76162 \| 0.9553 \| 0.3384 \| 0.6695 \| 0.3031 \| 0.1759 \| 0.4156 \| 0.5094 \| 0.1235 \| 0.3867 \| 0.4916 \| 0.3118 \| 0.5633 \| 0.6824 \| 0.218 \| 0.4643 \| 0.5271 \| 0.6271 \| 0.2702 \| 0.3833 \| \| 0.8289 \| 114.0 \| 76836 \| 0.9386 \| 0.3513 \| 0.6849 \| 0.317 \| 0.1902 \| 0.4215 \| 0.5248 \| 0.1255 \| 0.3975 \| 0.5078 \| 0.3418 \| 0.565 \| 0.696 \| 0.2302 \| 0.4829 \| 0.5207 \| 0.6266 \| 0.303 \| 0.4138 \| \| 0.8277 \| 115.0 \| 77510 \| 0.9126 \| 0.3588 \| 0.6899 \| 0.3214 \| 0.2084 \| 0.417 \| 0.5419 \| 0.1296 \| 0.3984 \| 0.5136 \| 0.3474 \| 0.5653 \| 0.7002 \| 0.2294 \| 0.4744 \| 0.5313 \| 0.6307 \| 0.3157 \| 0.4356 \| \| 0.8258 \| 116.0 \| 78184 \| 0.9158 \| 0.3468 \| 0.6842 \| 0.3006 \| 0.2008 \| 0.413 \| 0.5166 \| 0.1225 \| 0.3942 \| 0.5092 \| 0.349 \| 0.5687 \| 0.6854 \| 0.2222 \| 0.4852 \| 0.5146 \| 0.6237 \| 0.3037 \| 0.4189 \| \| 0.8254 \| 117.0 \| 78858 \| 0.9784 \| 0.3367 \| 0.6674 \| 0.2911 \| 0.1807 \| 0.408 \| 0.5215 \| 0.122 \| 0.3862 \| 0.4903 \| 0.3199 \| 0.5604 \| 0.6789 \| 0.2159 \| 0.4644 \| 0.4992 \| 0.6005 \| 0.295 \| 0.4061 \| \| 0.8227 \| 118.0 \| 79532 \| 0.9196 \| 0.3577 \| 0.6909 \| 0.3203 \| 0.2076 \| 0.4175 \| 0.5176 \| 0.1251 \| 0.401 \| 0.5189 \| 0.3517 \| 0.5745 \| 0.6897 \| 0.2276 \| 0.4797 \| 0.5282 \| 0.6343 \| 0.3174 \| 0.4426 \| \| 0.815 \| 119.0 \| 80206 \| 0.9294 \| 0.3503 \| 0.6839 \| 0.3158 \| 0.1935 \| 0.4198 \| 0.508 \| 0.1238 \| 0.3932 \| 0.5051 \| 0.3339 \| 0.5705 \| 0.6839 \| 0.2249 \| 0.4702 \| 0.5143 \| 0.6149 \| 0.3118 \| 0.4302 \| \| 0.8157 \| 120.0 \| 80880 \| 0.9266 \| 0.3496 \| 0.6771 \| 0.3085 \| 0.192 \| 0.416 \| 0.5157 \| 0.1233 \| 0.3969 \| 0.5065 \| 0.3455 \| 0.5631 \| 0.6867 \| 0.226 \| 0.4812 \| 0.5113 \| 0.6044 \| 0.3115 \| 0.4339 \| \| 0.803 \| 121.0 \| 81554 \| 0.9207 \| 0.3522 \| 0.6994 \| 0.2967 \| 0.212 \| 0.4152 \| 0.5082 \| 0.1229 \| 0.3919 \| 0.5107 \| 0.3581 \| 0.5669 \| 0.6635 \| 0.2353 \| 0.4873 \| 0.5013 \| 0.6046 \| 0.3199 \| 0.4402 \| \| 0.8069 \| 122.0 \| 82228 \| 0.9433 \| 0.3413 \| 0.6782 \| 0.2963 \| 0.1838 \| 0.4043 \| 0.501 \| 0.125 \| 0.3863 \| 0.4943 \| 0.3196 \| 0.5574 \| 0.6757 \| 0.2246 \| 0.4778 \| 0.5057 \| 0.6072 \| 0.2935 \| 0.3979 \| \| 0.8027 \| 123.0 \| 82902 \| 0.9324 \| 0.3567 \| 0.6809 \| 0.3193 \| 0.2019 \| 0.4193 \| 0.5365 \| 0.1284 \| 0.4005 \| 0.5096 \| 0.3597 \| 0.5663 \| 0.69 \| 0.2201 \| 0.4698 \| 0.5288 \| 0.6263 \| 0.321 \| 0.4327 \| \| 0.7997 \| 124.0 \| 83576 \| 0.9269 \| 0.3514 \| 0.6773 \| 0.3127 \| 0.1931 \| 0.4206 \| 0.5108 \| 0.1267 \| 0.3972 \| 0.5094 \| 0.3444 \| 0.5798 \| 0.6852 \| 0.2339 \| 0.4863 \| 0.5202 \| 0.625 \| 0.3003 \| 0.4169 \| \| 0.7842 \| 125.0 \| 84250 \| 0.9350 \| 0.3533 \| 0.6834 \| 0.311 \| 0.2027 \| 0.4267 \| 0.5216 \| 0.1249 \| 0.396 \| 0.5069 \| 0.3302 \| 0.5708 \| 0.6897 \| 0.2251 \| 0.4734 \| 0.5137 \| 0.6136 \| 0.3212 \| 0.4338 \| \| 0.7911 \| 126.0 \| 84924 \| 0.9341 \| 0.3415 \| 0.6854 \| 0.2923 \| 0.1878 \| 0.4115 \| 0.5173 \| 0.1213 \| 0.389 \| 0.5017 \| 0.3348 \| 0.5708 \| 0.6804 \| 0.2212 \| 0.4755 \| 0.5044 \| 0.6113 \| 0.2988 \| 0.4183 \| \| 0.795 \| 127.0 \| 85598 \| 0.9290 \| 0.3521 \| 0.6987 \| 0.3081 \| 0.2037 \| 0.4196 \| 0.5203 \| 0.1243 \| 0.3965 \| 0.5027 \| 0.3448 \| 0.5608 \| 0.673 \| 0.2279 \| 0.4791 \| 0.5088 \| 0.599 \| 0.3196 \| 0.4301 \| \| 0.7913 \| 128.0 \| 86272 \| 0.9014 \| 0.3665 \| 0.7039 \| 0.3274 \| 0.2171 \| 0.434 \| 0.5217 \| 0.1245 \| 0.4073 \| 0.5171 \| 0.3538 \| 0.5804 \| 0.6872 \| 0.2441 \| 0.4885 \| 0.5227 \| 0.6203 \| 0.3327 \| 0.4425 \| \| 0.8024 \| 129.0 \| 86946 \| 0.9343 \| 0.3526 \| 0.6873 \| 0.3128 \| 0.2008 \| 0.4171 \| 0.517 \| 0.124 \| 0.3944 \| 0.5035 \| 0.3368 \| 0.5583 \| 0.6855 \| 0.2363 \| 0.4816 \| 0.5101 \| 0.6067 \| 0.3115 \| 0.4223 \| \| 0.7843 \| 130.0 \| 87620 \| 0.9140 \| 0.3621 \| 0.6958 \| 0.3192 \| 0.2116 \| 0.4287 \| 0.5193 \| 0.1256 \| 0.4057 \| 0.5133 \| 0.3561 \| 0.5713 \| 0.6821 \| 0.2311 \| 0.4804 \| 0.5303 \| 0.6237 \| 0.3249 \| 0.4359 \| \| 0.7838 \| 131.0 \| 88294 \| 0.9106 \| 0.3595 \| 0.6948 \| 0.3128 \| 0.2067 \| 0.4276 \| 0.5219 \| 0.1259 \| 0.4097 \| 0.5186 \| 0.3534 \| 0.5829 \| 0.6998 \| 0.2371 \| 0.4949 \| 0.5243 \| 0.6289 \| 0.3171 \| 0.4319 \| \| 0.7874 \| 132.0 \| 88968 \| 0.9256 \| 0.3548 \| 0.6956 \| 0.3141 \| 0.2083 \| 0.4152 \| 0.5121 \| 0.1208 \| 0.4011 \| 0.504 \| 0.3422 \| 0.5547 \| 0.6818 \| 0.2255 \| 0.4768 \| 0.5211 \| 0.61 \| 0.3178 \| 0.4252 \| \| 0.7815 \| 133.0 \| 89642 \| 0.9369 \| 0.358 \| 0.6973 \| 0.3153 \| 0.2093 \| 0.4241 \| 0.5212 \| 0.1249 \| 0.3996 \| 0.5074 \| 0.3349 \| 0.5688 \| 0.6878 \| 0.2339 \| 0.4744 \| 0.5253 \| 0.6232 \| 0.3149 \| 0.4245 \| \| 0.7787 \| 134.0 \| 90316 \| 0.9380 \| 0.3584 \| 0.6903 \| 0.3202 \| 0.2172 \| 0.4207 \| 0.5172 \| 0.1209 \| 0.4024 \| 0.5127 \| 0.3404 \| 0.5719 \| 0.6907 \| 0.2306 \| 0.4773 \| 0.532 \| 0.6306 \| 0.3124 \| 0.4303 \| \| 0.7758 \| 135.0 \| 90990 \| 0.9247 \| 0.3603 \| 0.6984 \| 0.3199 \| 0.2095 \| 0.431 \| 0.5111 \| 0.1243 \| 0.404 \| 0.5148 \| 0.3493 \| 0.5771 \| 0.6828 \| 0.2328 \| 0.4862 \| 0.5255 \| 0.6281 \| 0.3226 \| 0.4301 \| \| 0.7685 \| 136.0 \| 91664 \| 0.9077 \| 0.3582 \| 0.6927 \| 0.3225 \| 0.2121 \| 0.4284 \| 0.502 \| 0.1249 \| 0.4034 \| 0.5167 \| 0.3515 \| 0.5834 \| 0.6809 \| 0.222 \| 0.4854 \| 0.5355 \| 0.6359 \| 0.3171 \| 0.4288 \| \| 0.7715 \| 137.0 \| 92338 \| 0.9177 \| 0.3589 \| 0.6928 \| 0.3281 \| 0.2065 \| 0.4255 \| 0.5242 \| 0.126 \| 0.4038 \| 0.5112 \| 0.3341 \| 0.5752 \| 0.6956 \| 0.2259 \| 0.4768 \| 0.5313 \| 0.6209 \| 0.3197 \| 0.4359 \| \| 0.7791 \| 138.0 \| 93012 \| 0.9369 \| 0.3584 \| 0.697 \| 0.3212 \| 0.2059 \| 0.4234 \| 0.498 \| 0.125 \| 0.4008 \| 0.5107 \| 0.3362 \| 0.5722 \| 0.6837 \| 0.2253 \| 0.4677 \| 0.5289 \| 0.625 \| 0.3212 \| 0.4395 \| \| 0.7696 \| 139.0 \| 93686 \| 0.9342 \| 0.3528 \| 0.685 \| 0.3189 \| 0.2025 \| 0.4157 \| 0.5091 \| 0.1213 \| 0.3973 \| 0.5084 \| 0.3457 \| 0.5647 \| 0.6786 \| 0.2141 \| 0.4687 \| 0.5204 \| 0.6183 \| 0.324 \| 0.4383 \| \| 0.7708 \| 140.0 \| 94360 \| 0.9159 \| 0.3558 \| 0.6961 \| 0.3299 \| 0.202 \| 0.4249 \| 0.5104 \| 0.1246 \| 0.3996 \| 0.51 \| 0.3354 \| 0.5756 \| 0.6758 \| 0.2243 \| 0.4783 \| 0.5192 \| 0.6203 \| 0.3239 \| 0.4314 \| \| 0.7646 \| 141.0 \| 95034 \| 0.9219 \| 0.3568 \| 0.6901 \| 0.3169 \| 0.2012 \| 0.4194 \| 0.5181 \| 0.1239 \| 0.4025 \| 0.5137 \| 0.3454 \| 0.5737 \| 0.6785 \| 0.2238 \| 0.4807 \| 0.5251 \| 0.6219 \| 0.3217 \| 0.4385 \| \| 0.7674 \| 142.0 \| 95708 \| 0.9096 \| 0.3575 \| 0.6982 \| 0.3154 \| 0.2063 \| 0.4157 \| 0.513 \| 0.127 \| 0.4045 \| 0.5181 \| 0.3504 \| 0.5767 \| 0.677 \| 0.2356 \| 0.491 \| 0.5129 \| 0.6186 \| 0.3239 \| 0.4446 \| \| 0.767 \| 143.0 \| 96382 \| 0.9192 \| 0.3593 \| 0.6997 \| 0.3248 \| 0.2059 \| 0.4301 \| 0.5155 \| 0.1257 \| 0.4044 \| 0.517 \| 0.3481 \| 0.5772 \| 0.6821 \| 0.2281 \| 0.48 \| 0.5251 \| 0.6291 \| 0.3246 \| 0.4419 \| \| 0.7725 \| 144.0 \| 97056 \| 0.9118 \| 0.3645 \| 0.6943 \| 0.3364 \| 0.2289 \| 0.4229 \| 0.5149 \| 0.1268 \| 0.4078 \| 0.5253 \| 0.3698 \| 0.5823 \| 0.6831 \| 0.2382 \| 0.4886 \| 0.5239 \| 0.641 \| 0.3313 \| 0.4463 \| \| 0.7513 \| 145.0 \| 97730 \| 0.9032 \| 0.3582 \| 0.6976 \| 0.3163 \| 0.2137 \| 0.4206 \| 0.5186 \| 0.1253 \| 0.399 \| 0.5188 \| 0.3494 \| 0.5822 \| 0.6808 \| 0.2355 \| 0.4932 \| 0.5171 \| 0.6237 \| 0.3218 \| 0.4396 \| \| 0.7616 \| 146.0 \| 98404 \| 0.9334 \| 0.3589 \| 0.691 \| 0.332 \| 0.2037 \| 0.4251 \| 0.5086 \| 0.1265 \| 0.401 \| 0.5094 \| 0.3381 \| 0.5696 \| 0.6734 \| 0.2339 \| 0.4769 \| 0.516 \| 0.6136 \| 0.3268 \| 0.4378 \| \| 0.7522 \| 147.0 \| 99078 \| 0.9363 \| 0.3411 \| 0.6925 \| 0.2947 \| 0.1824 \| 0.4081 \| 0.5223 \| 0.1222 \| 0.3863 \| 0.4952 \| 0.3158 \| 0.5594 \| 0.6856 \| 0.2294 \| 0.4721 \| 0.5007 \| 0.6042 \| 0.2932 \| 0.4092 \| \| 0.7521 \| 148.0 \| 99752 \| 0.9323 \| 0.353 \| 0.694 \| 0.3218 \| 0.1999 \| 0.4198 \| 0.5247 \| 0.125 \| 0.397 \| 0.5008 \| 0.334 \| 0.5615 \| 0.6902 \| 0.2293 \| 0.4729 \| 0.5317 \| 0.6206 \| 0.2981 \| 0.4088 \| \| 0.7349 \| 149.0 \| 100426 \| 0.9164 \| 0.3652 \| 0.6992 \| 0.3364 \| 0.2146 \| 0.4328 \| 0.5255 \| 0.1274 \| 0.405 \| 0.5128 \| 0.3483 \| 0.5717 \| 0.6951 \| 0.2412 \| 0.4809 \| 0.5344 \| 0.6258 \| 0.3201 \| 0.4316 \| \| 0.7411 \| 150.0 \| 101100 \| 0.9186 \| 0.3618 \| 0.7051 \| 0.3256 \| 0.2195 \| 0.4211 \| 0.5316 \| 0.1263 \| 0.4008 \| 0.5147 \| 0.3425 \| 0.5725 \| 0.699 \| 0.232 \| 0.4831 \| 0.5192 \| 0.623 \| 0.3342 \| 0.4378 \| \| 0.7414 \| 151.0 \| 101774 \| 0.9410 \| 0.3481 \| 0.6967 \| 0.2979 \| 0.2037 \| 0.4108 \| 0.5253 \| 0.1243 \| 0.3935 \| 0.4969 \| 0.3247 \| 0.5573 \| 0.6971 \| 0.2325 \| 0.473 \| 0.5156 \| 0.6139 \| 0.2961 \| 0.4038 \| \| 0.7483 \| 152.0 \| 102448 \| 0.9197 \| 0.36 \| 0.706 \| 0.3219 \| 0.2113 \| 0.4295 \| 0.5198 \| 0.1253 \| 0.4031 \| 0.5052 \| 0.341 \| 0.5733 \| 0.6855 \| 0.2445 \| 0.4826 \| 0.5167 \| 0.6069 \| 0.3189 \| 0.4261 \| \| 0.7479 \| 153.0 \| 103122 \| 0.9263 \| 0.3626 \| 0.7009 \| 0.3281 \| 0.2255 \| 0.4219 \| 0.5232 \| 0.1259 \| 0.3992 \| 0.5126 \| 0.3575 \| 0.5689 \| 0.6931 \| 0.2301 \| 0.4729 \| 0.5335 \| 0.6273 \| 0.3241 \| 0.4377 \| \| 0.7453 \| 154.0 \| 103796 \| 0.9261 \| 0.3472 \| 0.6895 \| 0.306 \| 0.2092 \| 0.4055 \| 0.5186 \| 0.1238 \| 0.395 \| 0.504 \| 0.3423 \| 0.5582 \| 0.6901 \| 0.2379 \| 0.4854 \| 0.5063 \| 0.6042 \| 0.2975 \| 0.4223 \| \| 0.7385 \| 155.0 \| 104470 \| 0.9238 \| 0.3557 \| 0.6999 \| 0.3134 \| 0.2171 \| 0.4286 \| 0.5169 \| 0.1284 \| 0.4038 \| 0.5117 \| 0.3532 \| 0.5763 \| 0.6915 \| 0.2299 \| 0.4867 \| 0.5298 \| 0.6278 \| 0.3075 \| 0.4206 \| \| 0.7455 \| 156.0 \| 105144 \| 0.9065 \| 0.3737 \| 0.7106 \| 0.3506 \| 0.2334 \| 0.4396 \| 0.531 \| 0.1281 \| 0.4133 \| 0.525 \| 0.3686 \| 0.5767 \| 0.6969 \| 0.2379 \| 0.4797 \| 0.5397 \| 0.6422 \| 0.3435 \| 0.4532 \| \| 0.7295 \| 157.0 \| 105818 \| 0.9046 \| 0.368 \| 0.7082 \| 0.3382 \| 0.2258 \| 0.4293 \| 0.5203 \| 0.1279 \| 0.4097 \| 0.5214 \| 0.3654 \| 0.5738 \| 0.689 \| 0.2449 \| 0.4872 \| 0.5266 \| 0.6289 \| 0.3326 \| 0.448 \| \| 0.719 \| 158.0 \| 106492 \| 0.9235 \| 0.3608 \| 0.7025 \| 0.3263 \| 0.2144 \| 0.4319 \| 0.514 \| 0.1241 \| 0.4037 \| 0.5157 \| 0.345 \| 0.5852 \| 0.6885 \| 0.2278 \| 0.4793 \| 0.5307 \| 0.6322 \| 0.3239 \| 0.4354 \| \| 0.7288 \| 159.0 \| 107166 \| 0.9041 \| 0.3665 \| 0.7148 \| 0.3323 \| 0.2195 \| 0.4296 \| 0.5263 \| 0.1257 \| 0.405 \| 0.5189 \| 0.3594 \| 0.5722 \| 0.6949 \| 0.237 \| 0.4821 \| 0.5247 \| 0.6232 \| 0.3377 \| 0.4514 \| \| 0.7212 \| 160.0 \| 107840 \| 0.9100 \| 0.366 \| 0.7096 \| 0.3289 \| 0.2198 \| 0.4288 \| 0.5389 \| 0.1267 \| 0.4079 \| 0.5165 \| 0.3559 \| 0.573 \| 0.6983 \| 0.2419 \| 0.4888 \| 0.5306 \| 0.6245 \| 0.3254 \| 0.436 \| \| 0.7329 \| 161.0 \| 108514 \| 0.9069 \| 0.3618 \| 0.7068 \| 0.3258 \| 0.2165 \| 0.4287 \| 0.5244 \| 0.1251 \| 0.4039 \| 0.5189 \| 0.358 \| 0.5778 \| 0.7006 \| 0.2376 \| 0.489 \| 0.5286 \| 0.626 \| 0.3193 \| 0.4418 \| \| 0.7277 \| 162.0 \| 109188 \| 0.9315 \| 0.3524 \| 0.6896 \| 0.3154 \| 0.2006 \| 0.4185 \| 0.5229 \| 0.1255 \| 0.3975 \| 0.5037 \| 0.3338 \| 0.5609 \| 0.6952 \| 0.2219 \| 0.4696 \| 0.5213 \| 0.6203 \| 0.3139 \| 0.4211 \| \| 0.7259 \| 163.0 \| 109862 \| 0.9106 \| 0.3672 \| 0.7106 \| 0.3346 \| 0.2218 \| 0.4299 \| 0.5228 \| 0.129 \| 0.4121 \| 0.5201 \| 0.3654 \| 0.5769 \| 0.6917 \| 0.2394 \| 0.4902 \| 0.532 \| 0.6258 \| 0.3303 \| 0.4443 \| \| 0.721 \| 164.0 \| 110536 \| 0.9176 \| 0.3631 \| 0.6997 \| 0.326 \| 0.2306 \| 0.4145 \| 0.5214 \| 0.1275 \| 0.4073 \| 0.5187 \| 0.3682 \| 0.5663 \| 0.695 \| 0.2233 \| 0.4734 \| 0.5376 \| 0.6356 \| 0.3283 \| 0.447 \| \| 0.7185 \| 165.0 \| 111210 \| 0.9117 \| 0.3541 \| 0.6993 \| 0.3038 \| 0.2154 \| 0.4139 \| 0.5239 \| 0.125 \| 0.4017 \| 0.5093 \| 0.3535 \| 0.5622 \| 0.688 \| 0.2156 \| 0.4852 \| 0.5266 \| 0.6155 \| 0.3203 \| 0.4273 \| \| 0.7195 \| 166.0 \| 111884 \| 0.9102 \| 0.3624 \| 0.7079 \| 0.3256 \| 0.2167 \| 0.4221 \| 0.526 \| 0.1245 \| 0.4076 \| 0.5142 \| 0.3563 \| 0.571 \| 0.6949 \| 0.2333 \| 0.4828 \| 0.527 \| 0.6154 \| 0.3268 \| 0.4444 \| \| 0.7038 \| 167.0 \| 112558 \| 0.9128 \| 0.3598 \| 0.7 \| 0.3183 \| 0.2203 \| 0.4209 \| 0.5171 \| 0.1227 \| 0.402 \| 0.5156 \| 0.3688 \| 0.5662 \| 0.6775 \| 0.2155 \| 0.4702 \| 0.5313 \| 0.6284 \| 0.3327 \| 0.4481 \| \| 0.7169 \| 168.0 \| 113232 \| 0.9213 \| 0.3699 \| 0.7115 \| 0.3385 \| 0.2137 \| 0.4351 \| 0.5303 \| 0.1265 \| 0.4081 \| 0.5178 \| 0.3618 \| 0.5748 \| 0.6827 \| 0.2378 \| 0.4749 \| 0.5268 \| 0.6265 \| 0.345 \| 0.4519 \| \| 0.7131 \| 169.0 \| 113906 \| 0.8994 \| 0.3711 \| 0.7156 \| 0.3336 \| 0.2216 \| 0.4325 \| 0.5238 \| 0.1243 \| 0.4114 \| 0.5199 \| 0.3716 \| 0.5775 \| 0.6868 \| 0.241 \| 0.4875 \| 0.5336 \| 0.6211 \| 0.3387 \| 0.4512 \| \| 0.7006 \| 170.0 \| 114580 \| 0.9152 \| 0.3638 \| 0.7141 \| 0.3274 \| 0.2249 \| 0.4315 \| 0.5145 \| 0.123 \| 0.404 \| 0.5108 \| 0.3585 \| 0.5702 \| 0.6872 \| 0.2308 \| 0.4729 \| 0.5316 \| 0.6291 \| 0.329 \| 0.4303 \| \| 0.7079 \| 171.0 \| 115254 \| 0.9157 \| 0.3715 \| 0.7098 \| 0.346 \| 0.2179 \| 0.4358 \| 0.534 \| 0.1272 \| 0.41 \| 0.516 \| 0.3513 \| 0.575 \| 0.688 \| 0.229 \| 0.4734 \| 0.5438 \| 0.6271 \| 0.3418 \| 0.4474 \| \| 0.7124 \| 172.0 \| 115928 \| 0.9151 \| 0.3702 \| 0.7079 \| 0.3351 \| 0.2281 \| 0.4318 \| 0.5306 \| 0.1264 \| 0.409 \| 0.5156 \| 0.3583 \| 0.5787 \| 0.6863 \| 0.2326 \| 0.47 \| 0.5402 \| 0.6268 \| 0.3379 \| 0.45 \| \| 0.6943 \| 173.0 \| 116602 \| 0.8994 \| 0.3737 \| 0.7191 \| 0.343 \| 0.2347 \| 0.4341 \| 0.535 \| 0.1257 \| 0.4123 \| 0.5207 \| 0.3651 \| 0.5757 \| 0.6867 \| 0.2324 \| 0.4764 \| 0.5433 \| 0.6299 \| 0.3454 \| 0.4558 \| \| 0.6954 \| 174.0 \| 117276 \| 0.9231 \| 0.3678 \| 0.7175 \| 0.3304 \| 0.2209 \| 0.4322 \| 0.5201 \| 0.1258 \| 0.406 \| 0.5146 \| 0.3529 \| 0.5687 \| 0.6909 \| 0.2324 \| 0.4711 \| 0.535 \| 0.6263 \| 0.3361 \| 0.4463 \| \| 0.7029 \| 175.0 \| 117950 \| 0.9124 \| 0.3681 \| 0.7049 \| 0.3358 \| 0.2149 \| 0.4416 \| 0.5207 \| 0.1277 \| 0.4076 \| 0.5165 \| 0.3455 \| 0.5828 \| 0.6818 \| 0.2336 \| 0.4814 \| 0.5385 \| 0.6217 \| 0.3322 \| 0.4465 \| \| 0.7015 \| 176.0 \| 118624 \| 0.9070 \| 0.3699 \| 0.7113 \| 0.3287 \| 0.2223 \| 0.4385 \| 0.5302 \| 0.1265 \| 0.41 \| 0.5193 \| 0.3567 \| 0.5772 \| 0.688 \| 0.2389 \| 0.4885 \| 0.5392 \| 0.6296 \| 0.3316 \| 0.44 \| \| 0.6986 \| 177.0 \| 119298 \| 0.9114 \| 0.3705 \| 0.7157 \| 0.3371 \| 0.223 \| 0.4422 \| 0.5259 \| 0.1258 \| 0.4102 \| 0.5213 \| 0.3576 \| 0.5881 \| 0.6911 \| 0.2423 \| 0.4891 \| 0.5285 \| 0.6221 \| 0.3406 \| 0.4527 \| \| 0.696 \| 178.0 \| 119972 \| 0.9084 \| 0.3735 \| 0.7095 \| 0.3413 \| 0.2241 \| 0.4423 \| 0.5247 \| 0.126 \| 0.4118 \| 0.5217 \| 0.362 \| 0.5814 \| 0.6876 \| 0.2397 \| 0.4897 \| 0.5403 \| 0.627 \| 0.3406 \| 0.4484 \| \| 0.6947 \| 179.0 \| 120646 \| 0.9165 \| 0.3652 \| 0.71 \| 0.3222 \| 0.2239 \| 0.4324 \| 0.5155 \| 0.1244 \| 0.4055 \| 0.5199 \| 0.3638 \| 0.5765 \| 0.6836 \| 0.2297 \| 0.4793 \| 0.5322 \| 0.6296 \| 0.3338 \| 0.4507 \| \| 0.6839 \| 180.0 \| 121320 \| 0.9187 \| 0.3694 \| 0.7158 \| 0.3367 \| 0.2223 \| 0.4332 \| 0.5333 \| 0.1257 \| 0.4089 \| 0.5156 \| 0.3479 \| 0.5724 \| 0.6943 \| 0.2401 \| 0.479 \| 0.5338 \| 0.6297 \| 0.3344 \| 0.4382 \| \| 0.6827 \| 181.0 \| 121994 \| 0.9069 \| 0.3738 \| 0.715 \| 0.3385 \| 0.2411 \| 0.4343 \| 0.5185 \| 0.1249 \| 0.4102 \| 0.5229 \| 0.3635 \| 0.5758 \| 0.6906 \| 0.2369 \| 0.4839 \| 0.5393 \| 0.6301 \| 0.3453 \| 0.4548 \| \| 0.6815 \| 182.0 \| 122668 \| 0.8978 \| 0.3707 \| 0.7203 \| 0.3308 \| 0.2263 \| 0.4343 \| 0.517 \| 0.1261 \| 0.4088 \| 0.5241 \| 0.3718 \| 0.5745 \| 0.6894 \| 0.2438 \| 0.4919 \| 0.5321 \| 0.6296 \| 0.3363 \| 0.4507 \| \| 0.6839 \| 183.0 \| 123342 \| 0.9090 \| 0.367 \| 0.7203 \| 0.3306 \| 0.2142 \| 0.432 \| 0.5231 \| 0.1244 \| 0.4028 \| 0.5124 \| 0.3462 \| 0.5662 \| 0.6937 \| 0.2342 \| 0.4722 \| 0.5338 \| 0.6222 \| 0.3331 \| 0.4428 \| \| 0.6862 \| 184.0 \| 124016 \| 0.8963 \| 0.3687 \| 0.7183 \| 0.3343 \| 0.2208 \| 0.4363 \| 0.5313 \| 0.1272 \| 0.4081 \| 0.5197 \| 0.3655 \| 0.5755 \| 0.688 \| 0.2328 \| 0.4815 \| 0.5341 \| 0.6276 \| 0.3392 \| 0.45 \| \| 0.6679 \| 185.0 \| 124690 \| 0.8899 \| 0.3766 \| 0.7263 \| 0.3309 \| 0.2418 \| 0.4348 \| 0.5213 \| 0.126 \| 0.4145 \| 0.5292 \| 0.3902 \| 0.5748 \| 0.686 \| 0.2321 \| 0.4819 \| 0.5496 \| 0.6405 \| 0.3481 \| 0.4653 \| \| 0.6733 \| 186.0 \| 125364 \| 0.9037 \| 0.3724 \| 0.7205 \| 0.335 \| 0.2338 \| 0.4345 \| 0.5274 \| 0.1255 \| 0.4145 \| 0.5277 \| 0.381 \| 0.58 \| 0.6888 \| 0.2312 \| 0.4914 \| 0.5429 \| 0.6345 \| 0.3431 \| 0.4573 \| \| 0.6797 \| 187.0 \| 126038 \| 0.9226 \| 0.3715 \| 0.7261 \| 0.3296 \| 0.2249 \| 0.4362 \| 0.5249 \| 0.1272 \| 0.4086 \| 0.5195 \| 0.3645 \| 0.5731 \| 0.6792 \| 0.236 \| 0.4852 \| 0.5436 \| 0.6304 \| 0.335 \| 0.4428 \| \| 0.6737 \| 188.0 \| 126712 \| 0.8917 \| 0.3766 \| 0.7257 \| 0.3377 \| 0.2381 \| 0.4389 \| 0.5243 \| 0.1255 \| 0.4144 \| 0.5284 \| 0.3752 \| 0.5831 \| 0.6806 \| 0.2334 \| 0.4845 \| 0.5425 \| 0.6377 \| 0.3538 \| 0.4628 \| \| 0.6622 \| 189.0 \| 127386 \| 0.9061 \| 0.3755 \| 0.724 \| 0.3462 \| 0.2286 \| 0.4403 \| 0.5293 \| 0.1263 \| 0.4124 \| 0.525 \| 0.369 \| 0.5837 \| 0.6843 \| 0.2445 \| 0.4921 \| 0.5385 \| 0.6268 \| 0.3435 \| 0.456 \| \| 0.6732 \| 190.0 \| 128060 \| 0.9081 \| 0.3662 \| 0.7146 \| 0.3294 \| 0.2195 \| 0.431 \| 0.5138 \| 0.128 \| 0.4042 \| 0.5206 \| 0.3688 \| 0.5747 \| 0.6761 \| 0.2282 \| 0.4845 \| 0.5328 \| 0.6258 \| 0.3374 \| 0.4516 \| \| 0.6593 \| 191.0 \| 128734 \| 0.8968 \| 0.3729 \| 0.7255 \| 0.3414 \| 0.226 \| 0.4377 \| 0.5332 \| 0.1278 \| 0.4121 \| 0.5279 \| 0.3801 \| 0.5818 \| 0.6885 \| 0.2371 \| 0.4925 \| 0.545 \| 0.6382 \| 0.3365 \| 0.453 \| \| 0.6631 \| 192.0 \| 129408 \| 0.8903 \| 0.3765 \| 0.7278 \| 0.3313 \| 0.2312 \| 0.4327 \| 0.5252 \| 0.1283 \| 0.4148 \| 0.5279 \| 0.376 \| 0.576 \| 0.6854 \| 0.2363 \| 0.4896 \| 0.5477 \| 0.6374 \| 0.3455 \| 0.4567 \| \| 0.6583 \| 193.0 \| 130082 \| 0.8890 \| 0.379 \| 0.727 \| 0.342 \| 0.242 \| 0.4362 \| 0.5392 \| 0.1272 \| 0.4163 \| 0.5286 \| 0.3787 \| 0.5779 \| 0.6999 \| 0.2451 \| 0.4891 \| 0.5476 \| 0.6426 \| 0.3442 \| 0.4542 \| \| 0.6583 \| 194.0 \| 130756 \| 0.9139 \| 0.3624 \| 0.7256 \| 0.3182 \| 0.2216 \| 0.4211 \| 0.5211 \| 0.1232 \| 0.4034 \| 0.5126 \| 0.3521 \| 0.563 \| 0.6929 \| 0.2344 \| 0.4739 \| 0.5179 \| 0.6201 \| 0.3348 \| 0.4439 \| \| 0.6638 \| 195.0 \| 131430 \| 0.9190 \| 0.3627 \| 0.7174 \| 0.3231 \| 0.2262 \| 0.4209 \| 0.5235 \| 0.1263 \| 0.4019 \| 0.5129 \| 0.359 \| 0.5644 \| 0.6883 \| 0.2314 \| 0.4791 \| 0.5241 \| 0.6188 \| 0.3327 \| 0.4408 \| \| 0.656 \| 196.0 \| 132104 \| 0.9119 \| 0.3735 \| 0.7141 \| 0.3528 \| 0.2262 \| 0.4352 \| 0.5194 \| 0.1282 \| 0.4069 \| 0.5195 \| 0.3663 \| 0.5683 \| 0.6813 \| 0.2313 \| 0.4716 \| 0.5328 \| 0.6234 \| 0.3565 \| 0.4636 \| \| 0.6591 \| 197.0 \| 132778 \| 0.9007 \| 0.3758 \| 0.7205 \| 0.3428 \| 0.2362 \| 0.4328 \| 0.5218 \| 0.1281 \| 0.413 \| 0.5261 \| 0.3806 \| 0.5708 \| 0.6915 \| 0.2337 \| 0.4859 \| 0.54 \| 0.6309 \| 0.3535 \| 0.4616 \| \| 0.6579 \| 198.0 \| 133452 \| 0.9165 \| 0.3727 \| 0.7219 \| 0.3365 \| 0.2271 \| 0.4335 \| 0.5244 \| 0.1263 \| 0.4101 \| 0.5182 \| 0.3628 \| 0.5737 \| 0.6931 \| 0.2417 \| 0.4814 \| 0.5356 \| 0.624 \| 0.3409 \| 0.4492 \| \| 0.6506 \| 199.0 \| 134126 \| 0.9074 \| 0.3708 \| 0.7188 \| 0.3439 \| 0.229 \| 0.4328 \| 0.518 \| 0.1261 \| 0.4145 \| 0.5196 \| 0.3716 \| 0.5689 \| 0.6889 \| 0.2289 \| 0.4773 \| 0.5439 \| 0.6348 \| 0.3396 \| 0.4465 \| \| 0.6562 \| 200.0 \| 134800 \| 0.8944 \| 0.3742 \| 0.724 \| 0.3406 \| 0.2332 \| 0.4334 \| 0.5212 \| 0.1246 \| 0.4132 \| 0.5188 \| 0.3672 \| 0.5666 \| 0.6823 \| 0.2273 \| 0.4663 \| 0.5397 \| 0.6309 \| 0.3555 \| 0.4592 \| \| 0.6485 \| 201.0 \| 135474 \| 0.8983 \| 0.3768 \| 0.7315 \| 0.3439 \| 0.2339 \| 0.4421 \| 0.5188 \| 0.1247 \| 0.4105 \| 0.5222 \| 0.3704 \| 0.576 \| 0.6861 \| 0.2288 \| 0.4705 \| 0.5481 \| 0.6333 \| 0.3535 \| 0.4629 \| \| 0.6583 \| 202.0 \| 136148 \| 0.9362 \| 0.3545 \| 0.7029 \| 0.3156 \| 0.2045 \| 0.435 \| 0.5097 \| 0.1218 \| 0.3976 \| 0.5059 \| 0.3352 \| 0.5695 \| 0.689 \| 0.2187 \| 0.4677 \| 0.5367 \| 0.6275 \| 0.308 \| 0.4224 \| \| 0.6458 \| 203.0 \| 136822 \| 0.9076 \| 0.3721 \| 0.7188 \| 0.3398 \| 0.2239 \| 0.4348 \| 0.5193 \| 0.1264 \| 0.4102 \| 0.5206 \| 0.3566 \| 0.5785 \| 0.6935 \| 0.237 \| 0.4825 \| 0.5397 \| 0.6268 \| 0.3396 \| 0.4524 \| \| 0.642 \| 204.0 \| 137496 \| 0.9140 \| 0.3664 \| 0.7155 \| 0.3346 \| 0.2231 \| 0.4351 \| 0.5212 \| 0.1268 \| 0.402 \| 0.5144 \| 0.3449 \| 0.5804 \| 0.6941 \| 0.2359 \| 0.4738 \| 0.5383 \| 0.6278 \| 0.325 \| 0.4416 \| \| 0.645 \| 205.0 \| 138170 \| 0.9019 \| 0.3747 \| 0.7289 \| 0.3457 \| 0.2277 \| 0.4403 \| 0.5268 \| 0.128 \| 0.4096 \| 0.5228 \| 0.3615 \| 0.578 \| 0.6981 \| 0.2339 \| 0.4745 \| 0.5421 \| 0.6348 \| 0.3481 \| 0.4591 \| \| 0.6376 \| 206.0 \| 138844 \| 0.9077 \| 0.3733 \| 0.7243 \| 0.3436 \| 0.2337 \| 0.4348 \| 0.5275 \| 0.1276 \| 0.4098 \| 0.521 \| 0.3716 \| 0.5731 \| 0.6874 \| 0.2361 \| 0.4762 \| 0.5498 \| 0.6382 \| 0.3339 \| 0.4486 \| \| 0.6416 \| 207.0 \| 139518 \| 0.8914 \| 0.3792 \| 0.7295 \| 0.3545 \| 0.2456 \| 0.4348 \| 0.526 \| 0.1272 \| 0.4187 \| 0.528 \| 0.382 \| 0.5725 \| 0.6948 \| 0.2371 \| 0.4843 \| 0.5529 \| 0.6423 \| 0.3476 \| 0.4574 \| \| 0.634 \| 208.0 \| 140192 \| 0.9061 \| 0.3661 \| 0.7218 \| 0.3271 \| 0.2207 \| 0.4298 \| 0.5096 \| 0.1269 \| 0.408 \| 0.5211 \| 0.3669 \| 0.5752 \| 0.6809 \| 0.2261 \| 0.4814 \| 0.5357 \| 0.6304 \| 0.3364 \| 0.4516 \| \| 0.6335 \| 209.0 \| 140866 \| 0.9002 \| 0.3677 \| 0.7281 \| 0.3289 \| 0.2329 \| 0.4282 \| 0.5194 \| 0.1267 \| 0.41 \| 0.5199 \| 0.3611 \| 0.5716 \| 0.6987 \| 0.229 \| 0.4828 \| 0.5356 \| 0.6281 \| 0.3385 \| 0.4489 \| \| 0.6302 \| 210.0 \| 141540 \| 0.8955 \| 0.3767 \| 0.7324 \| 0.3432 \| 0.2384 \| 0.4333 \| 0.5291 \| 0.1286 \| 0.4173 \| 0.5256 \| 0.37 \| 0.5764 \| 0.6883 \| 0.2367 \| 0.4767 \| 0.5399 \| 0.634 \| 0.3535 \| 0.4661 \| \| 0.6199 \| 211.0 \| 142214 \| 0.8979 \| 0.3792 \| 0.7353 \| 0.3472 \| 0.2463 \| 0.4402 \| 0.5101 \| 0.1286 \| 0.4187 \| 0.5344 \| 0.3821 \| 0.587 \| 0.6946 \| 0.2419 \| 0.4995 \| 0.5421 \| 0.6423 \| 0.3536 \| 0.4613 \| \| 0.6164 \| 212.0 \| 142888 \| 0.9057 \| 0.3743 \| 0.7306 \| 0.3464 \| 0.2374 \| 0.4365 \| 0.5114 \| 0.1249 \| 0.4148 \| 0.5255 \| 0.3746 \| 0.5769 \| 0.6919 \| 0.2353 \| 0.4806 \| 0.5378 \| 0.6382 \| 0.3499 \| 0.4576 \| \| 0.6184 \| 213.0 \| 143562 \| 0.8919 \| 0.3785 \| 0.7338 \| 0.3531 \| 0.2407 \| 0.4394 \| 0.5215 \| 0.126 \| 0.4164 \| 0.5291 \| 0.3779 \| 0.5812 \| 0.6906 \| 0.2349 \| 0.4909 \| 0.5465 \| 0.6356 \| 0.354 \| 0.4609 \| \| 0.6234 \| 214.0 \| 144236 \| 0.8890 \| 0.3816 \| 0.7378 \| 0.3565 \| 0.2538 \| 0.4348 \| 0.5286 \| 0.1284 \| 0.4164 \| 0.5276 \| 0.3801 \| 0.5707 \| 0.6903 \| 0.2303 \| 0.4743 \| 0.548 \| 0.6379 \| 0.3666 \| 0.4705 \| \| 0.6244 \| 215.0 \| 144910 \| 0.8916 \| 0.3801 \| 0.7309 \| 0.3586 \| 0.2515 \| 0.4337 \| 0.5222 \| 0.1273 \| 0.4161 \| 0.5272 \| 0.3785 \| 0.5734 \| 0.6925 \| 0.2393 \| 0.4847 \| 0.5506 \| 0.6377 \| 0.3505 \| 0.4592 \| \| 0.6212 \| 216.0 \| 145584 \| 0.8984 \| 0.3805 \| 0.7294 \| 0.3514 \| 0.2475 \| 0.4363 \| 0.531 \| 0.1294 \| 0.4138 \| 0.5258 \| 0.3819 \| 0.5675 \| 0.6996 \| 0.2388 \| 0.4833 \| 0.5439 \| 0.631 \| 0.359 \| 0.463 \| \| 0.6106 \| 217.0 \| 146258 \| 0.9122 \| 0.3752 \| 0.7228 \| 0.3512 \| 0.2492 \| 0.4295 \| 0.5183 \| 0.1277 \| 0.4091 \| 0.5252 \| 0.3776 \| 0.566 \| 0.6972 \| 0.2253 \| 0.472 \| 0.5393 \| 0.6381 \| 0.3611 \| 0.4656 \| \| 0.6127 \| 218.0 \| 146932 \| 0.9043 \| 0.3769 \| 0.7289 \| 0.3432 \| 0.2501 \| 0.4359 \| 0.5198 \| 0.1276 \| 0.4136 \| 0.5258 \| 0.3742 \| 0.5754 \| 0.6971 \| 0.2261 \| 0.4739 \| 0.55 \| 0.6426 \| 0.3546 \| 0.461 \| \| 0.6144 \| 219.0 \| 147606 \| 0.9077 \| 0.3753 \| 0.7305 \| 0.353 \| 0.2416 \| 0.4368 \| 0.5205 \| 0.1239 \| 0.4126 \| 0.5235 \| 0.3673 \| 0.5754 \| 0.6903 \| 0.2299 \| 0.4759 \| 0.5387 \| 0.6297 \| 0.3573 \| 0.4649 \| \| 0.6064 \| 220.0 \| 148280 \| 0.9145 \| 0.374 \| 0.724 \| 0.3392 \| 0.2351 \| 0.433 \| 0.5212 \| 0.1272 \| 0.4096 \| 0.522 \| 0.3685 \| 0.5661 \| 0.6911 \| 0.2267 \| 0.474 \| 0.5437 \| 0.632 \| 0.3516 \| 0.4599 \| \| 0.6065 \| 221.0 \| 148954 \| 0.9038 \| 0.3782 \| 0.7305 \| 0.3471 \| 0.2464 \| 0.4333 \| 0.5142 \| 0.1262 \| 0.4136 \| 0.5246 \| 0.3772 \| 0.5685 \| 0.6769 \| 0.2269 \| 0.4705 \| 0.5441 \| 0.6359 \| 0.3638 \| 0.4673 \| \| 0.605 \| 222.0 \| 149628 \| 0.8916 \| 0.3832 \| 0.7358 \| 0.3534 \| 0.2518 \| 0.4397 \| 0.5256 \| 0.1281 \| 0.4174 \| 0.5328 \| 0.3852 \| 0.5803 \| 0.6891 \| 0.2352 \| 0.4867 \| 0.5463 \| 0.6404 \| 0.368 \| 0.4712 \| \| 0.5999 \| 223.0 \| 150302 \| 0.9010 \| 0.3852 \| 0.734 \| 0.3557 \| 0.2555 \| 0.4468 \| 0.5187 \| 0.1294 \| 0.4188 \| 0.5302 \| 0.3862 \| 0.5785 \| 0.6829 \| 0.2343 \| 0.4771 \| 0.5534 \| 0.6456 \| 0.3678 \| 0.468 \| \| 0.6048 \| 224.0 \| 150976 \| 0.8917 \| 0.3858 \| 0.7325 \| 0.3647 \| 0.2535 \| 0.4433 \| 0.5283 \| 0.1274 \| 0.4161 \| 0.529 \| 0.3763 \| 0.5772 \| 0.6994 \| 0.231 \| 0.4721 \| 0.5566 \| 0.6443 \| 0.3699 \| 0.4706 \| \| 0.5953 \| 225.0 \| 151650 \| 0.9025 \| 0.3826 \| 0.7318 \| 0.3538 \| 0.2447 \| 0.4434 \| 0.5207 \| 0.1265 \| 0.4142 \| 0.5255 \| 0.3736 \| 0.5753 \| 0.684 \| 0.2342 \| 0.4759 \| 0.5501 \| 0.6337 \| 0.3635 \| 0.4671 \| \| 0.6101 \| 226.0 \| 152324 \| 0.9039 \| 0.3783 \| 0.7278 \| 0.3559 \| 0.2389 \| 0.441 \| 0.5256 \| 0.1265 \| 0.4129 \| 0.5224 \| 0.3613 \| 0.5761 \| 0.6851 \| 0.2272 \| 0.4715 \| 0.5495 \| 0.6395 \| 0.3581 \| 0.4563 \| \| 0.593 \| 227.0 \| 152998 \| 0.9035 \| 0.3825 \| 0.7304 \| 0.3535 \| 0.2473 \| 0.4413 \| 0.5247 \| 0.1281 \| 0.4172 \| 0.528 \| 0.3757 \| 0.5769 \| 0.692 \| 0.2391 \| 0.4802 \| 0.5464 \| 0.6382 \| 0.362 \| 0.4656 \| \| 0.5995 \| 228.0 \| 153672 \| 0.9083 \| 0.3794 \| 0.7297 \| 0.3441 \| 0.2496 \| 0.4342 \| 0.5178 \| 0.1264 \| 0.4142 \| 0.5297 \| 0.3819 \| 0.5773 \| 0.697 \| 0.2295 \| 0.4837 \| 0.5461 \| 0.6358 \| 0.3627 \| 0.4697 \| \| 0.6016 \| 229.0 \| 154346 \| 0.9108 \| 0.3741 \| 0.7277 \| 0.3384 \| 0.2429 \| 0.4316 \| 0.5104 \| 0.1268 \| 0.4109 \| 0.5229 \| 0.3762 \| 0.5719 \| 0.6881 \| 0.2325 \| 0.4785 \| 0.5422 \| 0.6325 \| 0.3476 \| 0.4576 \| \| 0.6008 \| 230.0 \| 155020 \| 0.8996 \| 0.3818 \| 0.736 \| 0.3452 \| 0.2521 \| 0.4341 \| 0.517 \| 0.1302 \| 0.417 \| 0.531 \| 0.3928 \| 0.5695 \| 0.6946 \| 0.2347 \| 0.4781 \| 0.5473 \| 0.6371 \| 0.3634 \| 0.4779 \| \| 0.6038 \| 231.0 \| 155694 \| 0.9017 \| 0.3782 \| 0.7279 \| 0.3403 \| 0.252 \| 0.4353 \| 0.5178 \| 0.1282 \| 0.4137 \| 0.527 \| 0.3819 \| 0.574 \| 0.6874 \| 0.2281 \| 0.4774 \| 0.5509 \| 0.6412 \| 0.3558 \| 0.4623 \| \| 0.5983 \| 232.0 \| 156368 \| 0.9024 \| 0.3801 \| 0.7262 \| 0.3458 \| 0.251 \| 0.436 \| 0.5109 \| 0.1268 \| 0.4129 \| 0.5299 \| 0.3872 \| 0.5748 \| 0.6814 \| 0.2292 \| 0.475 \| 0.5488 \| 0.6404 \| 0.3624 \| 0.4742 \| \| 0.5905 \| 233.0 \| 157042 \| 0.9003 \| 0.3828 \| 0.7349 \| 0.3451 \| 0.2498 \| 0.4389 \| 0.5156 \| 0.1264 \| 0.4143 \| 0.5283 \| 0.3861 \| 0.572 \| 0.6823 \| 0.2307 \| 0.467 \| 0.5504 \| 0.6399 \| 0.3672 \| 0.478 \| \| 0.5894 \| 234.0 \| 157716 \| 0.9006 \| 0.3848 \| 0.7332 \| 0.3529 \| 0.2483 \| 0.4449 \| 0.515 \| 0.1285 \| 0.4164 \| 0.5325 \| 0.3851 \| 0.5786 \| 0.6782 \| 0.2381 \| 0.4815 \| 0.5471 \| 0.6387 \| 0.3692 \| 0.4773 \| \| 0.5891 \| 235.0 \| 158390 \| 0.9039 \| 0.3845 \| 0.7311 \| 0.3574 \| 0.2565 \| 0.4386 \| 0.5118 \| 0.127 \| 0.4189 \| 0.5309 \| 0.3932 \| 0.5725 \| 0.6872 \| 0.2312 \| 0.4731 \| 0.5522 \| 0.6446 \| 0.37 \| 0.4751 \| \| 0.5845 \| 236.0 \| 159064 \| 0.9055 \| 0.3826 \| 0.7323 \| 0.351 \| 0.2473 \| 0.4424 \| 0.5204 \| 0.1241 \| 0.4151 \| 0.5313 \| 0.3756 \| 0.5821 \| 0.6938 \| 0.2331 \| 0.4807 \| 0.5502 \| 0.6392 \| 0.3646 \| 0.474 \| \| 0.5811 \| 237.0 \| 159738 \| 0.9109 \| 0.3768 \| 0.7261 \| 0.3539 \| 0.2417 \| 0.4402 \| 0.5118 \| 0.1262 \| 0.411 \| 0.5232 \| 0.3657 \| 0.5761 \| 0.6883 \| 0.2244 \| 0.4677 \| 0.5466 \| 0.6335 \| 0.3595 \| 0.4684 \| \| 0.5785 \| 238.0 \| 160412 \| 0.9032 \| 0.3791 \| 0.7253 \| 0.3562 \| 0.2531 \| 0.4382 \| 0.5054 \| 0.1263 \| 0.4121 \| 0.5284 \| 0.393 \| 0.5749 \| 0.6746 \| 0.2231 \| 0.4726 \| 0.5486 \| 0.6374 \| 0.3656 \| 0.4753 \| \| 0.5866 \| 239.0 \| 161086 \| 0.9085 \| 0.3743 \| 0.7325 \| 0.3389 \| 0.2456 \| 0.4301 \| 0.5144 \| 0.1268 \| 0.4135 \| 0.522 \| 0.3788 \| 0.5718 \| 0.6829 \| 0.2286 \| 0.4706 \| 0.5401 \| 0.6302 \| 0.3543 \| 0.4652 \| \| 0.5908 \| 240.0 \| 161760 \| 0.9156 \| 0.3757 \| 0.7338 \| 0.3459 \| 0.2458 \| 0.432 \| 0.5066 \| 0.1263 \| 0.4156 \| 0.5235 \| 0.3777 \| 0.5742 \| 0.6814 \| 0.229 \| 0.4719 \| 0.5447 \| 0.6348 \| 0.3534 \| 0.4637 \| \| 0.5843 \| 241.0 \| 162434 \| 0.8990 \| 0.38 \| 0.7358 \| 0.3539 \| 0.2485 \| 0.4353 \| 0.5192 \| 0.1276 \| 0.4188 \| 0.5282 \| 0.3842 \| 0.574 \| 0.6908 \| 0.2277 \| 0.4747 \| 0.5488 \| 0.6356 \| 0.3636 \| 0.4743 \| \| 0.5841 \| 242.0 \| 163108 \| 0.9133 \| 0.3761 \| 0.7327 \| 0.3518 \| 0.2414 \| 0.4367 \| 0.5116 \| 0.125 \| 0.4145 \| 0.5197 \| 0.3697 \| 0.5767 \| 0.6742 \| 0.2283 \| 0.4676 \| 0.5466 \| 0.6366 \| 0.3535 \| 0.455 \| \| 0.5883 \| 243.0 \| 163782 \| 0.9081 \| 0.3795 \| 0.73 \| 0.356 \| 0.2472 \| 0.4382 \| 0.5136 \| 0.1274 \| 0.4129 \| 0.5223 \| 0.3721 \| 0.5705 \| 0.6814 \| 0.2296 \| 0.4674 \| 0.554 \| 0.6404 \| 0.3548 \| 0.4591 \| \| 0.5741 \| 244.0 \| 164456 \| 0.9041 \| 0.3845 \| 0.7383 \| 0.3598 \| 0.2517 \| 0.4437 \| 0.5126 \| 0.1285 \| 0.4208 \| 0.5279 \| 0.3797 \| 0.578 \| 0.6758 \| 0.2331 \| 0.4744 \| 0.5538 \| 0.6394 \| 0.3665 \| 0.47 \| \| 0.5839 \| 245.0 \| 165130 \| 0.9027 \| 0.382 \| 0.7323 \| 0.3539 \| 0.2607 \| 0.4403 \| 0.5048 \| 0.1261 \| 0.4175 \| 0.5302 \| 0.3897 \| 0.5764 \| 0.674 \| 0.2281 \| 0.4743 \| 0.5503 \| 0.6418 \| 0.3677 \| 0.4746 \| \| 0.5669 \| 246.0 \| 165804 \| 0.9144 \| 0.3816 \| 0.7337 \| 0.3596 \| 0.2587 \| 0.4368 \| 0.5101 \| 0.1277 \| 0.4128 \| 0.5238 \| 0.3818 \| 0.5669 \| 0.68 \| 0.2283 \| 0.463 \| 0.5459 \| 0.6324 \| 0.3706 \| 0.476 \| \| 0.5771 \| 247.0 \| 166478 \| 0.9048 \| 0.3818 \| 0.7276 \| 0.3603 \| 0.2577 \| 0.4396 \| 0.5212 \| 0.1264 \| 0.4165 \| 0.5268 \| 0.3825 \| 0.574 \| 0.6893 \| 0.2287 \| 0.4717 \| 0.553 \| 0.6425 \| 0.3636 \| 0.4662 \| \| 0.5851 \| 248.0 \| 167152 \| 0.9139 \| 0.3793 \| 0.7331 \| 0.3533 \| 0.2543 \| 0.4351 \| 0.5104 \| 0.1269 \| 0.415 \| 0.5264 \| 0.382 \| 0.5715 \| 0.6781 \| 0.2268 \| 0.4681 \| 0.5443 \| 0.6376 \| 0.3669 \| 0.4735 \| \| 0.5684 \| 249.0 \| 167826 \| 0.8998 \| 0.3822 \| 0.7374 \| 0.3526 \| 0.2568 \| 0.4417 \| 0.5165 \| 0.1283 \| 0.4177 \| 0.5276 \| 0.3771 \| 0.5759 \| 0.6864 \| 0.2305 \| 0.4711 \| 0.5516 \| 0.6402 \| 0.3644 \| 0.4714 \| \| 0.564 \| 250.0 \| 168500 \| 0.9078 \| 0.3795 \| 0.7386 \| 0.3467 \| 0.2538 \| 0.4363 \| 0.5131 \| 0.1261 \| 0.4178 \| 0.5264 \| 0.3853 \| 0.5691 \| 0.6765 \| 0.2281 \| 0.4708 \| 0.5432 \| 0.6379 \| 0.3671 \| 0.4705 \| \| 0.5688 \| 251.0 \| 169174 \| 0.9198 \| 0.3779 \| 0.734 \| 0.3454 \| 0.2513 \| 0.4354 \| 0.5152 \| 0.1269 \| 0.4178 \| 0.5282 \| 0.3862 \| 0.5744 \| 0.6821 \| 0.2283 \| 0.473 \| 0.5443 \| 0.6428 \| 0.3612 \| 0.4687 \| \| 0.5773 \| 252.0 \| 169848 \| 0.9195 \| 0.3769 \| 0.7389 \| 0.3388 \| 0.248 \| 0.4372 \| 0.5128 \| 0.1258 \| 0.4159 \| 0.5205 \| 0.3782 \| 0.5665 \| 0.6794 \| 0.2311 \| 0.4646 \| 0.5473 \| 0.6425 \| 0.3524 \| 0.4543 \| \| 0.5603 \| 253.0 \| 170522 \| 0.9128 \| 0.3841 \| 0.7364 \| 0.353 \| 0.2535 \| 0.4425 \| 0.5187 \| 0.1278 \| 0.4199 \| 0.5279 \| 0.3786 \| 0.5801 \| 0.6824 \| 0.2353 \| 0.4714 \| 0.55 \| 0.6407 \| 0.3671 \| 0.4717 \| \| 0.5569 \| 254.0 \| 171196 \| 0.9131 \| 0.3806 \| 0.734 \| 0.3413 \| 0.2513 \| 0.4402 \| 0.5118 \| 0.1266 \| 0.4143 \| 0.5273 \| 0.3837 \| 0.5719 \| 0.685 \| 0.2292 \| 0.4679 \| 0.5454 \| 0.6381 \| 0.3671 \| 0.4759 \| \| 0.5582 \| 255.0 \| 171870 \| 0.9064 \| 0.3798 \| 0.7373 \| 0.3469 \| 0.2539 \| 0.4373 \| 0.5106 \| 0.1269 \| 0.4175 \| 0.527 \| 0.386 \| 0.5725 \| 0.6794 \| 0.2277 \| 0.4681 \| 0.5465 \| 0.6395 \| 0.3654 \| 0.4734 \| \| 0.5619 \| 256.0 \| 172544 \| 0.9147 \| 0.3749 \| 0.7309 \| 0.3352 \| 0.2434 \| 0.4374 \| 0.5114 \| 0.1263 \| 0.4134 \| 0.5233 \| 0.3721 \| 0.5777 \| 0.6883 \| 0.2268 \| 0.473 \| 0.5444 \| 0.6377 \| 0.3534 \| 0.4592 \| \| 0.5491 \| 257.0 \| 173218 \| 0.9117 \| 0.3787 \| 0.7377 \| 0.3439 \| 0.2519 \| 0.4378 \| 0.5095 \| 0.1261 \| 0.4145 \| 0.5237 \| 0.3818 \| 0.5709 \| 0.6749 \| 0.2289 \| 0.4665 \| 0.5411 \| 0.6342 \| 0.3662 \| 0.4703 \| \| 0.5609 \| 258.0 \| 173892 \| 0.9110 \| 0.38 \| 0.7362 \| 0.3517 \| 0.2572 \| 0.4346 \| 0.5069 \| 0.1272 \| 0.4178 \| 0.5287 \| 0.3893 \| 0.5737 \| 0.6796 \| 0.2239 \| 0.4717 \| 0.5476 \| 0.6373 \| 0.3683 \| 0.4772 \| \| 0.5608 \| 259.0 \| 174566 \| 0.9192 \| 0.3803 \| 0.733 \| 0.3532 \| 0.2543 \| 0.438 \| 0.5147 \| 0.1282 \| 0.4131 \| 0.5252 \| 0.3841 \| 0.5746 \| 0.6789 \| 0.2284 \| 0.4698 \| 0.5473 \| 0.6356 \| 0.3653 \| 0.4703 \| \| 0.5488 \| 260.0 \| 175240 \| 0.9231 \| 0.3787 \| 0.7325 \| 0.3526 \| 0.2544 \| 0.4336 \| 0.5085 \| 0.1259 \| 0.4142 \| 0.5245 \| 0.3774 \| 0.5752 \| 0.676 \| 0.2259 \| 0.4662 \| 0.5464 \| 0.6373 \| 0.3638 \| 0.47 \| \| 0.5537 \| 261.0 \| 175914 \| 0.9194 \| 0.3812 \| 0.7324 \| 0.3557 \| 0.2541 \| 0.4402 \| 0.5081 \| 0.1279 \| 0.4148 \| 0.5255 \| 0.3795 \| 0.5774 \| 0.675 \| 0.2248 \| 0.467 \| 0.5531 \| 0.6377 \| 0.3657 \| 0.4718 \| \| 0.5564 \| 262.0 \| 176588 \| 0.9197 \| 0.3788 \| 0.7362 \| 0.3527 \| 0.2536 \| 0.4364 \| 0.4998 \| 0.1258 \| 0.4153 \| 0.5257 \| 0.3871 \| 0.5697 \| 0.6747 \| 0.2242 \| 0.4677 \| 0.5509 \| 0.6394 \| 0.3614 \| 0.47 \| \| 0.5532 \| 263.0 \| 177262 \| 0.9117 \| 0.3835 \| 0.7387 \| 0.3575 \| 0.2585 \| 0.44 \| 0.5066 \| 0.1292 \| 0.4155 \| 0.5284 \| 0.3912 \| 0.5752 \| 0.6743 \| 0.227 \| 0.466 \| 0.5517 \| 0.642 \| 0.3717 \| 0.4771 \| \| 0.5501 \| 264.0 \| 177936 \| 0.9117 \| 0.3831 \| 0.737 \| 0.3573 \| 0.2586 \| 0.4375 \| 0.5138 \| 0.1275 \| 0.416 \| 0.5328 \| 0.3923 \| 0.5765 \| 0.6873 \| 0.2265 \| 0.4731 \| 0.553 \| 0.6469 \| 0.3697 \| 0.4784 \| \| 0.5519 \| 265.0 \| 178610 \| 0.9051 \| 0.3855 \| 0.7399 \| 0.3554 \| 0.2626 \| 0.4415 \| 0.5101 \| 0.1275 \| 0.4201 \| 0.5323 \| 0.3928 \| 0.5774 \| 0.6804 \| 0.229 \| 0.4731 \| 0.5537 \| 0.6436 \| 0.3738 \| 0.4801 \| \| 0.5478 \| 266.0 \| 179284 \| 0.9123 \| 0.3821 \| 0.7381 \| 0.3549 \| 0.262 \| 0.4349 \| 0.5107 \| 0.1266 \| 0.4163 \| 0.5281 \| 0.389 \| 0.573 \| 0.6788 \| 0.2271 \| 0.4681 \| 0.5497 \| 0.6431 \| 0.3693 \| 0.4732 \| \| 0.5422 \| 267.0 \| 179958 \| 0.9016 \| 0.3846 \| 0.7381 \| 0.3615 \| 0.2598 \| 0.4407 \| 0.5119 \| 0.1274 \| 0.4176 \| 0.5319 \| 0.3914 \| 0.5759 \| 0.6841 \| 0.2309 \| 0.4766 \| 0.5538 \| 0.6458 \| 0.369 \| 0.4735 \| \| 0.5456 \| 268.0 \| 180632 \| 0.9068 \| 0.3805 \| 0.7432 \| 0.3565 \| 0.255 \| 0.4402 \| 0.5109 \| 0.1249 \| 0.4153 \| 0.5283 \| 0.3857 \| 0.5772 \| 0.6814 \| 0.228 \| 0.4686 \| 0.5493 \| 0.6435 \| 0.3641 \| 0.4728 \| \| 0.5437 \| 269.0 \| 181306 \| 0.9086 \| 0.3857 \| 0.7369 \| 0.3653 \| 0.2638 \| 0.4417 \| 0.5143 \| 0.127 \| 0.4208 \| 0.5346 \| 0.399 \| 0.5765 \| 0.6837 \| 0.2299 \| 0.4749 \| 0.555 \| 0.6469 \| 0.3722 \| 0.4819 \| \| 0.5434 \| 270.0 \| 181980 \| 0.9111 \| 0.3857 \| 0.7317 \| 0.3581 \| 0.2621 \| 0.4418 \| 0.514 \| 0.1275 \| 0.4184 \| 0.533 \| 0.3985 \| 0.5762 \| 0.6832 \| 0.2273 \| 0.4755 \| 0.5591 \| 0.6461 \| 0.3707 \| 0.4773 \| \| 0.5399 \| 271.0 \| 182654 \| 0.9117 \| 0.3852 \| 0.7334 \| 0.3725 \| 0.2581 \| 0.4434 \| 0.5179 \| 0.1261 \| 0.4158 \| 0.5297 \| 0.3901 \| 0.5793 \| 0.6801 \| 0.2278 \| 0.4728 \| 0.5549 \| 0.6444 \| 0.3729 \| 0.4718 \| \| 0.541 \| 272.0 \| 183328 \| 0.9128 \| 0.3848 \| 0.7407 \| 0.3626 \| 0.2629 \| 0.4378 \| 0.5165 \| 0.1264 \| 0.4152 \| 0.529 \| 0.3942 \| 0.5711 \| 0.6816 \| 0.2277 \| 0.4705 \| 0.5499 \| 0.6361 \| 0.3769 \| 0.4805 \| \| 0.541 \| 273.0 \| 184002 \| 0.9104 \| 0.3815 \| 0.738 \| 0.3528 \| 0.2531 \| 0.4414 \| 0.515 \| 0.1252 \| 0.4136 \| 0.5265 \| 0.3873 \| 0.5768 \| 0.6756 \| 0.2264 \| 0.4657 \| 0.5515 \| 0.6446 \| 0.3667 \| 0.4693 \| \| 0.5458 \| 274.0 \| 184676 \| 0.9146 \| 0.3815 \| 0.7338 \| 0.3556 \| 0.2569 \| 0.4376 \| 0.5106 \| 0.1261 \| 0.4162 \| 0.5274 \| 0.3958 \| 0.572 \| 0.6762 \| 0.2288 \| 0.47 \| 0.5431 \| 0.6381 \| 0.3725 \| 0.4741 \| \| 0.5398 \| 275.0 \| 185350 \| 0.9026 \| 0.384 \| 0.7397 \| 0.3512 \| 0.2604 \| 0.4374 \| 0.5195 \| 0.1272 \| 0.4169 \| 0.5298 \| 0.3935 \| 0.5711 \| 0.6844 \| 0.2327 \| 0.473 \| 0.5473 \| 0.6399 \| 0.372 \| 0.4765 \| \| 0.5339 \| 276.0 \| 186024 \| 0.9063 \| 0.3833 \| 0.738 \| 0.3634 \| 0.2556 \| 0.4397 \| 0.5105 \| 0.1267 \| 0.4158 \| 0.5305 \| 0.3922 \| 0.574 \| 0.6797 \| 0.2265 \| 0.4716 \| 0.5486 \| 0.6404 \| 0.3748 \| 0.4795 \| \| 0.5282 \| 277.0 \| 186698 \| 0.9073 \| 0.386 \| 0.7391 \| 0.3663 \| 0.2609 \| 0.4413 \| 0.5116 \| 0.1275 \| 0.4172 \| 0.5305 \| 0.3942 \| 0.5715 \| 0.6795 \| 0.2294 \| 0.4692 \| 0.554 \| 0.6446 \| 0.3746 \| 0.4776 \| \| 0.5423 \| 278.0 \| 187372 \| 0.9045 \| 0.3867 \| 0.7398 \| 0.36 \| 0.2636 \| 0.4412 \| 0.5124 \| 0.127 \| 0.4183 \| 0.5323 \| 0.3981 \| 0.571 \| 0.6795 \| 0.2298 \| 0.471 \| 0.5531 \| 0.644 \| 0.3771 \| 0.4819 \| \| 0.5296 \| 279.0 \| 188046 \| 0.9049 \| 0.3867 \| 0.7468 \| 0.3621 \| 0.264 \| 0.4407 \| 0.5167 \| 0.1266 \| 0.4196 \| 0.5333 \| 0.3981 \| 0.5736 \| 0.6798 \| 0.2331 \| 0.4748 \| 0.5506 \| 0.6436 \| 0.3763 \| 0.4816 \| \| 0.5339 \| 280.0 \| 188720 \| 0.9117 \| 0.3857 \| 0.7354 \| 0.3608 \| 0.2623 \| 0.4415 \| 0.5133 \| 0.1276 \| 0.4172 \| 0.5311 \| 0.3953 \| 0.5725 \| 0.6816 \| 0.2287 \| 0.4731 \| 0.5517 \| 0.6425 \| 0.3766 \| 0.4778 \| \| 0.5259 \| 281.0 \| 189394 \| 0.9092 \| 0.3833 \| 0.7353 \| 0.36 \| 0.2564 \| 0.4394 \| 0.5153 \| 0.1272 \| 0.4161 \| 0.5277 \| 0.3877 \| 0.5715 \| 0.6781 \| 0.2285 \| 0.4688 \| 0.5474 \| 0.6382 \| 0.3738 \| 0.4759 \| \| 0.5275 \| 282.0 \| 190068 \| 0.9104 \| 0.3845 \| 0.741 \| 0.3689 \| 0.2601 \| 0.438 \| 0.5141 \| 0.1264 \| 0.4168 \| 0.5289 \| 0.3933 \| 0.5723 \| 0.6767 \| 0.2284 \| 0.4676 \| 0.5509 \| 0.6425 \| 0.3743 \| 0.4766 \| \| 0.5268 \| 283.0 \| 190742 \| 0.9081 \| 0.3859 \| 0.738 \| 0.3678 \| 0.2598 \| 0.4444 \| 0.5146 \| 0.1266 \| 0.4185 \| 0.5302 \| 0.3935 \| 0.5754 \| 0.6768 \| 0.2289 \| 0.472 \| 0.5544 \| 0.6441 \| 0.3745 \| 0.4745 \| \| 0.5237 \| 284.0 \| 191416 \| 0.9125 \| 0.3861 \| 0.7325 \| 0.3738 \| 0.259 \| 0.4446 \| 0.5095 \| 0.1272 \| 0.417 \| 0.5293 \| 0.3919 \| 0.5744 \| 0.6754 \| 0.2273 \| 0.4677 \| 0.5563 \| 0.6438 \| 0.3747 \| 0.4764 \| \| 0.5253 \| 285.0 \| 192090 \| 0.9077 \| 0.3883 \| 0.7366 \| 0.3703 \| 0.2622 \| 0.4486 \| 0.5082 \| 0.127 \| 0.4204 \| 0.5323 \| 0.3971 \| 0.5762 \| 0.6777 \| 0.2318 \| 0.473 \| 0.5541 \| 0.642 \| 0.3791 \| 0.482 \| \| 0.5268 \| 286.0 \| 192764 \| 0.9117 \| 0.3853 \| 0.7372 \| 0.3653 \| 0.2577 \| 0.4442 \| 0.5156 \| 0.1267 \| 0.4189 \| 0.528 \| 0.3883 \| 0.5715 \| 0.6773 \| 0.2298 \| 0.4688 \| 0.5514 \| 0.6405 \| 0.3747 \| 0.4747 \| \| 0.5262 \| 287.0 \| 193438 \| 0.9098 \| 0.3876 \| 0.74 \| 0.3726 \| 0.2628 \| 0.4443 \| 0.5129 \| 0.1278 \| 0.4219 \| 0.5313 \| 0.3981 \| 0.5755 \| 0.6766 \| 0.2315 \| 0.4691 \| 0.5544 \| 0.6448 \| 0.3768 \| 0.48 \| \| 0.5134 \| 288.0 \| 194112 \| 0.9101 \| 0.3876 \| 0.738 \| 0.3749 \| 0.2624 \| 0.4451 \| 0.5097 \| 0.1285 \| 0.4203 \| 0.5314 \| 0.3971 \| 0.5749 \| 0.6759 \| 0.2324 \| 0.4716 \| 0.5557 \| 0.6462 \| 0.3748 \| 0.4763 \| \| 0.517 \| 289.0 \| 194786 \| 0.9097 \| 0.3868 \| 0.7358 \| 0.3683 \| 0.259 \| 0.4456 \| 0.5121 \| 0.1279 \| 0.4201 \| 0.5292 \| 0.391 \| 0.5737 \| 0.6775 \| 0.2303 \| 0.4681 \| 0.5537 \| 0.6426 \| 0.3764 \| 0.477 \| \| 0.5329 \| 290.0 \| 195460 \| 0.9063 \| 0.3879 \| 0.7427 \| 0.3717 \| 0.2626 \| 0.4451 \| 0.511 \| 0.1285 \| 0.4203 \| 0.5308 \| 0.396 \| 0.5746 \| 0.6749 \| 0.2358 \| 0.4705 \| 0.5524 \| 0.6422 \| 0.3754 \| 0.4798 \| \| 0.5242 \| 291.0 \| 196134 \| 0.9133 \| 0.3847 \| 0.737 \| 0.3624 \| 0.2587 \| 0.4437 \| 0.5141 \| 0.1263 \| 0.4171 \| 0.5293 \| 0.3912 \| 0.5744 \| 0.6791 \| 0.234 \| 0.4726 \| 0.5506 \| 0.6426 \| 0.3693 \| 0.4726 \| \| 0.5197 \| 292.0 \| 196808 \| 0.9090 \| 0.3874 \| 0.7402 \| 0.37 \| 0.2623 \| 0.4446 \| 0.5092 \| 0.1279 \| 0.4195 \| 0.5322 \| 0.3957 \| 0.574 \| 0.6805 \| 0.2318 \| 0.4712 \| 0.5519 \| 0.6436 \| 0.3784 \| 0.4817 \| \| 0.5233 \| 293.0 \| 197482 \| 0.9104 \| 0.3868 \| 0.7368 \| 0.3642 \| 0.2606 \| 0.4459 \| 0.5108 \| 0.1283 \| 0.4192 \| 0.5304 \| 0.3957 \| 0.5748 \| 0.6757 \| 0.2343 \| 0.4736 \| 0.5525 \| 0.641 \| 0.3735 \| 0.4765 \| \| 0.5232 \| 294.0 \| 198156 \| 0.9099 \| 0.3854 \| 0.7391 \| 0.3618 \| 0.2593 \| 0.4432 \| 0.5122 \| 0.1274 \| 0.4176 \| 0.5297 \| 0.3921 \| 0.5732 \| 0.6796 \| 0.2334 \| 0.4726 \| 0.5501 \| 0.6407 \| 0.3728 \| 0.4759 \| \| 0.5165 \| 295.0 \| 198830 \| 0.9107 \| 0.3861 \| 0.7392 \| 0.3663 \| 0.2596 \| 0.4431 \| 0.5111 \| 0.1277 \| 0.4185 \| 0.531 \| 0.3934 \| 0.574 \| 0.6788 \| 0.2312 \| 0.4702 \| 0.5519 \| 0.6418 \| 0.3753 \| 0.4809 \| \| 0.5185 \| 296.0 \| 199504 \| 0.9077 \| 0.3881 \| 0.7402 \| 0.3758 \| 0.2655 \| 0.4413 \| 0.5179 \| 0.1282 \| 0.4188 \| 0.5318 \| 0.3961 \| 0.5731 \| 0.6811 \| 0.2326 \| 0.4707 \| 0.5531 \| 0.6433 \| 0.3785 \| 0.4814 \| \| 0.5174 \| 297.0 \| 200178 \| 0.9074 \| 0.3878 \| 0.7416 \| 0.3717 \| 0.2635 \| 0.4442 \| 0.5143 \| 0.129 \| 0.4187 \| 0.5309 \| 0.3949 \| 0.5737 \| 0.6793 \| 0.2335 \| 0.4702 \| 0.552 \| 0.6413 \| 0.3779 \| 0.4813 \| \| 0.5228 \| 298.0 \| 200852 \| 0.9071 \| 0.3875 \| 0.7411 \| 0.3668 \| 0.2637 \| 0.4431 \| 0.5118 \| 0.1283 \| 0.4186 \| 0.5321 \| 0.398 \| 0.5731 \| 0.6765 \| 0.2328 \| 0.4717 \| 0.5523 \| 0.6425 \| 0.3775 \| 0.4821 \| \| 0.5159 \| 299.0 \| 201526 \| 0.9071 \| 0.3879 \| 0.7416 \| 0.3684 \| 0.2634 \| 0.4435 \| 0.5135 \| 0.1286 \| 0.419 \| 0.5322 \| 0.3969 \| 0.5741 \| 0.6773 \| 0.2325 \| 0.4706 \| 0.553 \| 0.6415 \| 0.3782 \| 0.4844 \| \| 0.5213 \| 300.0 \| 202200 \| 0.9075 \| 0.3883 \| 0.7422 \| 0.3707 \| 0.2639 \| 0.4432 \| 0.5149 \| 0.1286 \| 0.419 \| 0.5325 \| 0.3975 \| 0.5729 \| 0.68 \| 0.2322 \| 0.4708 \| 0.5547 \| 0.6431 \| 0.3779 \| 0.4834 \| ### Framework versions - Transformers 4.46.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.20.1	[ "fire", "vehicle", "human" ]
hanskantoss/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.6645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.5801 \| 0.4 \| 50 \| 7.2791 \| \| 3.004 \| 0.8 \| 100 \| 5.6645 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2	[ "accessories", "bags", "clothing", "shoes" ]
kike/table_structured_recognition_fito	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]
julianlec/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9905 \| 1.0 \| 5000 \| 0.9542 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
SkowKyubu/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.2	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
Max-Ploter/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.7762 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6869 \| 0.16 \| 100 \| 5.9745 \| \| 2.3703 \| 0.32 \| 200 \| 6.5070 \| \| 2.5309 \| 0.48 \| 300 \| 7.8489 \| \| 2.3781 \| 0.64 \| 400 \| 6.9163 \| \| 2.2011 \| 0.8 \| 500 \| 5.8360 \| \| 1.9613 \| 0.96 \| 600 \| 5.7762 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
AnnabelKaasik/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1583 \| 0.8 \| 1000 \| 1.0589 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
Khanmhmdi/detr-resnet-50_finetuned_fss1000	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_fss1000 This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "trench_coat", "starfish", "nintendo_3ds", "maotai_bottle", "vending_machine", "black_grouse", "nintendo_sp", "yoga_pad", "chicken", "drumstick", "cream", "handcuff", "pistachio", "cristo_redentor", "water_ouzel", "olive", "sealion", "mite_predator", "croquet_ball", "television", "perfume", "indian_cobra", "thrush", "sparrow", "pool_table", "rock_snake", "sewing_machine", "curlew", "tank", "howler_monkey", "mouthpiece", "tape_player", "cougar", "mcdonald_sign", "dowitcher", "diver", "capuchin", "roller_coaster", "bradypod", "streetcar", "parthenon", "cradle", "chess_bishop", "stove", "magpie_bird", "warplane", "frying_pan", "rabbit", "kitchen_knife", "beam_bridge", "blenheim_spaniel", "cleaver", "wandering_albatross", "shih-tzu", "pickelhaube", "medical_kit", "bus", "cushion", "polo_shirt", "sundial", "sandwich_cookies", "transport_helicopter", "strawberry", "hami_melon", "excavator", "besom", "handshower", "mount_fuji", "toothpaste", "cosmetic_brush", "fish", "shower_cap", "egg_tart", "raven", "mango", "trolleybus", "otter", "brush_pen", "mouse", "jellyfish", "yawl", "cigarette", "pufferfish", "truss_bridge", "dingo", "australian_terrier", "leafhopper", "whale", "beet_root", "saltshaker", "carriage", "chalk_brush", "grey_whale", "hammerhead_shark", "shakuhachi", "pinecone", "rhinoceros", "barber_shaver", "face_powder", "sombrero", "soap", "afghan_hound", "hatchet", "rally_car", "moist_proof_pad", "prairie_chicken", "motor_scooter", "monkey", "garlic", "guinea_pig", "spark_plug", "tofu", "macaque", "flying_snakes", "pingpong_ball", "pyraminx", "broom", "jacko_lantern", "fire_engine", "proboscis", "macaw", "folding_chair", "ladle", "baseball", "golf_ball", "ocarina", "snowmobile", "tiger_shark", "airliner", "loguat", "groenendael", "knife", "net_surface_shoes", "bucket", "kappa_logo", "assult_rifle", "siamang", "rocking_chair", "iguana", "dragonfly", "vacuum_cup", "head_cabbage", "potted_plant", "spiderman", "submarine", "sea_cucumber", "bat", "spider_monkey", "fox_squirrel", "cactus_ball", "impala", "windsor_tie", "crash_helmet", "vestment", "gyromitra", "donkey", "balloon", "african_elephant", "giant_schnauzer", "mitten", "bra", "tow_truck", "pepitas", "drum", "kazoo", "ski_mask", "african_crocodile", "washer", "andean_condor", "peregine_falcon", "miniskirt", "gorilla", "harmonica", "cheese", "ashtray", "brick_card", "sungnyemun", "chainsaw", "seal", "skull", "kwanyin", "snowplow", "tulip", "refrigerator", "soap_dispenser", "cheetah", "brick_tea", "pteropus", "hartebeest", "gypsy_moth", "tiger_cat", "apple", "apron", "wallaby", "lion", "bee_house", "electronic_toothbrush", "table_lamp", "stool", "shower_curtain", "boston_bull", "petri_dish", "marmot", "typewriter", "mashed_potato", "vulture", "air_strip", "sweatshirt", "nintendo_switch", "warehouse_tray", "wooden_boat", "chimpanzee", "cantilever_bridge", "thatch", "polecat", "electric_fan", "combination_lock", "cactus", "spade", "forklift", "stole", "scarerow", "coyote", "skua", "carbonara", "ladyfinger", "cheese_burger", "cabbage", "fennel_bulb", "thimble", "ceiling_fan", "bullet_train", "carrot", "sports_car", "water_bike", "conversion_plug", "chinese_knot", "hover_board", "mountain_tent", "bouzouki", "gecko", "cornet", "pencil_sharpener1", "bloodhound", "chicken_leg", "battery", "turnstile", "schooner", "neck_brace", "briard", "goldfish", "woodpecker", "brambling", "helicopter", "victor_icon", "swimming_trunk", "speaker", "artichoke", "swim_ring", "quail_egg", "chinese_date", "dinosaur", "egret", "stretcher", "monocycle", "cucumber", "baboon", "shumai", "narcissus", "ocicat", "wine_bottle", "patas", "onion", "cabbage_butterfly", "pspgo", "water_buffalo", "kobe_logo", "hammer", "canoe", "eft_newt", "snowball", "lycaenid_butterfly", "dishwasher", "doughnut", "bell", "christmas_stocking", "church", "sulphur_butterfly", "housefinch", "bald_eagle", "white_shark", "soymilk_machine", "shopping_cart", "giant_panda", "lipstick", "snake", "tokyo_tower", "waffle_iron", "globe", "wheelchair", "wardrobe", "yorkshire_terrier", "coffeepot", "mooli", "walnut", "pidan", "raccoon", "french_ball", "carousel", "cpu", "cairn", "rose", "seagull", "panda", "windmill", "sandwich", "bomb", "wafer", "hyena", "swab", "diaper", "har_gow", "wash_basin", "beaker", "chopsticks", "lady_slipper", "leggings", "brain_coral", "earplug", "car_mirror", "okra", "pay_phone", "seatbelt", "fire_screen", "bell_pepper", "mooncake", "eletrical_switch", "fork", "one-armed_bandit", "statue_liberty", "guitar", "ladybug", "hare", "wig", "arrow", "speedboat", "owl", "lacewing", "laptop", "lampshade", "fire_hydrant", "turtle", "cd", "siamese_cat", "bracelet", "spatula", "fish_eagle", "pokermon_ball", "twin_tower", "border_terrier", "mario", "barbell", "ptarmigan", "ruler", "lifeboat", "adidas_logo1", "pineapple", "esport_chair", "pill_bottle", "banana_boat", "night_snake", "brasscica", "bamboo_slip", "glider_flyingfish", "wooden_spoon", "park_bench", "dhole", "acorn", "meatloaf", "joystick", "iron_man", "phonograph", "projector", "ferret", "crocodile", "nike_logo", "banded_gecko", "violin", "digital_watch", "birdhouse", "black_stork", "wok", "crt_screen", "indri", "convertible", "potato_chips", "spider", "spotted_salamander", "gas_tank", "ostrich", "microwave", "gym_ball", "ruffed_grouse", "swimming_glasses", "ballpoint", "buckingham_palace", "cumquat", "eagle", "fan", "villa_savoye", "tebby_cat", "leeks", "muscle_car", "brown_bear", "garfish", "three-toed_sloth", "chihuahua", "psp", "frog", "fire_balloon", "red_fox", "dutch_oven", "tile_roof", "earphone1", "captain_america_shield", "coho", "scabbard", "haddock", "broccoli", "razor", "bulbul_bird", "shovel", "pen", "persian_cat", "trimaran", "indian_elephant", "zucchini", "microscope", "beer_glass", "bolete", "armour", "sock", "hotel_slipper", "rosehip", "coin", "minicooper", "polar_bear", "corn", "drake", "nail_scissor", "crane", "jay_bird", "fox", "electronic_stove", "marshmallow", "white_wolf", "doublebus", "spinach", "bedlington_terrier", "paint_brush", "tomb", "adidas_logo2", "mud_turtle", "ox", "chess_king", "beaver", "peanut", "harp", "downy_pitch", "parallel_bars", "loafer", "cardoon", "camel", "teddy", "vase", "golden_plover", "measuring_cup", "waffle", "obelisk", "airedale", "plate", "space_heater", "aubergine", "comb", "revolver", "tray", "warthog", "leaf_egg", "feather_clothes", "jackfruit", "coconut", "almond", "timber_wolf", "water_snake", "lorikeet", "stinkhorn", "american_staffordshire", "water_heater", "grey_fox", "orang", "bamboo_dragonfly", "crab", "german_pointer", "cn_tower", "adhensive_tape", "african_grey", "pubg_airdrop", "reflex_camera", "ruddy_turnstone", "bee", "punching_bag", "necklace", "cello", "flowerpot", "wall_clock", "lotus", "jacamar", "dugong", "beacon", "lemon", "carambola", "conch", "whippet", "prayer_rug", "pyramid_cube", "lionfish", "beagle", "kart", "poker", "rock_beauty", "drilling_platform", "printer", "squirrel_monkey", "leaf_fan", "angora", "ipad", "egg", "upright_piano", "espresso_maker", "toaster", "consomme", "arabian_camel", "cowboy_hat", "bulb", "dart", "fly", "handkerchief", "croissant", "scissors", "screw", "redheart", "police_car", "weasel", "cauliflower", "balance_beam", "nintendo_gba", "hair_razor", "stone_lion", "cocktail_shaker", "cocacola", "umbrella", "astronaut", "mule", "sydney_opera_house", "poached_egg", "usb", "backpack", "flat-coated_retriever", "english_setter", "pickup", "bagel", "iceberg", "ice_lolly", "lemur_catta", "chicken_wings", "oriole", "wagtail", "flying_disc", "toilet_tissue", "pubg_lvl3backpack", "pinwheel", "icecream", "leather_shoes", "avocado", "boxing_gloves", "nagoya_castle", "coffee_mug", "scroll_brush", "sunscreen", "motorbike", "cottontail", "aircraft_carrier", "military_vest", "spoonbill", "melon_seed", "rugby_ball", "gourd", "tiltrotor", "sponge", "banjo", "trilobite", "wild_boar", "dwarf_beans", "matchstick", "strainer", "piano_keyboard", "butterfly", "cableways", "cigar", "titi_monkey", "computer_mouse", "baseball_player", "ipod", "children_slide", "kit_fox", "wreck", "solar_dish", "llama", "gazelle", "bluetick", "moon", "french_fries", "hippo", "lobster", "chalk", "saluki", "cornmeal", "toucan", "stupa", "panpipe", "radio_telescope", "manatee", "soup_bowl", "koala", "feeder", "hang_glider", "triumphal_arch", "taro", "snowman", "rocket", "bear", "stonechat", "daisy", "puma_logo", "egyptian_cat", "throne", "nintendo_wiiu", "pretzel", "black_bear", "banana", "sarong", "jinrikisha", "goblet", "stop_sign", "hook", "pumpkin", "rubber_eraser", "eel", "surfboard", "smoothing_iron", "apple_icon", "band-aid", "paper_plane", "red_breasted_merganser", "flying_frog", "hair_drier", "pubg_lvl3helmet", "cup", "balance_weight", "cloud", "badger", "radio", "saxophone", "hock", "dart_target", "whistle", "suitcase", "paper_crane", "calculator", "mushroom", "marimba", "lhasa_apso", "baby", "white_stork", "f1_racing", "burj_al", "staffordshire", "plaice", "chickadee_bird", "camomile", "lark", "carton", "wallet", "beer_bottle", "studio_couch", "ac_ground", "armadillo", "pheasant", "pizza", "dough", "scorpion", "wrench", "equestrian_helmet", "kremlin", "space_shuttle", "coucal", "oyster", "parachute", "cricketball", "panther", "cathedrale_paris", "little_blue_heron", "red_wolf", "car_wheel", "shuriken", "toilet_seat", "ferrari911", "pear", "carp", "lynx", "chiffon_cake", "tiger", "charge_battery", "tractor", "keyboard", "chest", "echidna", "breast_pump", "mailbox", "tredmill", "espresso", "flute", "window_screen", "accordion", "flamingo", "american_alligator", "abe's_flyingfish", "louvre_pyramid", "toothbrush", "common_newt", "terrapin_turtle", "spring_scroll", "kite", "bowtie", "doormat", "steering_wheel", "wolf", "big_ben", "jet_aircraft", "syringe", "abacus", "monitor", "golfcart", "igloo", "cannon", "bottle_cap", "taj_mahal", "tomato", "langur", "skunk", "tennis_racket", "leatherback_turtle", "sturgeon", "stealth_aircraft", "harvester", "pomegranate", "teapot", "shotgun", "triceratops", "volleyball", "lesser_panda", "pyramid", "lettuce", "stapler", "mortar", "ab_wheel", "hen_of_the_woods", "raft", "goldfinch", "zebra", "sea_urchin", "motarboard", "chicory", "pomelo", "stingray", "crayon", "watermelon", "tunnel", "tresher", "steam_locomotive", "paper_towel", "black_swan", "radiator", "totem_pole", "mcdonald_uncle", "fur_coat", "samarra_mosque", "toilet_brush", "sloth_bear", "delta_wing", "snail", "swan", "sushi", "cuckoo", "witch_hat", "gliding_lizard", "missile", "sandbar", "quail", "clam", "school_bus", "red_bayberry", "may_bug", "iphone", "osprey", "flatworm", "earphone2", "modem", "parking_meter", "ambulance", "memory_stick", "killer_whale", "barometer", "nematode", "bushtit", "bustard", "coffin", "shift_gear", "envelope", "pillow", "kangaroo", "ironing_board", "papaya", "bath_ball", "partridge", "guacamole", "torii", "pencil_box", "airship", "arch_bridge", "eggnog", "garbage_truck", "clearwing_flyingfish", "taxi", "ladder", "stopwatch", "hotdog", "vine_snake", "potato", "pig", "santa_sledge", "canton_tower", "gas_pump", "hornbill", "lapwing", "candle", "flying_geckos", "dandie_dinmont", "jordan_logo", "anise", "yurt", "ginger", "agama", "american_chamelon", "sunglasses", "plastic_bag", "window_shade", "peacock", "screwdriver", "baseball_bat", "litchi", "meerkat", "strongbox", "grasshopper", "paddle", "roller_skate", "cassette", "snow_leopard", "cablestayed_bridge", "oiltank_car", "buckler", "orange", "skateboard", "basset", "water_polo", "hard_disk", "monarch_butterfly", "pumpkin_pie", "spoon", "bighorn_sheep", "colubus", "pingpong_racket", "hawthorn", "trailer_truck", "collar", "golden_retriever", "letter_opener", "sled", "can_opener", "dumbbell", "microsd", "hawk", "porcupine", "diamond", "kunai", "brick", "basketball", "loggerhead_turtle", "sulphur_crested", "great_wall", "celery", "photocopier", "file_cabinet", "squirrel", "hornet", "briefcase", "oil_filter", "blossom_card", "flying_squirrel", "mink", "rice_cooker", "crepe", "steak", "bee_eater", "power_drill", "reel", "stork", "ringlet_butterfly", "condor", "english_foxhound", "mongoose", "bittern", "albatross", "single_log", "rubick_cube", "quill_pen", "rain_barrel", "conveyor", "thor's_hammer", "wombat", "chandelier", "ac_wall", "running_shoe", "chess_queen", "redshank", "fig", "toilet_plunger", "cherry", "manx", "box_turtle", "bolotie", "whiptail", "boa_constrictor", "cricket", "bison", "wasp", "soccer_ball", "light_tube", "maraca", "telescope", "vacuum", "relay_stick", "leopard", "gibbon", "kinguin", "pencil_sharpener2", "tobacco_pipe", "digital_clock", "traffic_light", "bathtub", "quad_drone", "remote_control", "key", "tower_pisa", "ganeva_chair", "green_mamba", "chess_knight", "fountain", "ibex", "sniper_rifle", "vinyl", "hummingbird", "microphone", "cicada", "yonex_icon", "sandal", "oscilloscope", "lawn_mower", "hamster", "sleeping_bag", "arctic_fox", "platypus", "bassoon", "sidewinder", "anemone_fish", "recreational_vehicle", "goose", "water_tower", "persimmon", "garbage_can" ]
SkowKyubu/yolo	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # yolo This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/z4r0avog) # facebook-detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.1507 - Map: 0.3346 - Map 50: 0.5981 - Map 75: 0.3283 - Map Small: 0.1903 - Map Medium: 0.2508 - Map Large: 0.4752 - Mar 1: 0.3914 - Mar 10: 0.5305 - Mar 100: 0.5425 - Mar Small: 0.2821 - Mar Medium: 0.4111 - Mar Large: 0.705 - Map Coverall: 0.5884 - Mar 100 Coverall: 0.7253 - Map Face Shield: 0.3168 - Mar 100 Face Shield: 0.5818 - Map Gloves: 0.2028 - Mar 100 Gloves: 0.3934 - Map Goggles: 0.2102 - Mar 100 Goggles: 0.558 - Map Mask: 0.3549 - Mar 100 Mask: 0.454 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 3.616 \| 1.0 \| 107 \| 2.9380 \| 0.0046 \| 0.0138 \| 0.0018 \| 0.0012 \| 0.0002 \| 0.0048 \| 0.0166 \| 0.0632 \| 0.1049 \| 0.0024 \| 0.0187 \| 0.1312 \| 0.0223 \| 0.4442 \| 0.0 \| 0.0 \| 0.0002 \| 0.0361 \| 0.0 \| 0.0 \| 0.0007 \| 0.0444 \| \| 2.9914 \| 2.0 \| 214 \| 2.7489 \| 0.0138 \| 0.0444 \| 0.0055 \| 0.0018 \| 0.0007 \| 0.0142 \| 0.0292 \| 0.0888 \| 0.1125 \| 0.0024 \| 0.041 \| 0.1216 \| 0.0664 \| 0.4253 \| 0.0 \| 0.0 \| 0.0004 \| 0.0486 \| 0.0 \| 0.0 \| 0.002 \| 0.0884 \| \| 2.6785 \| 3.0 \| 321 \| 2.5782 \| 0.0167 \| 0.0534 \| 0.0071 \| 0.0016 \| 0.0005 \| 0.0177 \| 0.0367 \| 0.1002 \| 0.1182 \| 0.0032 \| 0.0382 \| 0.1326 \| 0.0808 \| 0.4584 \| 0.0 \| 0.0 \| 0.0003 \| 0.0475 \| 0.0 \| 0.0 \| 0.0022 \| 0.0848 \| \| 2.4754 \| 4.0 \| 428 \| 2.4978 \| 0.0229 \| 0.0639 \| 0.0099 \| 0.002 \| 0.0042 \| 0.0221 \| 0.0417 \| 0.1182 \| 0.1475 \| 0.0155 \| 0.058 \| 0.1507 \| 0.1019 \| 0.5318 \| 0.0 \| 0.0 \| 0.001 \| 0.0776 \| 0.0 \| 0.0 \| 0.0118 \| 0.1283 \| \| 2.2601 \| 5.0 \| 535 \| 2.2584 \| 0.0342 \| 0.0846 \| 0.0251 \| 0.0055 \| 0.0057 \| 0.038 \| 0.0602 \| 0.1313 \| 0.1686 \| 0.0127 \| 0.0815 \| 0.1747 \| 0.1557 \| 0.5558 \| 0.0068 \| 0.0273 \| 0.0016 \| 0.0995 \| 0.0 \| 0.0 \| 0.007 \| 0.1606 \| \| 2.1746 \| 6.0 \| 642 \| 2.0845 \| 0.0536 \| 0.1098 \| 0.0468 \| 0.006 \| 0.0156 \| 0.0545 \| 0.0926 \| 0.1654 \| 0.204 \| 0.0391 \| 0.1055 \| 0.2434 \| 0.2312 \| 0.5922 \| 0.0181 \| 0.0964 \| 0.0032 \| 0.1295 \| 0.0 \| 0.0 \| 0.0157 \| 0.202 \| \| 2.1412 \| 7.0 \| 749 \| 2.1784 \| 0.0398 \| 0.1094 \| 0.0225 \| 0.0064 \| 0.0174 \| 0.0399 \| 0.0913 \| 0.1778 \| 0.2043 \| 0.0126 \| 0.126 \| 0.2373 \| 0.1418 \| 0.5214 \| 0.0394 \| 0.1836 \| 0.0019 \| 0.1246 \| 0.0 \| 0.0 \| 0.0161 \| 0.1919 \| \| 2.094 \| 8.0 \| 856 \| 2.1212 \| 0.0477 \| 0.1209 \| 0.027 \| 0.0098 \| 0.0087 \| 0.0474 \| 0.0939 \| 0.18 \| 0.2051 \| 0.0503 \| 0.1222 \| 0.1987 \| 0.2067 \| 0.5435 \| 0.0102 \| 0.1382 \| 0.0043 \| 0.1355 \| 0.0 \| 0.0 \| 0.0175 \| 0.2081 \| \| 2.0231 \| 9.0 \| 963 \| 1.9452 \| 0.0592 \| 0.1348 \| 0.0455 \| 0.0093 \| 0.015 \| 0.0622 \| 0.1093 \| 0.2226 \| 0.2572 \| 0.0603 \| 0.1648 \| 0.2804 \| 0.2438 \| 0.6117 \| 0.013 \| 0.2182 \| 0.0085 \| 0.1995 \| 0.0007 \| 0.002 \| 0.0299 \| 0.2545 \| \| 1.9703 \| 10.0 \| 1070 \| 1.9528 \| 0.0678 \| 0.1508 \| 0.0521 \| 0.0077 \| 0.0232 \| 0.075 \| 0.1116 \| 0.2175 \| 0.2571 \| 0.0347 \| 0.1752 \| 0.2909 \| 0.2741 \| 0.6013 \| 0.0274 \| 0.2164 \| 0.0052 \| 0.1798 \| 0.0 \| 0.0 \| 0.0326 \| 0.2879 \| \| 1.9284 \| 11.0 \| 1177 \| 1.8626 \| 0.0843 \| 0.1763 \| 0.0776 \| 0.0263 \| 0.0162 \| 0.1032 \| 0.1378 \| 0.2587 \| 0.2928 \| 0.0701 \| 0.1837 \| 0.3684 \| 0.3527 \| 0.6273 \| 0.0321 \| 0.3509 \| 0.0057 \| 0.1902 \| 0.0001 \| 0.004 \| 0.0307 \| 0.2914 \| \| 1.85 \| 12.0 \| 1284 \| 1.8513 \| 0.1011 \| 0.207 \| 0.0878 \| 0.0073 \| 0.0384 \| 0.1079 \| 0.1468 \| 0.2539 \| 0.2795 \| 0.0377 \| 0.1753 \| 0.332 \| 0.3676 \| 0.6481 \| 0.0796 \| 0.2818 \| 0.0085 \| 0.1836 \| 0.0005 \| 0.012 \| 0.0492 \| 0.2722 \| \| 1.7521 \| 13.0 \| 1391 \| 1.7764 \| 0.105 \| 0.2085 \| 0.0924 \| 0.0254 \| 0.0396 \| 0.1046 \| 0.1551 \| 0.2772 \| 0.3019 \| 0.0707 \| 0.1945 \| 0.3994 \| 0.3992 \| 0.6416 \| 0.0502 \| 0.3018 \| 0.0142 \| 0.2306 \| 0.0008 \| 0.028 \| 0.0606 \| 0.3076 \| \| 1.6934 \| 14.0 \| 1498 \| 1.7405 \| 0.1262 \| 0.2607 \| 0.1124 \| 0.0365 \| 0.0736 \| 0.1244 \| 0.1663 \| 0.2984 \| 0.3249 \| 0.0764 \| 0.2257 \| 0.4122 \| 0.4113 \| 0.6506 \| 0.1122 \| 0.3527 \| 0.0099 \| 0.2224 \| 0.0025 \| 0.044 \| 0.0952 \| 0.3545 \| \| 1.6837 \| 15.0 \| 1605 \| 1.6992 \| 0.1192 \| 0.246 \| 0.1049 \| 0.0455 \| 0.063 \| 0.1248 \| 0.1809 \| 0.3105 \| 0.3314 \| 0.0763 \| 0.223 \| 0.4305 \| 0.3945 \| 0.6435 \| 0.1059 \| 0.3982 \| 0.0159 \| 0.235 \| 0.0062 \| 0.046 \| 0.0733 \| 0.3343 \| \| 1.6609 \| 16.0 \| 1712 \| 1.8307 \| 0.09 \| 0.2062 \| 0.0676 \| 0.0277 \| 0.0295 \| 0.107 \| 0.1515 \| 0.2788 \| 0.3128 \| 0.0831 \| 0.2117 \| 0.3951 \| 0.3372 \| 0.6084 \| 0.0415 \| 0.3091 \| 0.0089 \| 0.2426 \| 0.007 \| 0.08 \| 0.0553 \| 0.3237 \| \| 1.646 \| 17.0 \| 1819 \| 1.6690 \| 0.1319 \| 0.275 \| 0.1164 \| 0.051 \| 0.0747 \| 0.1526 \| 0.1989 \| 0.3525 \| 0.384 \| 0.123 \| 0.2814 \| 0.5094 \| 0.4002 \| 0.6247 \| 0.065 \| 0.4164 \| 0.0259 \| 0.2973 \| 0.0627 \| 0.216 \| 0.1058 \| 0.3657 \| \| 1.6016 \| 18.0 \| 1926 \| 1.6489 \| 0.1374 \| 0.3023 \| 0.1079 \| 0.0649 \| 0.0727 \| 0.171 \| 0.1936 \| 0.3339 \| 0.3578 \| 0.1163 \| 0.2523 \| 0.4989 \| 0.4021 \| 0.5877 \| 0.0671 \| 0.3782 \| 0.0228 \| 0.2798 \| 0.0493 \| 0.174 \| 0.1458 \| 0.3692 \| \| 1.5686 \| 19.0 \| 2033 \| 1.6094 \| 0.1497 \| 0.3101 \| 0.1236 \| 0.0418 \| 0.0841 \| 0.1755 \| 0.218 \| 0.3731 \| 0.4009 \| 0.1232 \| 0.2946 \| 0.5219 \| 0.4527 \| 0.6468 \| 0.0936 \| 0.4182 \| 0.0263 \| 0.2934 \| 0.0408 \| 0.266 \| 0.1349 \| 0.3803 \| \| 1.5312 \| 20.0 \| 2140 \| 1.5237 \| 0.1732 \| 0.3454 \| 0.1548 \| 0.0729 \| 0.1081 \| 0.2238 \| 0.2383 \| 0.3908 \| 0.4182 \| 0.2073 \| 0.2878 \| 0.5665 \| 0.4815 \| 0.6636 \| 0.1266 \| 0.44 \| 0.037 \| 0.3115 \| 0.0266 \| 0.288 \| 0.1941 \| 0.3879 \| \| 1.4907 \| 21.0 \| 2247 \| 1.5332 \| 0.1792 \| 0.3615 \| 0.152 \| 0.0759 \| 0.1167 \| 0.2209 \| 0.2473 \| 0.3973 \| 0.4221 \| 0.1877 \| 0.2963 \| 0.5843 \| 0.4678 \| 0.661 \| 0.104 \| 0.4491 \| 0.0496 \| 0.3071 \| 0.0722 \| 0.314 \| 0.2023 \| 0.3793 \| \| 1.4154 \| 22.0 \| 2354 \| 1.5248 \| 0.1742 \| 0.3551 \| 0.1524 \| 0.0867 \| 0.0917 \| 0.2499 \| 0.2541 \| 0.3974 \| 0.4196 \| 0.1728 \| 0.2843 \| 0.5959 \| 0.4828 \| 0.663 \| 0.0991 \| 0.4455 \| 0.0502 \| 0.294 \| 0.057 \| 0.342 \| 0.182 \| 0.3535 \| \| 1.4162 \| 23.0 \| 2461 \| 1.4762 \| 0.1931 \| 0.3804 \| 0.1741 \| 0.0783 \| 0.1263 \| 0.2436 \| 0.2567 \| 0.3938 \| 0.4137 \| 0.15 \| 0.279 \| 0.5965 \| 0.4868 \| 0.6935 \| 0.1134 \| 0.4182 \| 0.0542 \| 0.2863 \| 0.0953 \| 0.312 \| 0.2156 \| 0.3586 \| \| 1.4624 \| 24.0 \| 2568 \| 1.4757 \| 0.173 \| 0.3577 \| 0.1468 \| 0.0352 \| 0.0993 \| 0.2231 \| 0.2258 \| 0.3952 \| 0.4155 \| 0.1291 \| 0.2987 \| 0.5419 \| 0.4726 \| 0.6552 \| 0.0822 \| 0.42 \| 0.0489 \| 0.3186 \| 0.0408 \| 0.312 \| 0.2207 \| 0.3717 \| \| 1.4275 \| 25.0 \| 2675 \| 1.5116 \| 0.1759 \| 0.3586 \| 0.1602 \| 0.0576 \| 0.13 \| 0.2456 \| 0.24 \| 0.3856 \| 0.4077 \| 0.1952 \| 0.2959 \| 0.5276 \| 0.4527 \| 0.6643 \| 0.1087 \| 0.4218 \| 0.057 \| 0.3169 \| 0.0409 \| 0.278 \| 0.2202 \| 0.3576 \| \| 1.4002 \| 26.0 \| 2782 \| 1.4787 \| 0.1954 \| 0.3737 \| 0.1821 \| 0.0338 \| 0.1296 \| 0.2585 \| 0.2523 \| 0.4039 \| 0.4237 \| 0.1232 \| 0.2934 \| 0.6087 \| 0.4823 \| 0.6701 \| 0.1562 \| 0.4164 \| 0.0361 \| 0.2973 \| 0.0473 \| 0.37 \| 0.2552 \| 0.3646 \| \| 1.3991 \| 27.0 \| 2889 \| 1.5002 \| 0.1946 \| 0.3787 \| 0.1846 \| 0.0501 \| 0.1434 \| 0.2684 \| 0.2503 \| 0.4031 \| 0.4282 \| 0.1418 \| 0.3042 \| 0.5904 \| 0.4605 \| 0.6675 \| 0.1255 \| 0.4236 \| 0.0438 \| 0.2907 \| 0.0891 \| 0.382 \| 0.2543 \| 0.3773 \| \| 1.3786 \| 28.0 \| 2996 \| 1.4114 \| 0.2167 \| 0.4296 \| 0.2011 \| 0.055 \| 0.1596 \| 0.2997 \| 0.2711 \| 0.4271 \| 0.4439 \| 0.1325 \| 0.3265 \| 0.611 \| 0.5044 \| 0.6929 \| 0.1611 \| 0.4309 \| 0.0871 \| 0.3322 \| 0.0671 \| 0.382 \| 0.2639 \| 0.3813 \| \| 1.3221 \| 29.0 \| 3103 \| 1.4392 \| 0.2103 \| 0.4118 \| 0.19 \| 0.0658 \| 0.1463 \| 0.2814 \| 0.2744 \| 0.4262 \| 0.4473 \| 0.1744 \| 0.3121 \| 0.6248 \| 0.4909 \| 0.6864 \| 0.1448 \| 0.4491 \| 0.0675 \| 0.2929 \| 0.0811 \| 0.432 \| 0.267 \| 0.3763 \| \| 1.3073 \| 30.0 \| 3210 \| 1.3707 \| 0.2243 \| 0.4368 \| 0.1973 \| 0.0558 \| 0.1679 \| 0.3023 \| 0.2749 \| 0.4348 \| 0.456 \| 0.163 \| 0.3312 \| 0.6184 \| 0.5199 \| 0.6987 \| 0.1462 \| 0.4545 \| 0.0965 \| 0.3536 \| 0.0996 \| 0.394 \| 0.2592 \| 0.3793 \| \| 1.2889 \| 31.0 \| 3317 \| 1.3956 \| 0.2121 \| 0.4279 \| 0.1948 \| 0.0479 \| 0.1462 \| 0.2967 \| 0.2589 \| 0.4308 \| 0.4478 \| 0.1559 \| 0.3281 \| 0.6031 \| 0.4899 \| 0.6779 \| 0.1249 \| 0.4309 \| 0.0952 \| 0.3372 \| 0.111 \| 0.43 \| 0.2395 \| 0.3631 \| \| 1.3164 \| 32.0 \| 3424 \| 1.4315 \| 0.2094 \| 0.4238 \| 0.1815 \| 0.0341 \| 0.1474 \| 0.2662 \| 0.2662 \| 0.4129 \| 0.4247 \| 0.0857 \| 0.3045 \| 0.5793 \| 0.4874 \| 0.6766 \| 0.1177 \| 0.3927 \| 0.0835 \| 0.3027 \| 0.1297 \| 0.396 \| 0.2288 \| 0.3556 \| \| 1.3735 \| 33.0 \| 3531 \| 1.3750 \| 0.2322 \| 0.4495 \| 0.223 \| 0.0762 \| 0.1597 \| 0.297 \| 0.3006 \| 0.4463 \| 0.4602 \| 0.1737 \| 0.3294 \| 0.6188 \| 0.5351 \| 0.7006 \| 0.1626 \| 0.4545 \| 0.0956 \| 0.3251 \| 0.1178 \| 0.452 \| 0.2498 \| 0.3687 \| \| 1.2856 \| 34.0 \| 3638 \| 1.4287 \| 0.2257 \| 0.4418 \| 0.2076 \| 0.0646 \| 0.1505 \| 0.3169 \| 0.2616 \| 0.4221 \| 0.4429 \| 0.1341 \| 0.3274 \| 0.5719 \| 0.5082 \| 0.6747 \| 0.1528 \| 0.4509 \| 0.11 \| 0.3082 \| 0.0935 \| 0.414 \| 0.264 \| 0.3667 \| \| 1.2558 \| 35.0 \| 3745 \| 1.3187 \| 0.2401 \| 0.4657 \| 0.2202 \| 0.0721 \| 0.1688 \| 0.3091 \| 0.3008 \| 0.4597 \| 0.4756 \| 0.1618 \| 0.3272 \| 0.6627 \| 0.5188 \| 0.7032 \| 0.1495 \| 0.4891 \| 0.1232 \| 0.3415 \| 0.1313 \| 0.45 \| 0.2776 \| 0.3939 \| \| 1.1862 \| 36.0 \| 3852 \| 1.2866 \| 0.2588 \| 0.4753 \| 0.2457 \| 0.0634 \| 0.1939 \| 0.3323 \| 0.3199 \| 0.4643 \| 0.4754 \| 0.1514 \| 0.342 \| 0.6447 \| 0.5416 \| 0.7006 \| 0.213 \| 0.4745 \| 0.13 \| 0.3432 \| 0.1414 \| 0.474 \| 0.2682 \| 0.3848 \| \| 1.2361 \| 37.0 \| 3959 \| 1.3003 \| 0.2531 \| 0.4754 \| 0.2405 \| 0.0619 \| 0.191 \| 0.3391 \| 0.3006 \| 0.4612 \| 0.4814 \| 0.1487 \| 0.3592 \| 0.6214 \| 0.5226 \| 0.7039 \| 0.2095 \| 0.4873 \| 0.1069 \| 0.3678 \| 0.1434 \| 0.46 \| 0.2832 \| 0.3879 \| \| 1.1919 \| 38.0 \| 4066 \| 1.3157 \| 0.2411 \| 0.4507 \| 0.2127 \| 0.1031 \| 0.1691 \| 0.3298 \| 0.3007 \| 0.4654 \| 0.4887 \| 0.1863 \| 0.3628 \| 0.6209 \| 0.5242 \| 0.713 \| 0.1632 \| 0.4927 \| 0.1206 \| 0.3699 \| 0.1164 \| 0.478 \| 0.2812 \| 0.3899 \| \| 1.2089 \| 39.0 \| 4173 \| 1.3253 \| 0.2378 \| 0.4618 \| 0.2139 \| 0.06 \| 0.1662 \| 0.3619 \| 0.2932 \| 0.456 \| 0.4747 \| 0.1647 \| 0.3502 \| 0.6456 \| 0.5365 \| 0.687 \| 0.1477 \| 0.44 \| 0.1341 \| 0.3612 \| 0.1181 \| 0.512 \| 0.2526 \| 0.3732 \| \| 1.1989 \| 40.0 \| 4280 \| 1.3079 \| 0.243 \| 0.4632 \| 0.2212 \| 0.09 \| 0.1745 \| 0.3635 \| 0.2888 \| 0.4601 \| 0.4815 \| 0.1716 \| 0.3643 \| 0.6676 \| 0.5474 \| 0.711 \| 0.1511 \| 0.5055 \| 0.1275 \| 0.3596 \| 0.1169 \| 0.46 \| 0.2721 \| 0.3712 \| \| 1.123 \| 41.0 \| 4387 \| 1.2717 \| 0.2545 \| 0.4648 \| 0.2389 \| 0.0865 \| 0.1746 \| 0.3768 \| 0.3137 \| 0.4805 \| 0.5019 \| 0.2341 \| 0.3727 \| 0.6742 \| 0.5567 \| 0.7182 \| 0.1537 \| 0.5127 \| 0.1243 \| 0.3738 \| 0.1443 \| 0.494 \| 0.2933 \| 0.4106 \| \| 1.1247 \| 42.0 \| 4494 \| 1.3207 \| 0.2547 \| 0.4802 \| 0.2359 \| 0.0646 \| 0.1709 \| 0.3638 \| 0.3091 \| 0.4584 \| 0.4749 \| 0.1666 \| 0.339 \| 0.6682 \| 0.5703 \| 0.7162 \| 0.2116 \| 0.5055 \| 0.1441 \| 0.341 \| 0.1132 \| 0.474 \| 0.2342 \| 0.3379 \| \| 1.127 \| 43.0 \| 4601 \| 1.2355 \| 0.2651 \| 0.5025 \| 0.2454 \| 0.0659 \| 0.2016 \| 0.3784 \| 0.3272 \| 0.4851 \| 0.5009 \| 0.1616 \| 0.3671 \| 0.691 \| 0.5439 \| 0.7188 \| 0.2047 \| 0.5164 \| 0.1539 \| 0.382 \| 0.1291 \| 0.49 \| 0.2942 \| 0.3975 \| \| 1.1019 \| 44.0 \| 4708 \| 1.2395 \| 0.2684 \| 0.4895 \| 0.2566 \| 0.0892 \| 0.1954 \| 0.3819 \| 0.3367 \| 0.4887 \| 0.5068 \| 0.183 \| 0.3718 \| 0.6757 \| 0.5447 \| 0.7195 \| 0.2254 \| 0.52 \| 0.1424 \| 0.3781 \| 0.1198 \| 0.506 \| 0.3096 \| 0.4106 \| \| 1.1069 \| 45.0 \| 4815 \| 1.2494 \| 0.2673 \| 0.4805 \| 0.2646 \| 0.0882 \| 0.1876 \| 0.3995 \| 0.3261 \| 0.4852 \| 0.5081 \| 0.2162 \| 0.3564 \| 0.7021 \| 0.5524 \| 0.7149 \| 0.2068 \| 0.5255 \| 0.1756 \| 0.3907 \| 0.1071 \| 0.498 \| 0.2945 \| 0.4116 \| \| 1.0929 \| 46.0 \| 4922 \| 1.2631 \| 0.2738 \| 0.5088 \| 0.2541 \| 0.0801 \| 0.1949 \| 0.375 \| 0.3189 \| 0.4701 \| 0.4968 \| 0.2285 \| 0.3514 \| 0.6741 \| 0.5683 \| 0.7136 \| 0.2407 \| 0.5109 \| 0.184 \| 0.3852 \| 0.1181 \| 0.49 \| 0.2578 \| 0.3843 \| \| 1.1297 \| 47.0 \| 5029 \| 1.2737 \| 0.263 \| 0.4986 \| 0.2373 \| 0.0855 \| 0.1837 \| 0.3832 \| 0.315 \| 0.4695 \| 0.4879 \| 0.2051 \| 0.3548 \| 0.6878 \| 0.564 \| 0.7052 \| 0.2275 \| 0.5182 \| 0.1486 \| 0.3481 \| 0.0788 \| 0.462 \| 0.2959 \| 0.4061 \| \| 1.1593 \| 48.0 \| 5136 \| 1.2690 \| 0.2644 \| 0.5107 \| 0.2612 \| 0.0745 \| 0.1985 \| 0.3765 \| 0.312 \| 0.4634 \| 0.4813 \| 0.1852 \| 0.3491 \| 0.6642 \| 0.5391 \| 0.7019 \| 0.2197 \| 0.5073 \| 0.1748 \| 0.3601 \| 0.1367 \| 0.466 \| 0.2516 \| 0.3712 \| \| 1.1372 \| 49.0 \| 5243 \| 1.2779 \| 0.2707 \| 0.4987 \| 0.2736 \| 0.0876 \| 0.1936 \| 0.4004 \| 0.3236 \| 0.4708 \| 0.489 \| 0.2231 \| 0.3449 \| 0.6683 \| 0.5527 \| 0.7182 \| 0.2438 \| 0.5 \| 0.1566 \| 0.3667 \| 0.1353 \| 0.466 \| 0.2652 \| 0.3939 \| \| 1.0796 \| 50.0 \| 5350 \| 1.1901 \| 0.2915 \| 0.5427 \| 0.2927 \| 0.0752 \| 0.2182 \| 0.428 \| 0.336 \| 0.4896 \| 0.5065 \| 0.1738 \| 0.3675 \| 0.6844 \| 0.5609 \| 0.7253 \| 0.2496 \| 0.5073 \| 0.1813 \| 0.3923 \| 0.1545 \| 0.496 \| 0.3109 \| 0.4116 \| \| 1.0472 \| 51.0 \| 5457 \| 1.2350 \| 0.2861 \| 0.5365 \| 0.2647 \| 0.0842 \| 0.2116 \| 0.4284 \| 0.3274 \| 0.4874 \| 0.4988 \| 0.1772 \| 0.3617 \| 0.6809 \| 0.5501 \| 0.7136 \| 0.247 \| 0.5018 \| 0.1633 \| 0.3339 \| 0.1554 \| 0.526 \| 0.315 \| 0.4187 \| \| 1.0605 \| 52.0 \| 5564 \| 1.1940 \| 0.2927 \| 0.5423 \| 0.2773 \| 0.1478 \| 0.2135 \| 0.429 \| 0.3472 \| 0.49 \| 0.5068 \| 0.2497 \| 0.3565 \| 0.6921 \| 0.5544 \| 0.7097 \| 0.2547 \| 0.4818 \| 0.1827 \| 0.3913 \| 0.1602 \| 0.528 \| 0.3113 \| 0.4232 \| \| 1.0506 \| 53.0 \| 5671 \| 1.2127 \| 0.2751 \| 0.5239 \| 0.2555 \| 0.101 \| 0.1945 \| 0.4041 \| 0.3218 \| 0.4813 \| 0.4952 \| 0.2365 \| 0.3461 \| 0.6708 \| 0.5685 \| 0.724 \| 0.2394 \| 0.4873 \| 0.1758 \| 0.3634 \| 0.1136 \| 0.52 \| 0.2784 \| 0.3813 \| \| 1.053 \| 54.0 \| 5778 \| 1.1906 \| 0.2994 \| 0.5424 \| 0.2941 \| 0.1302 \| 0.213 \| 0.4167 \| 0.3406 \| 0.5033 \| 0.5241 \| 0.2372 \| 0.3795 \| 0.6998 \| 0.5648 \| 0.7266 \| 0.2552 \| 0.5273 \| 0.1795 \| 0.3732 \| 0.176 \| 0.564 \| 0.3217 \| 0.4293 \| \| 1.0142 \| 55.0 \| 5885 \| 1.1860 \| 0.2937 \| 0.5326 \| 0.28 \| 0.1125 \| 0.2158 \| 0.4177 \| 0.3366 \| 0.4924 \| 0.5081 \| 0.2158 \| 0.3796 \| 0.6679 \| 0.567 \| 0.7195 \| 0.2523 \| 0.5018 \| 0.1687 \| 0.3765 \| 0.1754 \| 0.534 \| 0.305 \| 0.4086 \| \| 1.0391 \| 56.0 \| 5992 \| 1.1970 \| 0.2856 \| 0.5244 \| 0.2672 \| 0.106 \| 0.2012 \| 0.4148 \| 0.3396 \| 0.4901 \| 0.5089 \| 0.2218 \| 0.3697 \| 0.6807 \| 0.561 \| 0.7065 \| 0.2175 \| 0.4982 \| 0.1701 \| 0.3661 \| 0.1658 \| 0.548 \| 0.3133 \| 0.4258 \| \| 1.0031 \| 57.0 \| 6099 \| 1.1818 \| 0.2964 \| 0.5385 \| 0.2752 \| 0.1798 \| 0.2196 \| 0.4145 \| 0.3537 \| 0.5142 \| 0.5287 \| 0.3015 \| 0.3943 \| 0.6906 \| 0.5677 \| 0.7247 \| 0.2248 \| 0.5382 \| 0.1842 \| 0.3809 \| 0.17 \| 0.562 \| 0.3353 \| 0.4379 \| \| 0.9794 \| 58.0 \| 6206 \| 1.1965 \| 0.2903 \| 0.5258 \| 0.2852 \| 0.1404 \| 0.2064 \| 0.422 \| 0.3436 \| 0.4984 \| 0.5121 \| 0.2596 \| 0.3677 \| 0.6908 \| 0.5615 \| 0.7149 \| 0.2557 \| 0.5091 \| 0.155 \| 0.3656 \| 0.1543 \| 0.536 \| 0.3247 \| 0.4348 \| \| 1.0123 \| 59.0 \| 6313 \| 1.1943 \| 0.2831 \| 0.5389 \| 0.2672 \| 0.1388 \| 0.213 \| 0.4055 \| 0.3503 \| 0.5034 \| 0.5191 \| 0.2761 \| 0.3874 \| 0.6878 \| 0.5536 \| 0.711 \| 0.2217 \| 0.52 \| 0.1557 \| 0.3672 \| 0.1714 \| 0.562 \| 0.3133 \| 0.4354 \| \| 0.9814 \| 60.0 \| 6420 \| 1.2012 \| 0.303 \| 0.5553 \| 0.2868 \| 0.1351 \| 0.2209 \| 0.4293 \| 0.3507 \| 0.5019 \| 0.5164 \| 0.2826 \| 0.3747 \| 0.6823 \| 0.5686 \| 0.7104 \| 0.2642 \| 0.5036 \| 0.1664 \| 0.3732 \| 0.198 \| 0.56 \| 0.3177 \| 0.4348 \| \| 0.9583 \| 61.0 \| 6527 \| 1.1838 \| 0.3041 \| 0.5548 \| 0.305 \| 0.1338 \| 0.2199 \| 0.4376 \| 0.3457 \| 0.4977 \| 0.5123 \| 0.2561 \| 0.3739 \| 0.6789 \| 0.5853 \| 0.724 \| 0.2694 \| 0.5164 \| 0.193 \| 0.3836 \| 0.1766 \| 0.522 \| 0.2962 \| 0.4157 \| \| 0.9506 \| 62.0 \| 6634 \| 1.1634 \| 0.3013 \| 0.5503 \| 0.2856 \| 0.1514 \| 0.2189 \| 0.4223 \| 0.3473 \| 0.5123 \| 0.5251 \| 0.2774 \| 0.3852 \| 0.6884 \| 0.5762 \| 0.7117 \| 0.2389 \| 0.5345 \| 0.1946 \| 0.3896 \| 0.1656 \| 0.542 \| 0.3309 \| 0.4475 \| \| 0.9914 \| 63.0 \| 6741 \| 1.1681 \| 0.3051 \| 0.5439 \| 0.2905 \| 0.1401 \| 0.2514 \| 0.4296 \| 0.3476 \| 0.5 \| 0.5137 \| 0.2761 \| 0.4054 \| 0.6728 \| 0.5816 \| 0.7143 \| 0.2752 \| 0.5436 \| 0.1746 \| 0.3656 \| 0.1677 \| 0.518 \| 0.3264 \| 0.4268 \| \| 0.9516 \| 64.0 \| 6848 \| 1.1811 \| 0.2933 \| 0.532 \| 0.2921 \| 0.1145 \| 0.2285 \| 0.4111 \| 0.3384 \| 0.4869 \| 0.5034 \| 0.2024 \| 0.38 \| 0.6794 \| 0.5576 \| 0.7019 \| 0.2721 \| 0.5291 \| 0.1595 \| 0.3579 \| 0.1524 \| 0.502 \| 0.3251 \| 0.4263 \| \| 0.9232 \| 65.0 \| 6955 \| 1.1625 \| 0.304 \| 0.551 \| 0.2776 \| 0.1706 \| 0.2403 \| 0.4162 \| 0.3477 \| 0.5064 \| 0.5193 \| 0.2514 \| 0.3986 \| 0.6958 \| 0.5644 \| 0.7188 \| 0.2713 \| 0.5327 \| 0.1806 \| 0.388 \| 0.1681 \| 0.526 \| 0.3357 \| 0.4308 \| \| 0.9479 \| 66.0 \| 7062 \| 1.1899 \| 0.3017 \| 0.5539 \| 0.2705 \| 0.157 \| 0.2135 \| 0.4417 \| 0.36 \| 0.5122 \| 0.5277 \| 0.2622 \| 0.3857 \| 0.7181 \| 0.5725 \| 0.7136 \| 0.2637 \| 0.5636 \| 0.173 \| 0.3814 \| 0.1876 \| 0.56 \| 0.3117 \| 0.4197 \| \| 0.9041 \| 67.0 \| 7169 \| 1.1758 \| 0.3142 \| 0.5585 \| 0.3089 \| 0.1431 \| 0.2326 \| 0.4294 \| 0.3619 \| 0.5049 \| 0.5161 \| 0.2519 \| 0.3782 \| 0.6862 \| 0.5954 \| 0.7266 \| 0.2947 \| 0.5182 \| 0.1781 \| 0.3617 \| 0.1817 \| 0.558 \| 0.3213 \| 0.4162 \| \| 0.9385 \| 68.0 \| 7276 \| 1.1483 \| 0.3169 \| 0.5782 \| 0.2985 \| 0.1935 \| 0.232 \| 0.4517 \| 0.3714 \| 0.5241 \| 0.5357 \| 0.2918 \| 0.3976 \| 0.7021 \| 0.5802 \| 0.7312 \| 0.2987 \| 0.5636 \| 0.1982 \| 0.3896 \| 0.1854 \| 0.566 \| 0.3218 \| 0.4283 \| \| 0.9177 \| 69.0 \| 7383 \| 1.1799 \| 0.3144 \| 0.5645 \| 0.3005 \| 0.2049 \| 0.2287 \| 0.456 \| 0.3629 \| 0.5138 \| 0.5294 \| 0.3176 \| 0.3806 \| 0.6951 \| 0.5789 \| 0.7227 \| 0.2856 \| 0.5655 \| 0.1928 \| 0.3852 \| 0.1902 \| 0.544 \| 0.3246 \| 0.4298 \| \| 0.8781 \| 70.0 \| 7490 \| 1.1637 \| 0.3114 \| 0.5637 \| 0.2894 \| 0.1957 \| 0.2345 \| 0.4385 \| 0.3655 \| 0.5149 \| 0.5293 \| 0.3021 \| 0.3919 \| 0.6927 \| 0.58 \| 0.7331 \| 0.2764 \| 0.5473 \| 0.1943 \| 0.3858 \| 0.191 \| 0.55 \| 0.3151 \| 0.4303 \| \| 0.9337 \| 71.0 \| 7597 \| 1.1627 \| 0.3156 \| 0.5643 \| 0.3023 \| 0.1742 \| 0.229 \| 0.4506 \| 0.3683 \| 0.517 \| 0.5322 \| 0.302 \| 0.3825 \| 0.7129 \| 0.5811 \| 0.7357 \| 0.2999 \| 0.54 \| 0.1897 \| 0.3831 \| 0.1859 \| 0.578 \| 0.3212 \| 0.4242 \| \| 0.8986 \| 72.0 \| 7704 \| 1.1679 \| 0.3119 \| 0.5658 \| 0.2888 \| 0.1704 \| 0.24 \| 0.4547 \| 0.3554 \| 0.5196 \| 0.5338 \| 0.2748 \| 0.3949 \| 0.7075 \| 0.5799 \| 0.7286 \| 0.3 \| 0.5855 \| 0.1882 \| 0.3896 \| 0.1744 \| 0.534 \| 0.317 \| 0.4313 \| \| 0.8766 \| 73.0 \| 7811 \| 1.1545 \| 0.319 \| 0.5724 \| 0.2952 \| 0.1869 \| 0.2428 \| 0.4541 \| 0.3684 \| 0.5239 \| 0.5388 \| 0.2905 \| 0.4015 \| 0.7063 \| 0.5834 \| 0.7325 \| 0.2992 \| 0.5782 \| 0.1877 \| 0.3913 \| 0.2001 \| 0.564 \| 0.3247 \| 0.4283 \| \| 0.8668 \| 74.0 \| 7918 \| 1.1662 \| 0.3178 \| 0.5811 \| 0.3096 \| 0.1839 \| 0.2384 \| 0.4517 \| 0.3609 \| 0.5171 \| 0.5335 \| 0.2927 \| 0.3982 \| 0.6937 \| 0.5824 \| 0.7344 \| 0.3004 \| 0.5582 \| 0.1927 \| 0.3863 \| 0.1833 \| 0.552 \| 0.3304 \| 0.4364 \| \| 0.9248 \| 75.0 \| 8025 \| 1.1544 \| 0.3152 \| 0.5751 \| 0.2907 \| 0.1729 \| 0.2337 \| 0.444 \| 0.3545 \| 0.5134 \| 0.5281 \| 0.2933 \| 0.3852 \| 0.705 \| 0.5802 \| 0.726 \| 0.302 \| 0.5473 \| 0.1915 \| 0.3891 \| 0.1634 \| 0.528 \| 0.3389 \| 0.45 \| \| 0.8511 \| 76.0 \| 8132 \| 1.1887 \| 0.3263 \| 0.5907 \| 0.3062 \| 0.1854 \| 0.2392 \| 0.4552 \| 0.3585 \| 0.5155 \| 0.5295 \| 0.2971 \| 0.3832 \| 0.7044 \| 0.5832 \| 0.7227 \| 0.3305 \| 0.5691 \| 0.1884 \| 0.3678 \| 0.1977 \| 0.542 \| 0.3315 \| 0.446 \| \| 0.8675 \| 77.0 \| 8239 \| 1.1739 \| 0.3276 \| 0.5954 \| 0.3103 \| 0.1956 \| 0.2426 \| 0.4537 \| 0.361 \| 0.5207 \| 0.5333 \| 0.2866 \| 0.3944 \| 0.6991 \| 0.5889 \| 0.7286 \| 0.322 \| 0.5655 \| 0.2014 \| 0.3825 \| 0.1879 \| 0.54 \| 0.3379 \| 0.45 \| \| 0.821 \| 78.0 \| 8346 \| 1.1699 \| 0.3277 \| 0.5932 \| 0.313 \| 0.2021 \| 0.2523 \| 0.4642 \| 0.3718 \| 0.5263 \| 0.5403 \| 0.283 \| 0.4125 \| 0.7088 \| 0.5832 \| 0.7299 \| 0.3294 \| 0.5745 \| 0.2013 \| 0.4011 \| 0.1897 \| 0.552 \| 0.3347 \| 0.4439 \| \| 0.8467 \| 79.0 \| 8453 \| 1.1643 \| 0.332 \| 0.5979 \| 0.3249 \| 0.2042 \| 0.2473 \| 0.4766 \| 0.3875 \| 0.5246 \| 0.5332 \| 0.3188 \| 0.4013 \| 0.6999 \| 0.5873 \| 0.7318 \| 0.3269 \| 0.5655 \| 0.2038 \| 0.3836 \| 0.2126 \| 0.544 \| 0.3296 \| 0.4409 \| \| 0.8951 \| 80.0 \| 8560 \| 1.1533 \| 0.3359 \| 0.5938 \| 0.3165 \| 0.2187 \| 0.2461 \| 0.4588 \| 0.3917 \| 0.5416 \| 0.5544 \| 0.3381 \| 0.4132 \| 0.7085 \| 0.5918 \| 0.737 \| 0.3356 \| 0.5836 \| 0.1981 \| 0.3967 \| 0.2108 \| 0.604 \| 0.3433 \| 0.4505 \| \| 0.8303 \| 81.0 \| 8667 \| 1.1637 \| 0.3298 \| 0.5901 \| 0.306 \| 0.1872 \| 0.244 \| 0.4758 \| 0.3891 \| 0.5293 \| 0.5387 \| 0.279 \| 0.405 \| 0.7137 \| 0.5887 \| 0.7312 \| 0.3384 \| 0.5727 \| 0.1918 \| 0.3776 \| 0.1974 \| 0.57 \| 0.3327 \| 0.4419 \| \| 0.8386 \| 82.0 \| 8774 \| 1.1539 \| 0.3293 \| 0.5917 \| 0.3013 \| 0.1962 \| 0.2435 \| 0.4732 \| 0.3916 \| 0.5291 \| 0.5404 \| 0.2927 \| 0.4116 \| 0.7092 \| 0.5858 \| 0.7299 \| 0.3162 \| 0.5764 \| 0.1995 \| 0.3891 \| 0.2015 \| 0.556 \| 0.3432 \| 0.4505 \| \| 0.809 \| 83.0 \| 8881 \| 1.1587 \| 0.3284 \| 0.5927 \| 0.3001 \| 0.1922 \| 0.2476 \| 0.4659 \| 0.3861 \| 0.5219 \| 0.5347 \| 0.2976 \| 0.4068 \| 0.6911 \| 0.5829 \| 0.726 \| 0.3299 \| 0.5818 \| 0.1993 \| 0.3918 \| 0.1948 \| 0.54 \| 0.3351 \| 0.4338 \| \| 0.8205 \| 84.0 \| 8988 \| 1.1508 \| 0.3333 \| 0.5942 \| 0.3156 \| 0.2093 \| 0.2456 \| 0.4866 \| 0.3853 \| 0.5314 \| 0.5429 \| 0.3255 \| 0.4043 \| 0.7107 \| 0.5863 \| 0.7279 \| 0.3185 \| 0.5691 \| 0.203 \| 0.3885 \| 0.2253 \| 0.592 \| 0.3335 \| 0.4369 \| \| 0.8459 \| 85.0 \| 9095 \| 1.1513 \| 0.335 \| 0.5963 \| 0.3219 \| 0.2008 \| 0.2476 \| 0.4894 \| 0.3915 \| 0.527 \| 0.537 \| 0.3072 \| 0.3987 \| 0.7064 \| 0.5833 \| 0.7234 \| 0.3246 \| 0.5709 \| 0.2059 \| 0.3814 \| 0.2144 \| 0.562 \| 0.3465 \| 0.4475 \| \| 0.8008 \| 86.0 \| 9202 \| 1.1530 \| 0.3245 \| 0.5927 \| 0.3062 \| 0.1832 \| 0.2477 \| 0.4705 \| 0.3825 \| 0.5248 \| 0.5359 \| 0.2794 \| 0.41 \| 0.6952 \| 0.5836 \| 0.7299 \| 0.3002 \| 0.5709 \| 0.1856 \| 0.3749 \| 0.2223 \| 0.57 \| 0.3308 \| 0.4338 \| \| 0.7936 \| 87.0 \| 9309 \| 1.1558 \| 0.3266 \| 0.5861 \| 0.3079 \| 0.1893 \| 0.2374 \| 0.4833 \| 0.3848 \| 0.5226 \| 0.5357 \| 0.2929 \| 0.4 \| 0.7015 \| 0.5788 \| 0.7299 \| 0.3111 \| 0.5745 \| 0.1929 \| 0.3814 \| 0.2219 \| 0.56 \| 0.3281 \| 0.4328 \| \| 0.7991 \| 88.0 \| 9416 \| 1.1667 \| 0.3246 \| 0.5969 \| 0.2974 \| 0.167 \| 0.2393 \| 0.49 \| 0.379 \| 0.5165 \| 0.53 \| 0.2649 \| 0.4023 \| 0.6997 \| 0.5824 \| 0.726 \| 0.3114 \| 0.5636 \| 0.1853 \| 0.3721 \| 0.2107 \| 0.55 \| 0.3335 \| 0.4384 \| \| 0.7914 \| 89.0 \| 9523 \| 1.1521 \| 0.3281 \| 0.5926 \| 0.3144 \| 0.1756 \| 0.2404 \| 0.4755 \| 0.3851 \| 0.5305 \| 0.5402 \| 0.2902 \| 0.4018 \| 0.7063 \| 0.5845 \| 0.7338 \| 0.313 \| 0.5636 \| 0.2014 \| 0.3863 \| 0.2031 \| 0.576 \| 0.3386 \| 0.4414 \| \| 0.7722 \| 90.0 \| 9630 \| 1.1521 \| 0.3292 \| 0.5978 \| 0.3104 \| 0.1804 \| 0.2419 \| 0.4764 \| 0.3893 \| 0.5308 \| 0.5407 \| 0.2913 \| 0.4016 \| 0.7088 \| 0.5845 \| 0.7253 \| 0.3133 \| 0.5727 \| 0.1944 \| 0.382 \| 0.2064 \| 0.57 \| 0.3475 \| 0.4535 \| \| 0.7563 \| 91.0 \| 9737 \| 1.1742 \| 0.331 \| 0.5967 \| 0.3157 \| 0.1783 \| 0.2413 \| 0.4789 \| 0.393 \| 0.5273 \| 0.5397 \| 0.302 \| 0.403 \| 0.7119 \| 0.5886 \| 0.724 \| 0.3014 \| 0.5673 \| 0.2036 \| 0.3874 \| 0.2167 \| 0.574 \| 0.3445 \| 0.446 \| \| 0.7714 \| 92.0 \| 9844 \| 1.1554 \| 0.3274 \| 0.5924 \| 0.3159 \| 0.1808 \| 0.2393 \| 0.4769 \| 0.387 \| 0.5304 \| 0.5426 \| 0.2972 \| 0.4029 \| 0.712 \| 0.5857 \| 0.7266 \| 0.3051 \| 0.5855 \| 0.1966 \| 0.3874 \| 0.2062 \| 0.568 \| 0.3435 \| 0.4455 \| \| 0.7669 \| 93.0 \| 9951 \| 1.1550 \| 0.325 \| 0.5913 \| 0.2998 \| 0.1885 \| 0.2429 \| 0.4674 \| 0.3818 \| 0.5262 \| 0.5388 \| 0.3039 \| 0.4037 \| 0.7082 \| 0.5781 \| 0.7227 \| 0.301 \| 0.5673 \| 0.1997 \| 0.3995 \| 0.205 \| 0.56 \| 0.3414 \| 0.4444 \| \| 0.7811 \| 94.0 \| 10058 \| 1.1591 \| 0.3263 \| 0.5903 \| 0.31 \| 0.1742 \| 0.2371 \| 0.4802 \| 0.3841 \| 0.5252 \| 0.5356 \| 0.2761 \| 0.3988 \| 0.7018 \| 0.5788 \| 0.7234 \| 0.2981 \| 0.5582 \| 0.1989 \| 0.3885 \| 0.2056 \| 0.558 \| 0.3498 \| 0.45 \| \| 0.7932 \| 95.0 \| 10165 \| 1.1517 \| 0.3301 \| 0.5926 \| 0.3201 \| 0.1803 \| 0.2432 \| 0.4713 \| 0.3859 \| 0.5256 \| 0.5361 \| 0.2813 \| 0.402 \| 0.7011 \| 0.583 \| 0.7292 \| 0.3034 \| 0.5673 \| 0.2052 \| 0.3918 \| 0.2131 \| 0.546 \| 0.346 \| 0.446 \| \| 0.77 \| 96.0 \| 10272 \| 1.1538 \| 0.3319 \| 0.5967 \| 0.3222 \| 0.1813 \| 0.2434 \| 0.4755 \| 0.3891 \| 0.5203 \| 0.5319 \| 0.2707 \| 0.3965 \| 0.694 \| 0.5833 \| 0.7299 \| 0.3101 \| 0.56 \| 0.2049 \| 0.3907 \| 0.223 \| 0.538 \| 0.3384 \| 0.4409 \| \| 0.764 \| 97.0 \| 10379 \| 1.1521 \| 0.3323 \| 0.6031 \| 0.3131 \| 0.1855 \| 0.2427 \| 0.4726 \| 0.3908 \| 0.5256 \| 0.5369 \| 0.2745 \| 0.4053 \| 0.6897 \| 0.5893 \| 0.7266 \| 0.31 \| 0.5764 \| 0.1998 \| 0.3913 \| 0.2162 \| 0.546 \| 0.3464 \| 0.4444 \| \| 0.7517 \| 98.0 \| 10486 \| 1.1509 \| 0.3328 \| 0.601 \| 0.3287 \| 0.1866 \| 0.2448 \| 0.478 \| 0.3888 \| 0.5252 \| 0.5395 \| 0.2761 \| 0.4053 \| 0.7042 \| 0.5868 \| 0.724 \| 0.3141 \| 0.5782 \| 0.1991 \| 0.3907 \| 0.2159 \| 0.554 \| 0.3482 \| 0.4505 \| \| 0.7519 \| 99.0 \| 10593 \| 1.1516 \| 0.3334 \| 0.597 \| 0.3165 \| 0.1921 \| 0.2488 \| 0.4761 \| 0.3897 \| 0.5306 \| 0.5431 \| 0.2851 \| 0.4112 \| 0.7061 \| 0.5871 \| 0.726 \| 0.3091 \| 0.58 \| 0.2027 \| 0.3951 \| 0.2161 \| 0.562 \| 0.3522 \| 0.4525 \| \| 0.7373 \| 100.0 \| 10700 \| 1.1507 \| 0.3346 \| 0.5981 \| 0.3283 \| 0.1903 \| 0.2508 \| 0.4752 \| 0.3914 \| 0.5305 \| 0.5425 \| 0.2821 \| 0.4111 \| 0.705 \| 0.5884 \| 0.7253 \| 0.3168 \| 0.5818 \| 0.2028 \| 0.3934 \| 0.2102 \| 0.558 \| 0.3549 \| 0.454 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]
JuudasMooses/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.6795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.9973 \| 0.3195 \| 100 \| 3.0595 \| \| 2.5938 \| 0.6390 \| 200 \| 5.8527 \| \| 2.0334 \| 0.9585 \| 300 \| 5.6795 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
HannesKuslap/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
qubvel-hf/sbchoi-rtdetr_r50vd-finetuned-10k-cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/koo968ri) # sbchoi-rtdetr_r50vd-finetuned-10k-cppe5 This model is a fine-tuned version of [sbchoi/rtdetr_r50vd](https://huggingface.co/sbchoi/rtdetr_r50vd) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 5.4784 - Map: 0.2239 - Map 50: 0.4506 - Map 75: 0.2138 - Map Small: 0.0183 - Map Medium: 0.1151 - Map Large: 0.2987 - Mar 1: 0.218 - Mar 10: 0.3079 - Mar 100: 0.3163 - Mar Small: 0.0568 - Mar Medium: 0.1656 - Mar Large: 0.402 - Map Coverall: 0.4629 - Mar 100 Coverall: 0.5994 - Map Face Shield: 0.0649 - Mar 100 Face Shield: 0.1417 - Map Gloves: 0.1978 - Mar 100 Gloves: 0.3059 - Map Goggles: 0.0656 - Mar 100 Goggles: 0.1187 - Map Mask: 0.3282 - Mar 100 Mask: 0.4157 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 19.8959 \| 1.0 \| 107 \| 10.4382 \| 0.015 \| 0.0518 \| 0.006 \| 0.0 \| 0.0032 \| 0.0154 \| 0.0312 \| 0.08 \| 0.1101 \| 0.0 \| 0.0511 \| 0.1487 \| 0.0705 \| 0.2114 \| 0.0027 \| 0.15 \| 0.0001 \| 0.0211 \| 0.0003 \| 0.1083 \| 0.0011 \| 0.0595 \| \| 8.7775 \| 2.0 \| 214 \| 9.1554 \| 0.0523 \| 0.1237 \| 0.0395 \| 0.0092 \| 0.0289 \| 0.0704 \| 0.0938 \| 0.163 \| 0.1773 \| 0.0122 \| 0.1071 \| 0.2541 \| 0.123 \| 0.1861 \| 0.0219 \| 0.175 \| 0.0423 \| 0.1573 \| 0.0113 \| 0.1813 \| 0.0632 \| 0.187 \| \| 7.7422 \| 3.0 \| 321 \| 7.4197 \| 0.1 \| 0.2581 \| 0.0614 \| 0.0425 \| 0.0738 \| 0.1211 \| 0.1424 \| 0.2413 \| 0.2591 \| 0.1086 \| 0.2103 \| 0.3067 \| 0.16 \| 0.2199 \| 0.0337 \| 0.1933 \| 0.1186 \| 0.2746 \| 0.0085 \| 0.225 \| 0.179 \| 0.3827 \| \| 7.4523 \| 4.0 \| 428 \| 7.4193 \| 0.1107 \| 0.2562 \| 0.0744 \| 0.0116 \| 0.066 \| 0.1631 \| 0.1469 \| 0.2205 \| 0.2294 \| 0.0744 \| 0.1243 \| 0.3197 \| 0.1811 \| 0.3301 \| 0.0302 \| 0.1467 \| 0.1121 \| 0.2735 \| 0.018 \| 0.0875 \| 0.2122 \| 0.3092 \| \| 7.5623 \| 5.0 \| 535 \| 7.8406 \| 0.1154 \| 0.2796 \| 0.0821 \| 0.0134 \| 0.0782 \| 0.1731 \| 0.1308 \| 0.1992 \| 0.2013 \| 0.0254 \| 0.1302 \| 0.2818 \| 0.1452 \| 0.2343 \| 0.0315 \| 0.0767 \| 0.1517 \| 0.2643 \| 0.0161 \| 0.1042 \| 0.2326 \| 0.327 \| \| 7.6045 \| 6.0 \| 642 \| 7.1243 \| 0.1411 \| 0.3101 \| 0.1111 \| 0.0138 \| 0.0868 \| 0.1868 \| 0.1533 \| 0.2187 \| 0.2257 \| 0.0628 \| 0.1473 \| 0.275 \| 0.2607 \| 0.3584 \| 0.027 \| 0.0917 \| 0.1651 \| 0.3043 \| 0.0253 \| 0.0562 \| 0.2272 \| 0.3178 \| \| 6.8108 \| 7.0 \| 749 \| 7.6319 \| 0.1252 \| 0.2857 \| 0.1 \| 0.0103 \| 0.0904 \| 0.1878 \| 0.1441 \| 0.2117 \| 0.2132 \| 0.0609 \| 0.151 \| 0.2848 \| 0.1204 \| 0.2229 \| 0.0357 \| 0.0983 \| 0.1194 \| 0.2086 \| 0.037 \| 0.1271 \| 0.3136 \| 0.4092 \| \| 6.7824 \| 8.0 \| 856 \| 6.3509 \| 0.1761 \| 0.3743 \| 0.1561 \| 0.0176 \| 0.1056 \| 0.2354 \| 0.1887 \| 0.2682 \| 0.2772 \| 0.0262 \| 0.1671 \| 0.3683 \| 0.2966 \| 0.3831 \| 0.0392 \| 0.1517 \| 0.1887 \| 0.2957 \| 0.0216 \| 0.1312 \| 0.3346 \| 0.4243 \| \| 6.5661 \| 9.0 \| 963 \| 6.9362 \| 0.1619 \| 0.3614 \| 0.1367 \| 0.0094 \| 0.0878 \| 0.2252 \| 0.1837 \| 0.2428 \| 0.2459 \| 0.0145 \| 0.1376 \| 0.334 \| 0.2901 \| 0.3747 \| 0.0753 \| 0.1717 \| 0.1239 \| 0.1876 \| 0.0409 \| 0.1229 \| 0.2793 \| 0.3724 \| \| 6.8621 \| 10.0 \| 1070 \| 7.7606 \| 0.1142 \| 0.2565 \| 0.089 \| 0.0125 \| 0.0667 \| 0.1606 \| 0.1302 \| 0.1857 \| 0.1876 \| 0.0286 \| 0.1134 \| 0.2369 \| 0.2217 \| 0.3422 \| 0.0061 \| 0.04 \| 0.0766 \| 0.1584 \| 0.0141 \| 0.075 \| 0.2525 \| 0.3227 \| \| 7.0258 \| 11.0 \| 1177 \| 6.9053 \| 0.1446 \| 0.3325 \| 0.1254 \| 0.0156 \| 0.0929 \| 0.1954 \| 0.1755 \| 0.2389 \| 0.2431 \| 0.0649 \| 0.1647 \| 0.3001 \| 0.243 \| 0.3488 \| 0.0663 \| 0.1583 \| 0.1163 \| 0.2319 \| 0.0351 \| 0.1104 \| 0.2625 \| 0.3659 \| \| 6.8975 \| 12.0 \| 1284 \| 7.1437 \| 0.1348 \| 0.318 \| 0.101 \| 0.0171 \| 0.0922 \| 0.1828 \| 0.1579 \| 0.2284 \| 0.2338 \| 0.0616 \| 0.1612 \| 0.307 \| 0.1919 \| 0.2639 \| 0.0259 \| 0.1017 \| 0.1215 \| 0.2362 \| 0.0229 \| 0.15 \| 0.3118 \| 0.4173 \| \| 7.1849 \| 13.0 \| 1391 \| 7.5812 \| 0.1296 \| 0.3093 \| 0.0909 \| 0.0195 \| 0.0742 \| 0.1821 \| 0.1485 \| 0.1964 \| 0.2003 \| 0.0412 \| 0.1191 \| 0.2639 \| 0.2199 \| 0.3114 \| 0.0389 \| 0.0733 \| 0.0835 \| 0.173 \| 0.0367 \| 0.0875 \| 0.2691 \| 0.3562 \| \| 7.2413 \| 14.0 \| 1498 \| 6.7032 \| 0.1493 \| 0.3214 \| 0.1337 \| 0.0103 \| 0.0977 \| 0.2105 \| 0.1766 \| 0.2424 \| 0.2465 \| 0.0217 \| 0.1591 \| 0.3171 \| 0.2244 \| 0.3699 \| 0.0346 \| 0.1267 \| 0.145 \| 0.2443 \| 0.037 \| 0.0979 \| 0.3058 \| 0.3935 \| \| 6.2961 \| 15.0 \| 1605 \| 5.8884 \| 0.1614 \| 0.3412 \| 0.137 \| 0.0104 \| 0.0897 \| 0.2213 \| 0.1664 \| 0.2609 \| 0.2766 \| 0.0353 \| 0.139 \| 0.3778 \| 0.332 \| 0.5386 \| 0.0262 \| 0.0867 \| 0.1593 \| 0.2632 \| 0.0226 \| 0.1167 \| 0.2669 \| 0.3778 \| \| 6.1921 \| 16.0 \| 1712 \| 6.0759 \| 0.1672 \| 0.366 \| 0.1332 \| 0.0078 \| 0.087 \| 0.2378 \| 0.1742 \| 0.2685 \| 0.2809 \| 0.013 \| 0.1333 \| 0.3916 \| 0.3246 \| 0.5416 \| 0.0425 \| 0.1067 \| 0.1582 \| 0.2676 \| 0.0158 \| 0.1021 \| 0.2949 \| 0.3865 \| \| 5.901 \| 17.0 \| 1819 \| 5.9836 \| 0.1719 \| 0.349 \| 0.1598 \| 0.0146 \| 0.0836 \| 0.2536 \| 0.1752 \| 0.2609 \| 0.274 \| 0.0411 \| 0.1335 \| 0.3793 \| 0.3581 \| 0.5265 \| 0.0362 \| 0.1067 \| 0.1555 \| 0.2714 \| 0.0168 \| 0.0917 \| 0.293 \| 0.3741 \| \| 5.8931 \| 18.0 \| 1926 \| 5.6273 \| 0.1864 \| 0.39 \| 0.169 \| 0.0145 \| 0.0894 \| 0.2542 \| 0.1882 \| 0.2834 \| 0.2926 \| 0.0618 \| 0.1544 \| 0.3746 \| 0.3912 \| 0.5651 \| 0.0448 \| 0.1117 \| 0.1629 \| 0.2757 \| 0.0385 \| 0.1167 \| 0.2945 \| 0.3941 \| \| 5.7067 \| 19.0 \| 2033 \| 5.8492 \| 0.1802 \| 0.3771 \| 0.1605 \| 0.0152 \| 0.0957 \| 0.244 \| 0.193 \| 0.2793 \| 0.2898 \| 0.0396 \| 0.1489 \| 0.3767 \| 0.3808 \| 0.5633 \| 0.0394 \| 0.1217 \| 0.1812 \| 0.2984 \| 0.0279 \| 0.1125 \| 0.2717 \| 0.353 \| \| 5.8838 \| 20.0 \| 2140 \| 5.5158 \| 0.197 \| 0.4056 \| 0.1799 \| 0.0082 \| 0.1051 \| 0.2671 \| 0.2033 \| 0.3007 \| 0.3178 \| 0.0316 \| 0.1809 \| 0.3955 \| 0.4057 \| 0.5952 \| 0.0475 \| 0.1317 \| 0.1568 \| 0.293 \| 0.0655 \| 0.1771 \| 0.3096 \| 0.3919 \| \| 5.757 \| 21.0 \| 2247 \| 5.8292 \| 0.1895 \| 0.3919 \| 0.1651 \| 0.0138 \| 0.0887 \| 0.2658 \| 0.1937 \| 0.2791 \| 0.2896 \| 0.0232 \| 0.1351 \| 0.3858 \| 0.4212 \| 0.5994 \| 0.0476 \| 0.1467 \| 0.1219 \| 0.2297 \| 0.0719 \| 0.1146 \| 0.2847 \| 0.3578 \| \| 5.5671 \| 22.0 \| 2354 \| 5.5429 \| 0.1919 \| 0.4088 \| 0.1584 \| 0.0189 \| 0.0879 \| 0.268 \| 0.1975 \| 0.2975 \| 0.3105 \| 0.0382 \| 0.1482 \| 0.4233 \| 0.4063 \| 0.5729 \| 0.0606 \| 0.165 \| 0.1521 \| 0.2773 \| 0.0788 \| 0.1667 \| 0.2616 \| 0.3708 \| \| 5.9776 \| 23.0 \| 2461 \| 6.0600 \| 0.1805 \| 0.3854 \| 0.147 \| 0.0113 \| 0.08 \| 0.2629 \| 0.1977 \| 0.2919 \| 0.2998 \| 0.0174 \| 0.1355 \| 0.4161 \| 0.3742 \| 0.547 \| 0.0702 \| 0.19 \| 0.1298 \| 0.2238 \| 0.0594 \| 0.1833 \| 0.2688 \| 0.3551 \| \| 6.0912 \| 24.0 \| 2568 \| 6.2436 \| 0.178 \| 0.3731 \| 0.1579 \| 0.0107 \| 0.0704 \| 0.2571 \| 0.1842 \| 0.2619 \| 0.2687 \| 0.0182 \| 0.1068 \| 0.3908 \| 0.4095 \| 0.5476 \| 0.0585 \| 0.1283 \| 0.157 \| 0.2276 \| 0.0474 \| 0.1396 \| 0.2177 \| 0.3005 \| \| 6.3446 \| 25.0 \| 2675 \| 6.2047 \| 0.1718 \| 0.3749 \| 0.1428 \| 0.0138 \| 0.0834 \| 0.2428 \| 0.1772 \| 0.2644 \| 0.2761 \| 0.0477 \| 0.1334 \| 0.3698 \| 0.3639 \| 0.5205 \| 0.0449 \| 0.1267 \| 0.1729 \| 0.2903 \| 0.0472 \| 0.1354 \| 0.2301 \| 0.3076 \| \| 5.8688 \| 26.0 \| 2782 \| 5.5055 \| 0.2074 \| 0.4247 \| 0.1647 \| 0.0089 \| 0.0972 \| 0.3015 \| 0.218 \| 0.3273 \| 0.3416 \| 0.0243 \| 0.1589 \| 0.4928 \| 0.4014 \| 0.5753 \| 0.0992 \| 0.215 \| 0.1658 \| 0.3027 \| 0.0643 \| 0.2 \| 0.3063 \| 0.4151 \| \| 5.8882 \| 27.0 \| 2889 \| 6.0958 \| 0.1899 \| 0.3901 \| 0.1635 \| 0.0066 \| 0.0841 \| 0.2864 \| 0.1968 \| 0.281 \| 0.2909 \| 0.0058 \| 0.1351 \| 0.4151 \| 0.367 \| 0.5223 \| 0.0766 \| 0.1617 \| 0.1599 \| 0.2557 \| 0.0717 \| 0.1583 \| 0.2745 \| 0.3568 \| \| 5.9026 \| 28.0 \| 2996 \| 5.8641 \| 0.1894 \| 0.4096 \| 0.1672 \| 0.0186 \| 0.085 \| 0.2829 \| 0.1837 \| 0.2797 \| 0.2936 \| 0.0456 \| 0.1392 \| 0.4058 \| 0.3684 \| 0.5349 \| 0.0602 \| 0.1417 \| 0.1774 \| 0.3216 \| 0.0601 \| 0.1167 \| 0.281 \| 0.353 \| \| 5.7844 \| 29.0 \| 3103 \| 5.9831 \| 0.1829 \| 0.3867 \| 0.1519 \| 0.0236 \| 0.0753 \| 0.2618 \| 0.1825 \| 0.2804 \| 0.3 \| 0.0797 \| 0.1264 \| 0.4228 \| 0.4015 \| 0.5867 \| 0.0746 \| 0.17 \| 0.1316 \| 0.2935 \| 0.0529 \| 0.1167 \| 0.254 \| 0.333 \| \| 5.4624 \| 30.0 \| 3210 \| 5.5451 \| 0.2103 \| 0.4341 \| 0.1763 \| 0.007 \| 0.104 \| 0.3013 \| 0.2181 \| 0.3252 \| 0.3413 \| 0.0332 \| 0.1706 \| 0.4648 \| 0.4311 \| 0.6181 \| 0.1039 \| 0.21 \| 0.1703 \| 0.2995 \| 0.0464 \| 0.1729 \| 0.3 \| 0.4059 \| \| 5.3751 \| 31.0 \| 3317 \| 5.6696 \| 0.2164 \| 0.4465 \| 0.1884 \| 0.0175 \| 0.0944 \| 0.3133 \| 0.2159 \| 0.3192 \| 0.3365 \| 0.053 \| 0.1452 \| 0.4768 \| 0.4498 \| 0.647 \| 0.0963 \| 0.1783 \| 0.1693 \| 0.3162 \| 0.0427 \| 0.1312 \| 0.3238 \| 0.4097 \| \| 5.3834 \| 32.0 \| 3424 \| 5.5642 \| 0.2084 \| 0.4319 \| 0.1891 \| 0.0167 \| 0.1065 \| 0.2875 \| 0.2009 \| 0.3046 \| 0.3166 \| 0.0536 \| 0.1732 \| 0.4105 \| 0.4126 \| 0.5687 \| 0.0453 \| 0.1433 \| 0.2076 \| 0.34 \| 0.0713 \| 0.1396 \| 0.3052 \| 0.3914 \| \| 5.6258 \| 33.0 \| 3531 \| 6.1584 \| 0.1752 \| 0.3825 \| 0.142 \| 0.0104 \| 0.0937 \| 0.2504 \| 0.1836 \| 0.2604 \| 0.2714 \| 0.0332 \| 0.1404 \| 0.3709 \| 0.3399 \| 0.497 \| 0.0343 \| 0.105 \| 0.1651 \| 0.2838 \| 0.0363 \| 0.0917 \| 0.3006 \| 0.3795 \| \| 5.891 \| 34.0 \| 3638 \| 5.7572 \| 0.1819 \| 0.387 \| 0.1466 \| 0.0139 \| 0.0838 \| 0.2645 \| 0.1921 \| 0.2855 \| 0.295 \| 0.0256 \| 0.143 \| 0.4095 \| 0.3692 \| 0.544 \| 0.0404 \| 0.1233 \| 0.1534 \| 0.2935 \| 0.0656 \| 0.1333 \| 0.281 \| 0.3811 \| \| 5.2963 \| 35.0 \| 3745 \| 5.5806 \| 0.2119 \| 0.4322 \| 0.1983 \| 0.0247 \| 0.0959 \| 0.3041 \| 0.2154 \| 0.3071 \| 0.3219 \| 0.1019 \| 0.1576 \| 0.4346 \| 0.4475 \| 0.5916 \| 0.0559 \| 0.1617 \| 0.1749 \| 0.2978 \| 0.0765 \| 0.15 \| 0.3049 \| 0.4086 \| \| 5.4486 \| 36.0 \| 3852 \| 5.4363 \| 0.2144 \| 0.4363 \| 0.1795 \| 0.0246 \| 0.102 \| 0.3068 \| 0.2103 \| 0.304 \| 0.3157 \| 0.0818 \| 0.1577 \| 0.4234 \| 0.4336 \| 0.5819 \| 0.0811 \| 0.175 \| 0.1744 \| 0.2903 \| 0.061 \| 0.1208 \| 0.3217 \| 0.4103 \| \| 5.3556 \| 37.0 \| 3959 \| 5.4515 \| 0.224 \| 0.4305 \| 0.2072 \| 0.0146 \| 0.1068 \| 0.3151 \| 0.2236 \| 0.3178 \| 0.3311 \| 0.0367 \| 0.1779 \| 0.4321 \| 0.4554 \| 0.6102 \| 0.0784 \| 0.1633 \| 0.1903 \| 0.3184 \| 0.0758 \| 0.1542 \| 0.3203 \| 0.4092 \| \| 5.5322 \| 38.0 \| 4066 \| 5.7703 \| 0.209 \| 0.4153 \| 0.1905 \| 0.0095 \| 0.0996 \| 0.2975 \| 0.2088 \| 0.2973 \| 0.307 \| 0.0441 \| 0.1532 \| 0.4187 \| 0.4287 \| 0.5614 \| 0.0493 \| 0.145 \| 0.1987 \| 0.3341 \| 0.0656 \| 0.1333 \| 0.3028 \| 0.3611 \| \| 5.6029 \| 39.0 \| 4173 \| 5.5674 \| 0.2185 \| 0.4515 \| 0.1913 \| 0.012 \| 0.1012 \| 0.3171 \| 0.2243 \| 0.3148 \| 0.3294 \| 0.0188 \| 0.169 \| 0.4454 \| 0.4315 \| 0.5819 \| 0.0739 \| 0.175 \| 0.1828 \| 0.3184 \| 0.0715 \| 0.1542 \| 0.3327 \| 0.4173 \| \| 5.5652 \| 40.0 \| 4280 \| 6.3700 \| 0.1913 \| 0.3917 \| 0.1778 \| 0.0159 \| 0.0829 \| 0.2774 \| 0.1936 \| 0.2656 \| 0.2758 \| 0.044 \| 0.1229 \| 0.3773 \| 0.3794 \| 0.5355 \| 0.095 \| 0.1383 \| 0.1359 \| 0.247 \| 0.0617 \| 0.1104 \| 0.2845 \| 0.3476 \| \| 6.0757 \| 41.0 \| 4387 \| 6.1865 \| 0.1762 \| 0.3632 \| 0.1617 \| 0.0235 \| 0.0823 \| 0.2449 \| 0.1806 \| 0.2524 \| 0.2599 \| 0.0794 \| 0.1258 \| 0.3441 \| 0.3949 \| 0.5205 \| 0.0347 \| 0.0883 \| 0.1377 \| 0.2454 \| 0.0237 \| 0.0792 \| 0.2898 \| 0.3659 \| \| 5.7012 \| 42.0 \| 4494 \| 6.3980 \| 0.1822 \| 0.3586 \| 0.1738 \| 0.0154 \| 0.0854 \| 0.2402 \| 0.1903 \| 0.2665 \| 0.2732 \| 0.0311 \| 0.1329 \| 0.3519 \| 0.4061 \| 0.5518 \| 0.0653 \| 0.1283 \| 0.1038 \| 0.1989 \| 0.0516 \| 0.1146 \| 0.2842 \| 0.3724 \| \| 5.3802 \| 43.0 \| 4601 \| 5.3665 \| 0.2106 \| 0.4207 \| 0.1825 \| 0.0225 \| 0.1026 \| 0.2906 \| 0.2157 \| 0.3075 \| 0.3177 \| 0.0327 \| 0.1608 \| 0.4207 \| 0.4352 \| 0.6054 \| 0.0439 \| 0.115 \| 0.1684 \| 0.3151 \| 0.0719 \| 0.1208 \| 0.3338 \| 0.4319 \| \| 5.4719 \| 44.0 \| 4708 \| 5.7122 \| 0.2072 \| 0.4086 \| 0.1859 \| 0.0155 \| 0.1034 \| 0.2908 \| 0.2095 \| 0.2866 \| 0.2949 \| 0.0432 \| 0.1589 \| 0.3876 \| 0.4153 \| 0.5416 \| 0.0485 \| 0.1217 \| 0.1916 \| 0.2968 \| 0.0724 \| 0.1167 \| 0.3084 \| 0.3978 \| \| 5.4061 \| 45.0 \| 4815 \| 5.3479 \| 0.2171 \| 0.4339 \| 0.1925 \| 0.0335 \| 0.1027 \| 0.3095 \| 0.2117 \| 0.3016 \| 0.3108 \| 0.1145 \| 0.1625 \| 0.4053 \| 0.4328 \| 0.5681 \| 0.0406 \| 0.1033 \| 0.177 \| 0.3049 \| 0.098 \| 0.1396 \| 0.3369 \| 0.4384 \| \| 5.3701 \| 46.0 \| 4922 \| 5.7335 \| 0.1938 \| 0.3969 \| 0.1733 \| 0.0204 \| 0.093 \| 0.2671 \| 0.1995 \| 0.2872 \| 0.3008 \| 0.0615 \| 0.1581 \| 0.3877 \| 0.4239 \| 0.5747 \| 0.0468 \| 0.125 \| 0.1603 \| 0.28 \| 0.0505 \| 0.125 \| 0.2874 \| 0.3995 \| \| 5.4386 \| 47.0 \| 5029 \| 5.5070 \| 0.2167 \| 0.4648 \| 0.1839 \| 0.0397 \| 0.1143 \| 0.297 \| 0.2241 \| 0.3128 \| 0.327 \| 0.0784 \| 0.1872 \| 0.4106 \| 0.4129 \| 0.5711 \| 0.0934 \| 0.1833 \| 0.183 \| 0.3119 \| 0.0985 \| 0.1604 \| 0.2956 \| 0.4081 \| \| 5.538 \| 48.0 \| 5136 \| 5.9021 \| 0.196 \| 0.4063 \| 0.1583 \| 0.0386 \| 0.08 \| 0.2826 \| 0.2034 \| 0.2765 \| 0.2853 \| 0.0862 \| 0.1286 \| 0.3832 \| 0.4243 \| 0.5506 \| 0.0823 \| 0.16 \| 0.1432 \| 0.2492 \| 0.0531 \| 0.0917 \| 0.2771 \| 0.3751 \| \| 5.4485 \| 49.0 \| 5243 \| 5.4971 \| 0.2043 \| 0.4362 \| 0.1812 \| 0.0161 \| 0.097 \| 0.277 \| 0.2091 \| 0.3035 \| 0.3142 \| 0.0304 \| 0.1567 \| 0.4055 \| 0.4557 \| 0.603 \| 0.0814 \| 0.16 \| 0.136 \| 0.2568 \| 0.0484 \| 0.1312 \| 0.3 \| 0.42 \| \| 5.3201 \| 50.0 \| 5350 \| 5.6181 \| 0.2187 \| 0.4415 \| 0.1978 \| 0.0231 \| 0.0955 \| 0.3126 \| 0.2133 \| 0.3034 \| 0.3125 \| 0.0407 \| 0.1525 \| 0.4199 \| 0.4325 \| 0.5669 \| 0.1138 \| 0.16 \| 0.1449 \| 0.26 \| 0.1097 \| 0.175 \| 0.2925 \| 0.4005 \| \| 5.6202 \| 51.0 \| 5457 \| 5.6736 \| 0.2082 \| 0.415 \| 0.1912 \| 0.0194 \| 0.0929 \| 0.3019 \| 0.2147 \| 0.2935 \| 0.3021 \| 0.0498 \| 0.1481 \| 0.4087 \| 0.432 \| 0.556 \| 0.0478 \| 0.11 \| 0.1599 \| 0.2627 \| 0.11 \| 0.1979 \| 0.2913 \| 0.3838 \| \| 5.5043 \| 52.0 \| 5564 \| 5.7540 \| 0.2152 \| 0.4244 \| 0.198 \| 0.024 \| 0.0907 \| 0.309 \| 0.2191 \| 0.303 \| 0.3098 \| 0.0667 \| 0.1542 \| 0.4141 \| 0.4192 \| 0.5398 \| 0.0972 \| 0.16 \| 0.1706 \| 0.2762 \| 0.0898 \| 0.1708 \| 0.2991 \| 0.4022 \| \| 5.4446 \| 53.0 \| 5671 \| 5.3788 \| 0.2273 \| 0.4656 \| 0.193 \| 0.0183 \| 0.1133 \| 0.3178 \| 0.2377 \| 0.3129 \| 0.3266 \| 0.0609 \| 0.1723 \| 0.4317 \| 0.4162 \| 0.5608 \| 0.1212 \| 0.1783 \| 0.1715 \| 0.2886 \| 0.1082 \| 0.1854 \| 0.3193 \| 0.42 \| \| 5.4776 \| 54.0 \| 5778 \| 5.6180 \| 0.2073 \| 0.4264 \| 0.1724 \| 0.0176 \| 0.1079 \| 0.2764 \| 0.2074 \| 0.2949 \| 0.3064 \| 0.0399 \| 0.1632 \| 0.3915 \| 0.4111 \| 0.5554 \| 0.0909 \| 0.1683 \| 0.1553 \| 0.2649 \| 0.0574 \| 0.1167 \| 0.3217 \| 0.4265 \| \| 5.4939 \| 55.0 \| 5885 \| 5.5716 \| 0.2042 \| 0.4311 \| 0.1723 \| 0.016 \| 0.0937 \| 0.2869 \| 0.2085 \| 0.3014 \| 0.3129 \| 0.0594 \| 0.1605 \| 0.4079 \| 0.4217 \| 0.5753 \| 0.0589 \| 0.1467 \| 0.1548 \| 0.2827 \| 0.0679 \| 0.1396 \| 0.318 \| 0.42 \| \| 5.2805 \| 56.0 \| 5992 \| 5.3773 \| 0.2041 \| 0.4212 \| 0.1711 \| 0.0343 \| 0.1026 \| 0.278 \| 0.2126 \| 0.3049 \| 0.316 \| 0.0873 \| 0.1654 \| 0.4012 \| 0.3985 \| 0.5566 \| 0.073 \| 0.17 \| 0.159 \| 0.2811 \| 0.0496 \| 0.1229 \| 0.3405 \| 0.4492 \| \| 5.435 \| 57.0 \| 6099 \| 5.7054 \| 0.2086 \| 0.4337 \| 0.1867 \| 0.0223 \| 0.0939 \| 0.2906 \| 0.2077 \| 0.292 \| 0.3008 \| 0.0866 \| 0.1452 \| 0.3944 \| 0.4274 \| 0.559 \| 0.0861 \| 0.16 \| 0.1584 \| 0.2643 \| 0.0732 \| 0.1271 \| 0.2978 \| 0.3935 \| \| 5.4074 \| 58.0 \| 6206 \| 5.7383 \| 0.2143 \| 0.4488 \| 0.1947 \| 0.012 \| 0.1032 \| 0.2983 \| 0.2141 \| 0.2961 \| 0.3071 \| 0.0654 \| 0.1567 \| 0.3987 \| 0.4173 \| 0.5711 \| 0.1133 \| 0.1817 \| 0.1558 \| 0.2557 \| 0.075 \| 0.1292 \| 0.31 \| 0.3978 \| \| 5.4203 \| 59.0 \| 6313 \| 5.8282 \| 0.2027 \| 0.4063 \| 0.1735 \| 0.0219 \| 0.097 \| 0.2848 \| 0.2034 \| 0.2812 \| 0.2893 \| 0.0807 \| 0.1433 \| 0.3785 \| 0.3967 \| 0.5241 \| 0.0692 \| 0.16 \| 0.1734 \| 0.2692 \| 0.065 \| 0.0958 \| 0.3093 \| 0.3973 \| \| 5.429 \| 60.0 \| 6420 \| 5.5048 \| 0.2013 \| 0.4125 \| 0.1838 \| 0.0339 \| 0.103 \| 0.2693 \| 0.2001 \| 0.2909 \| 0.2978 \| 0.1268 \| 0.1539 \| 0.3725 \| 0.4207 \| 0.5633 \| 0.0551 \| 0.1367 \| 0.167 \| 0.2805 \| 0.0468 \| 0.1042 \| 0.317 \| 0.4043 \| \| 5.4682 \| 61.0 \| 6527 \| 5.8043 \| 0.2072 \| 0.4226 \| 0.197 \| 0.0257 \| 0.1029 \| 0.2771 \| 0.2042 \| 0.2843 \| 0.2924 \| 0.0821 \| 0.1599 \| 0.3601 \| 0.413 \| 0.5404 \| 0.0596 \| 0.12 \| 0.1735 \| 0.2832 \| 0.0768 \| 0.1167 \| 0.313 \| 0.4016 \| \| 5.3648 \| 62.0 \| 6634 \| 5.6528 \| 0.2191 \| 0.4496 \| 0.2036 \| 0.0395 \| 0.1227 \| 0.2863 \| 0.2181 \| 0.3029 \| 0.3087 \| 0.11 \| 0.1759 \| 0.3789 \| 0.4085 \| 0.5301 \| 0.0834 \| 0.16 \| 0.1924 \| 0.2908 \| 0.06 \| 0.1146 \| 0.3511 \| 0.4481 \| \| 5.4239 \| 63.0 \| 6741 \| 5.6781 \| 0.2203 \| 0.4493 \| 0.1915 \| 0.0484 \| 0.1114 \| 0.3007 \| 0.2141 \| 0.2983 \| 0.3046 \| 0.0873 \| 0.1715 \| 0.3796 \| 0.4097 \| 0.5253 \| 0.0762 \| 0.13 \| 0.2113 \| 0.3259 \| 0.0678 \| 0.1187 \| 0.3367 \| 0.4232 \| \| 5.462 \| 64.0 \| 6848 \| 5.6454 \| 0.2113 \| 0.4239 \| 0.1881 \| 0.0304 \| 0.1122 \| 0.2978 \| 0.2107 \| 0.2981 \| 0.3086 \| 0.0568 \| 0.1716 \| 0.3965 \| 0.393 \| 0.5265 \| 0.0634 \| 0.1417 \| 0.1984 \| 0.3324 \| 0.0619 \| 0.1125 \| 0.3399 \| 0.4297 \| \| 5.5407 \| 65.0 \| 6955 \| 6.0103 \| 0.2027 \| 0.4166 \| 0.1891 \| 0.0212 \| 0.1034 \| 0.2701 \| 0.2019 \| 0.2762 \| 0.2824 \| 0.0628 \| 0.1506 \| 0.3556 \| 0.3955 \| 0.5048 \| 0.0522 \| 0.1217 \| 0.1849 \| 0.293 \| 0.0558 \| 0.0771 \| 0.3253 \| 0.4157 \| \| 5.8151 \| 66.0 \| 7062 \| 5.6046 \| 0.2131 \| 0.4179 \| 0.1946 \| 0.0203 \| 0.1078 \| 0.289 \| 0.2069 \| 0.2969 \| 0.3064 \| 0.0667 \| 0.1587 \| 0.3923 \| 0.4381 \| 0.5807 \| 0.0814 \| 0.1583 \| 0.1628 \| 0.2681 \| 0.0567 \| 0.1 \| 0.3264 \| 0.4249 \| \| 5.7518 \| 67.0 \| 7169 \| 6.1826 \| 0.1905 \| 0.3764 \| 0.18 \| 0.018 \| 0.1042 \| 0.2507 \| 0.1928 \| 0.2613 \| 0.2665 \| 0.0844 \| 0.1446 \| 0.3312 \| 0.4 \| 0.506 \| 0.0291 \| 0.0917 \| 0.1851 \| 0.2914 \| 0.0344 \| 0.0688 \| 0.304 \| 0.3746 \| \| 5.7898 \| 68.0 \| 7276 \| 5.6343 \| 0.2043 \| 0.4141 \| 0.1788 \| 0.0188 \| 0.1111 \| 0.2593 \| 0.207 \| 0.2905 \| 0.2985 \| 0.0947 \| 0.1705 \| 0.3525 \| 0.4386 \| 0.5717 \| 0.0603 \| 0.14 \| 0.167 \| 0.2778 \| 0.0362 \| 0.0833 \| 0.3194 \| 0.4195 \| \| 5.4899 \| 69.0 \| 7383 \| 5.5064 \| 0.2105 \| 0.4178 \| 0.1815 \| 0.0536 \| 0.1057 \| 0.2802 \| 0.2087 \| 0.3034 \| 0.3124 \| 0.1259 \| 0.1579 \| 0.3948 \| 0.4457 \| 0.5759 \| 0.0721 \| 0.1567 \| 0.1767 \| 0.2908 \| 0.0264 \| 0.0979 \| 0.3317 \| 0.4405 \| \| 5.5614 \| 70.0 \| 7490 \| 5.9147 \| 0.1879 \| 0.3858 \| 0.1698 \| 0.0411 \| 0.1004 \| 0.246 \| 0.1963 \| 0.2823 \| 0.2887 \| 0.1108 \| 0.1583 \| 0.3513 \| 0.4046 \| 0.5301 \| 0.0437 \| 0.1183 \| 0.1606 \| 0.2897 \| 0.0223 \| 0.1042 \| 0.3086 \| 0.4011 \| \| 5.502 \| 71.0 \| 7597 \| 5.7527 \| 0.1992 \| 0.4014 \| 0.1893 \| 0.0326 \| 0.1013 \| 0.2601 \| 0.1973 \| 0.2851 \| 0.2937 \| 0.0768 \| 0.1534 \| 0.3678 \| 0.4346 \| 0.5542 \| 0.054 \| 0.135 \| 0.1512 \| 0.2686 \| 0.0376 \| 0.0771 \| 0.3187 \| 0.4335 \| \| 5.3745 \| 72.0 \| 7704 \| 5.6854 \| 0.2101 \| 0.4397 \| 0.1789 \| 0.0181 \| 0.0985 \| 0.2954 \| 0.2087 \| 0.2997 \| 0.308 \| 0.0494 \| 0.1565 \| 0.3996 \| 0.4275 \| 0.5578 \| 0.0993 \| 0.2033 \| 0.1524 \| 0.2719 \| 0.0587 \| 0.1063 \| 0.3124 \| 0.4005 \| \| 5.478 \| 73.0 \| 7811 \| 5.5914 \| 0.2143 \| 0.4404 \| 0.1894 \| 0.0361 \| 0.1078 \| 0.2929 \| 0.2177 \| 0.2978 \| 0.3066 \| 0.0948 \| 0.1641 \| 0.3871 \| 0.4374 \| 0.5524 \| 0.0901 \| 0.1683 \| 0.1761 \| 0.313 \| 0.0519 \| 0.1 \| 0.3162 \| 0.3995 \| \| 5.4078 \| 74.0 \| 7918 \| 5.4932 \| 0.2274 \| 0.4827 \| 0.1945 \| 0.0366 \| 0.1166 \| 0.311 \| 0.2232 \| 0.3153 \| 0.3248 \| 0.0961 \| 0.1764 \| 0.4136 \| 0.44 \| 0.5633 \| 0.0936 \| 0.1933 \| 0.2148 \| 0.3259 \| 0.0631 \| 0.1125 \| 0.3257 \| 0.4292 \| \| 5.2262 \| 75.0 \| 8025 \| 5.3089 \| 0.2239 \| 0.4499 \| 0.2093 \| 0.0182 \| 0.1143 \| 0.3137 \| 0.2196 \| 0.3143 \| 0.3221 \| 0.0651 \| 0.1718 \| 0.4182 \| 0.4429 \| 0.5747 \| 0.0946 \| 0.17 \| 0.1882 \| 0.3216 \| 0.0451 \| 0.1104 \| 0.3488 \| 0.4335 \| \| 5.2505 \| 76.0 \| 8132 \| 5.5950 \| 0.2166 \| 0.4315 \| 0.1951 \| 0.0185 \| 0.1073 \| 0.309 \| 0.2177 \| 0.3029 \| 0.3129 \| 0.0718 \| 0.164 \| 0.4127 \| 0.4407 \| 0.5512 \| 0.08 \| 0.1667 \| 0.1763 \| 0.2978 \| 0.0558 \| 0.1271 \| 0.3301 \| 0.4216 \| \| 5.2589 \| 77.0 \| 8239 \| 5.3893 \| 0.2289 \| 0.4676 \| 0.2032 \| 0.0268 \| 0.1122 \| 0.3243 \| 0.2232 \| 0.3189 \| 0.3279 \| 0.0791 \| 0.1659 \| 0.4289 \| 0.4389 \| 0.5771 \| 0.0804 \| 0.1717 \| 0.1938 \| 0.3211 \| 0.0831 \| 0.1333 \| 0.3481 \| 0.4362 \| \| 5.158 \| 78.0 \| 8346 \| 5.4600 \| 0.2209 \| 0.4324 \| 0.2044 \| 0.0174 \| 0.1087 \| 0.3026 \| 0.2175 \| 0.3024 \| 0.3113 \| 0.0747 \| 0.1596 \| 0.4028 \| 0.4444 \| 0.5771 \| 0.0736 \| 0.1483 \| 0.1923 \| 0.3032 \| 0.0642 \| 0.1083 \| 0.33 \| 0.4195 \| \| 5.2881 \| 79.0 \| 8453 \| 5.5639 \| 0.2199 \| 0.4295 \| 0.1993 \| 0.0205 \| 0.1089 \| 0.3022 \| 0.2114 \| 0.2986 \| 0.307 \| 0.0784 \| 0.1555 \| 0.4008 \| 0.4604 \| 0.5801 \| 0.0794 \| 0.1483 \| 0.1871 \| 0.2962 \| 0.049 \| 0.0979 \| 0.3237 \| 0.4124 \| \| 5.2688 \| 80.0 \| 8560 \| 5.4073 \| 0.221 \| 0.4583 \| 0.2045 \| 0.0442 \| 0.1057 \| 0.3 \| 0.215 \| 0.3064 \| 0.3151 \| 0.0879 \| 0.1617 \| 0.4029 \| 0.4573 \| 0.5801 \| 0.096 \| 0.1783 \| 0.1794 \| 0.3054 \| 0.0463 \| 0.0875 \| 0.3261 \| 0.4243 \| \| 5.2374 \| 81.0 \| 8667 \| 5.4014 \| 0.2221 \| 0.4509 \| 0.2 \| 0.0324 \| 0.1089 \| 0.3074 \| 0.2204 \| 0.3108 \| 0.319 \| 0.0774 \| 0.1632 \| 0.4133 \| 0.4389 \| 0.5723 \| 0.085 \| 0.18 \| 0.2015 \| 0.3097 \| 0.052 \| 0.1042 \| 0.3333 \| 0.4286 \| \| 5.2208 \| 82.0 \| 8774 \| 5.4175 \| 0.2289 \| 0.449 \| 0.2124 \| 0.0471 \| 0.1066 \| 0.3136 \| 0.2269 \| 0.3162 \| 0.3255 \| 0.0902 \| 0.1611 \| 0.4251 \| 0.4557 \| 0.5898 \| 0.1118 \| 0.2017 \| 0.2025 \| 0.3076 \| 0.0472 \| 0.1063 \| 0.3273 \| 0.4222 \| \| 5.3092 \| 83.0 \| 8881 \| 5.5742 \| 0.2091 \| 0.4243 \| 0.1928 \| 0.0163 \| 0.1006 \| 0.2874 \| 0.2142 \| 0.2966 \| 0.3081 \| 0.0533 \| 0.1518 \| 0.3977 \| 0.4426 \| 0.5813 \| 0.0731 \| 0.175 \| 0.1763 \| 0.2935 \| 0.0386 \| 0.0833 \| 0.3148 \| 0.4076 \| \| 5.3841 \| 84.0 \| 8988 \| 5.5962 \| 0.2158 \| 0.4373 \| 0.1891 \| 0.0239 \| 0.1024 \| 0.2958 \| 0.2167 \| 0.3028 \| 0.3121 \| 0.0576 \| 0.1562 \| 0.4005 \| 0.4528 \| 0.5759 \| 0.0885 \| 0.1817 \| 0.1699 \| 0.2751 \| 0.0405 \| 0.1021 \| 0.3273 \| 0.4259 \| \| 5.3724 \| 85.0 \| 9095 \| 5.6336 \| 0.2162 \| 0.4289 \| 0.1967 \| 0.0148 \| 0.1008 \| 0.2989 \| 0.2186 \| 0.2993 \| 0.3096 \| 0.0576 \| 0.1572 \| 0.3935 \| 0.4352 \| 0.5735 \| 0.0742 \| 0.1617 \| 0.1661 \| 0.2746 \| 0.0894 \| 0.1208 \| 0.3164 \| 0.4173 \| \| 5.3174 \| 86.0 \| 9202 \| 5.4311 \| 0.2202 \| 0.4377 \| 0.1949 \| 0.0118 \| 0.1035 \| 0.3026 \| 0.2138 \| 0.3086 \| 0.3187 \| 0.0519 \| 0.1592 \| 0.4123 \| 0.4611 \| 0.597 \| 0.075 \| 0.1683 \| 0.1778 \| 0.2914 \| 0.0653 \| 0.1125 \| 0.3217 \| 0.4243 \| \| 5.3588 \| 87.0 \| 9309 \| 5.6099 \| 0.2102 \| 0.4264 \| 0.2016 \| 0.0098 \| 0.101 \| 0.2872 \| 0.2037 \| 0.2961 \| 0.3042 \| 0.0246 \| 0.1528 \| 0.3895 \| 0.457 \| 0.5982 \| 0.0576 \| 0.15 \| 0.1728 \| 0.2876 \| 0.0494 \| 0.0771 \| 0.3144 \| 0.4081 \| \| 5.3652 \| 88.0 \| 9416 \| 5.5130 \| 0.214 \| 0.4258 \| 0.1935 \| 0.0173 \| 0.1086 \| 0.2806 \| 0.2091 \| 0.302 \| 0.313 \| 0.052 \| 0.1613 \| 0.3917 \| 0.4535 \| 0.6084 \| 0.0677 \| 0.1583 \| 0.1857 \| 0.2989 \| 0.0423 \| 0.0771 \| 0.3211 \| 0.4222 \| \| 5.3507 \| 89.0 \| 9523 \| 5.5487 \| 0.2165 \| 0.4273 \| 0.2017 \| 0.0147 \| 0.1075 \| 0.2891 \| 0.2102 \| 0.3003 \| 0.3078 \| 0.0527 \| 0.1573 \| 0.3945 \| 0.4663 \| 0.5982 \| 0.0668 \| 0.1417 \| 0.1759 \| 0.2859 \| 0.0473 \| 0.0958 \| 0.3261 \| 0.4173 \| \| 5.3079 \| 90.0 \| 9630 \| 5.5233 \| 0.2173 \| 0.4314 \| 0.1982 \| 0.0199 \| 0.1091 \| 0.2885 \| 0.2187 \| 0.3076 \| 0.317 \| 0.0697 \| 0.1633 \| 0.401 \| 0.4609 \| 0.6102 \| 0.0677 \| 0.16 \| 0.171 \| 0.287 \| 0.065 \| 0.1125 \| 0.3221 \| 0.4151 \| \| 5.3084 \| 91.0 \| 9737 \| 5.5679 \| 0.221 \| 0.429 \| 0.2074 \| 0.0271 \| 0.1074 \| 0.2907 \| 0.219 \| 0.3044 \| 0.313 \| 0.0771 \| 0.1587 \| 0.3927 \| 0.4744 \| 0.6157 \| 0.076 \| 0.1467 \| 0.181 \| 0.2903 \| 0.0489 \| 0.1 \| 0.3248 \| 0.4124 \| \| 5.3048 \| 92.0 \| 9844 \| 5.7454 \| 0.209 \| 0.4153 \| 0.1905 \| 0.0224 \| 0.1041 \| 0.2769 \| 0.2082 \| 0.2927 \| 0.3032 \| 0.0488 \| 0.1552 \| 0.3825 \| 0.4488 \| 0.603 \| 0.0529 \| 0.1167 \| 0.1875 \| 0.2876 \| 0.0395 \| 0.1 \| 0.3161 \| 0.4086 \| \| 5.3667 \| 93.0 \| 9951 \| 5.5465 \| 0.2198 \| 0.4462 \| 0.2006 \| 0.0237 \| 0.1071 \| 0.2946 \| 0.217 \| 0.3033 \| 0.313 \| 0.0739 \| 0.1637 \| 0.3937 \| 0.4632 \| 0.6054 \| 0.0673 \| 0.13 \| 0.1884 \| 0.3 \| 0.0578 \| 0.1063 \| 0.3224 \| 0.4232 \| \| 5.3391 \| 94.0 \| 10058 \| 5.5115 \| 0.2158 \| 0.4391 \| 0.2025 \| 0.0185 \| 0.1086 \| 0.2846 \| 0.2157 \| 0.3061 \| 0.3152 \| 0.0549 \| 0.164 \| 0.3952 \| 0.4752 \| 0.6157 \| 0.0758 \| 0.1583 \| 0.1781 \| 0.287 \| 0.0371 \| 0.1042 \| 0.3127 \| 0.4108 \| \| 5.2343 \| 95.0 \| 10165 \| 5.5251 \| 0.2204 \| 0.4441 \| 0.2043 \| 0.0164 \| 0.1093 \| 0.2925 \| 0.2163 \| 0.3073 \| 0.3172 \| 0.0586 \| 0.1601 \| 0.4069 \| 0.4783 \| 0.612 \| 0.0685 \| 0.1583 \| 0.1884 \| 0.2951 \| 0.0477 \| 0.1104 \| 0.3191 \| 0.4103 \| \| 5.2467 \| 96.0 \| 10272 \| 5.3810 \| 0.2182 \| 0.4426 \| 0.2023 \| 0.0206 \| 0.11 \| 0.2947 \| 0.2212 \| 0.3112 \| 0.3212 \| 0.0655 \| 0.1642 \| 0.4137 \| 0.4659 \| 0.612 \| 0.0601 \| 0.145 \| 0.1971 \| 0.3151 \| 0.0488 \| 0.1167 \| 0.3191 \| 0.4173 \| \| 5.2167 \| 97.0 \| 10379 \| 5.3852 \| 0.2288 \| 0.4657 \| 0.2075 \| 0.0193 \| 0.1121 \| 0.3084 \| 0.2222 \| 0.3144 \| 0.3237 \| 0.0576 \| 0.165 \| 0.4169 \| 0.48 \| 0.6108 \| 0.0767 \| 0.1617 \| 0.2061 \| 0.3173 \| 0.0553 \| 0.1146 \| 0.3259 \| 0.4141 \| \| 5.2097 \| 98.0 \| 10486 \| 5.4532 \| 0.2189 \| 0.4447 \| 0.2072 \| 0.0186 \| 0.1176 \| 0.2845 \| 0.2229 \| 0.3122 \| 0.3196 \| 0.0517 \| 0.1717 \| 0.4046 \| 0.4699 \| 0.603 \| 0.058 \| 0.15 \| 0.1928 \| 0.3027 \| 0.0519 \| 0.1271 \| 0.3221 \| 0.4151 \| \| 5.276 \| 99.0 \| 10593 \| 5.4841 \| 0.2195 \| 0.4472 \| 0.2109 \| 0.0152 \| 0.1101 \| 0.2944 \| 0.2192 \| 0.3053 \| 0.3125 \| 0.0535 \| 0.1658 \| 0.3939 \| 0.4666 \| 0.5982 \| 0.0653 \| 0.1417 \| 0.1999 \| 0.313 \| 0.0452 \| 0.1 \| 0.3207 \| 0.4097 \| \| 5.2471 \| 100.0 \| 10700 \| 5.4784 \| 0.2239 \| 0.4506 \| 0.2138 \| 0.0183 \| 0.1151 \| 0.2987 \| 0.218 \| 0.3079 \| 0.3163 \| 0.0568 \| 0.1656 \| 0.402 \| 0.4629 \| 0.5994 \| 0.0649 \| 0.1417 \| 0.1978 \| 0.3059 \| 0.0656 \| 0.1187 \| 0.3282 \| 0.4157 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
aisak-ai/aisak-detect	# AISAK-Detect ## Overview: AISAK-Detect is an integral component of the AISAK-Visual system, specializing in object detection tasks. Leveraging an encoder-decoder transformer architecture with a convolutional backbone, AISAK-Detect excels in accurately and efficiently detecting objects within images. This model enhances the image understanding capabilities of AISAK-Visual, contributing to comprehensive visual analysis. Trained and fine-tuned by the AISAK team, AISAK-Detect is designed to seamlessly integrate into the broader AISAK system, ensuring cohesive performance in image analysis tasks. ## Model Information: - Model Name: AISAK-Detect - Version: 1.0 - Model Architecture: Transformer with convolutional backbone - Specialization: AISAK-Detect is a specialized model within the AISAK-Visual system, focusing on object detection tasks. It employs an encoder-decoder transformer architecture with a convolutional backbone, enabling it to effectively analyze images and generate precise object detection results. AISAK-Visual is part of the broader AISAK system and is specialized in image captioning tasks. ## Intended Use: The model demonstrates high accuracy in object detection tasks, leveraging the synergy between its transformer-based encoder-decoder architecture and the convolutional backbone. When utilized in conjunction with AISAK-Visual, it enhances overall performance in image analysis tasks. ## Performance: AISAK-Visual, based on the BLIP framework, achieves state-of-the-art results on image captioning tasks, including image-text retrieval, image captioning, and VQA. Its generalization ability is demonstrated by its strong performance on video-language tasks in a zero-shot manner. ## Ethical Considerations: - Bias Mitigation: Efforts have been made to mitigate bias during training; however, users are encouraged to remain vigilant about potential biases in the model's output. - Fair Use: Users should exercise caution when using AISAK-Visual in sensitive contexts and ensure fair and ethical use of the generated image captions. ## Limitations: - While proficient in general object detection, AISAK-Detect may encounter challenges in scenarios requiring specialized object recognition or highly cluttered images. - Users should be aware of these limitations and consider them when interpreting the model's outputs. ## Deployment: AISAK-Detect's inferencing capabilities will be seamlessly integrated into the deployment of the AISAK-Visual system. This integration ensures smooth operation and maximizes the synergy between the two models, providing comprehensive image understanding and analysis. ## Caveats: - Users should verify critical decisions based on AISAK-Detect's object detection results, particularly in high-stakes scenarios. Considering the broader context provided by AISAK-Visual is essential for a comprehensive understanding of visual content and informed decision-making. ## Model Card Information: - Model Card Created: April 25, 2024 - Last Updated: April 25, 2024 - Contact Information: For any inquiries or communication regarding AISAK, please contact me at mandelakorilogan@gmail.com. © 2024 Mandela Logan. All rights reserved. No part of this model may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the copyright holder. Users are expressly prohibited from creating replications or spaces derived from this model, whether in whole or in part, without the explicit authorization of the copyright holder. Unauthorized use or reproduction of this model is strictly prohibited by copyright law.	[ "n/a", "person", "traffic light", "fire hydrant", "street sign", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "bicycle", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "hat", "backpack", "umbrella", "shoe", "car", "eye glasses", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "motorcycle", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "plate", "wine glass", "cup", "fork", "knife", "airplane", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "bus", "donut", "cake", "chair", "couch", "potted plant", "bed", "mirror", "dining table", "window", "desk", "train", "toilet", "door", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "truck", "toaster", "sink", "refrigerator", "blender", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "boat", "toothbrush" ]
qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/hgyy7wjo) # facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
MarkoLillemagi/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4503 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.5641 \| 0.8 \| 1000 \| 1.4503 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
YaroslavPrytula/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9698 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.237 \| 0.2 \| 500 \| 1.0990 \| \| 1.0403 \| 0.4 \| 1000 \| 0.9698 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "accessories", "bags", "clothing", "shoes" ]
firefiruses/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "dog", "dog", "dogs" ]
qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/y96s266q) # facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.3283 - Map: 0.2947 - Map 50: 0.5809 - Map 75: 0.2681 - Map Small: 0.1583 - Map Medium: 0.23 - Map Large: 0.4971 - Mar 1: 0.2972 - Mar 10: 0.4633 - Mar 100: 0.4771 - Mar Small: 0.2237 - Mar Medium: 0.4317 - Mar Large: 0.7008 - Map Coverall: 0.5445 - Mar 100 Coverall: 0.6829 - Map Face Shield: 0.2753 - Mar 100 Face Shield: 0.4937 - Map Gloves: 0.2028 - Mar 100 Gloves: 0.4098 - Map Goggles: 0.151 - Mar 100 Goggles: 0.3938 - Map Mask: 0.3002 - Mar 100 Mask: 0.4053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 2.7248 \| 1.0 \| 107 \| 2.6074 \| 0.0142 \| 0.0396 \| 0.0076 \| 0.0039 \| 0.0015 \| 0.0139 \| 0.0281 \| 0.085 \| 0.1226 \| 0.0401 \| 0.0831 \| 0.1324 \| 0.0625 \| 0.3068 \| 0.0 \| 0.0 \| 0.0024 \| 0.1589 \| 0.0 \| 0.0 \| 0.0061 \| 0.1476 \| \| 2.3311 \| 2.0 \| 214 \| 2.3317 \| 0.0296 \| 0.0744 \| 0.019 \| 0.007 \| 0.0084 \| 0.0291 \| 0.0548 \| 0.1307 \| 0.1676 \| 0.0627 \| 0.1019 \| 0.1817 \| 0.1282 \| 0.4536 \| 0.0 \| 0.0 \| 0.0076 \| 0.1777 \| 0.0 \| 0.0 \| 0.0119 \| 0.2067 \| \| 2.1268 \| 3.0 \| 321 \| 2.2497 \| 0.0259 \| 0.0601 \| 0.0189 \| 0.0075 \| 0.0188 \| 0.0236 \| 0.0516 \| 0.1358 \| 0.183 \| 0.053 \| 0.1058 \| 0.2174 \| 0.1028 \| 0.5477 \| 0.0 \| 0.0 \| 0.0056 \| 0.175 \| 0.0 \| 0.0 \| 0.0209 \| 0.1924 \| \| 1.9337 \| 4.0 \| 428 \| 2.0126 \| 0.0464 \| 0.1088 \| 0.0338 \| 0.0173 \| 0.0202 \| 0.0392 \| 0.0779 \| 0.1704 \| 0.2113 \| 0.0536 \| 0.1312 \| 0.2662 \| 0.166 \| 0.5896 \| 0.0 \| 0.0 \| 0.0117 \| 0.2121 \| 0.0 \| 0.0 \| 0.0543 \| 0.2547 \| \| 1.7712 \| 5.0 \| 535 \| 1.9681 \| 0.0654 \| 0.1458 \| 0.0466 \| 0.0205 \| 0.037 \| 0.0593 \| 0.0942 \| 0.1996 \| 0.236 \| 0.0773 \| 0.1439 \| 0.3188 \| 0.2289 \| 0.6279 \| 0.0026 \| 0.0367 \| 0.0318 \| 0.2286 \| 0.0 \| 0.0 \| 0.0636 \| 0.2867 \| \| 1.6867 \| 6.0 \| 642 \| 1.9592 \| 0.0736 \| 0.1771 \| 0.0487 \| 0.0041 \| 0.0415 \| 0.1024 \| 0.094 \| 0.1794 \| 0.2144 \| 0.0524 \| 0.1234 \| 0.3006 \| 0.2756 \| 0.5964 \| 0.0057 \| 0.0342 \| 0.0178 \| 0.2353 \| 0.0 \| 0.0 \| 0.0688 \| 0.2062 \| \| 1.6342 \| 7.0 \| 749 \| 1.8956 \| 0.0829 \| 0.172 \| 0.0727 \| 0.0108 \| 0.0426 \| 0.131 \| 0.1135 \| 0.2153 \| 0.2315 \| 0.0463 \| 0.1354 \| 0.3658 \| 0.2921 \| 0.5892 \| 0.0143 \| 0.0873 \| 0.0306 \| 0.1817 \| 0.002 \| 0.0077 \| 0.0758 \| 0.2916 \| \| 1.6003 \| 8.0 \| 856 \| 1.7564 \| 0.1105 \| 0.2502 \| 0.0854 \| 0.0256 \| 0.0815 \| 0.1467 \| 0.1344 \| 0.2559 \| 0.2844 \| 0.074 \| 0.2137 \| 0.4119 \| 0.3628 \| 0.6252 \| 0.0492 \| 0.1987 \| 0.036 \| 0.2688 \| 0.0009 \| 0.0262 \| 0.1035 \| 0.3031 \| \| 1.5536 \| 9.0 \| 963 \| 1.8190 \| 0.1043 \| 0.2518 \| 0.0728 \| 0.0168 \| 0.0669 \| 0.1254 \| 0.1301 \| 0.2492 \| 0.268 \| 0.0637 \| 0.1812 \| 0.4043 \| 0.3236 \| 0.5775 \| 0.0381 \| 0.1823 \| 0.043 \| 0.2424 \| 0.0011 \| 0.0569 \| 0.1158 \| 0.2809 \| \| 1.5082 \| 10.0 \| 1070 \| 1.7315 \| 0.1314 \| 0.2986 \| 0.1032 \| 0.0233 \| 0.0862 \| 0.1886 \| 0.1654 \| 0.2975 \| 0.3201 \| 0.062 \| 0.2453 \| 0.4825 \| 0.3749 \| 0.5991 \| 0.0633 \| 0.2696 \| 0.0546 \| 0.2728 \| 0.0197 \| 0.1569 \| 0.1446 \| 0.3022 \| \| 1.4375 \| 11.0 \| 1177 \| 1.6288 \| 0.1535 \| 0.3396 \| 0.1235 \| 0.0308 \| 0.1048 \| 0.2523 \| 0.1912 \| 0.3504 \| 0.3762 \| 0.1132 \| 0.281 \| 0.5931 \| 0.4365 \| 0.65 \| 0.0788 \| 0.3456 \| 0.0635 \| 0.2848 \| 0.02 \| 0.2708 \| 0.1687 \| 0.3298 \| \| 1.4056 \| 12.0 \| 1284 \| 1.6457 \| 0.1394 \| 0.3148 \| 0.0984 \| 0.0325 \| 0.0954 \| 0.2416 \| 0.1685 \| 0.3278 \| 0.3516 \| 0.0839 \| 0.2689 \| 0.5579 \| 0.3979 \| 0.6477 \| 0.0657 \| 0.2861 \| 0.0692 \| 0.2688 \| 0.0096 \| 0.2615 \| 0.1546 \| 0.2938 \| \| 1.424 \| 13.0 \| 1391 \| 1.6102 \| 0.1626 \| 0.3626 \| 0.1216 \| 0.0531 \| 0.1099 \| 0.2685 \| 0.1902 \| 0.3443 \| 0.3722 \| 0.1117 \| 0.299 \| 0.5444 \| 0.4262 \| 0.6423 \| 0.0721 \| 0.3519 \| 0.0749 \| 0.283 \| 0.0412 \| 0.2585 \| 0.1984 \| 0.3253 \| \| 1.3553 \| 14.0 \| 1498 \| 1.5945 \| 0.1601 \| 0.3462 \| 0.1318 \| 0.0314 \| 0.1071 \| 0.278 \| 0.1977 \| 0.3453 \| 0.3659 \| 0.1093 \| 0.2915 \| 0.5759 \| 0.4558 \| 0.6329 \| 0.0692 \| 0.3291 \| 0.0696 \| 0.2937 \| 0.0229 \| 0.2554 \| 0.183 \| 0.3182 \| \| 1.3127 \| 15.0 \| 1605 \| 1.6288 \| 0.165 \| 0.3566 \| 0.1315 \| 0.0444 \| 0.1033 \| 0.287 \| 0.1937 \| 0.3491 \| 0.3718 \| 0.0778 \| 0.2843 \| 0.6119 \| 0.4463 \| 0.6383 \| 0.0903 \| 0.343 \| 0.0818 \| 0.3049 \| 0.0277 \| 0.2569 \| 0.1791 \| 0.316 \| \| 1.2941 \| 16.0 \| 1712 \| 1.5854 \| 0.1643 \| 0.3635 \| 0.1281 \| 0.0533 \| 0.1198 \| 0.2617 \| 0.2003 \| 0.3668 \| 0.3896 \| 0.1619 \| 0.3128 \| 0.5948 \| 0.439 \| 0.641 \| 0.1011 \| 0.3886 \| 0.0718 \| 0.2973 \| 0.0277 \| 0.3123 \| 0.1819 \| 0.3089 \| \| 1.271 \| 17.0 \| 1819 \| 1.5453 \| 0.1645 \| 0.3585 \| 0.1352 \| 0.069 \| 0.1089 \| 0.2721 \| 0.2053 \| 0.3563 \| 0.3835 \| 0.1498 \| 0.3023 \| 0.5795 \| 0.4413 \| 0.65 \| 0.0907 \| 0.3405 \| 0.0652 \| 0.2969 \| 0.0292 \| 0.3092 \| 0.1963 \| 0.3209 \| \| 1.2797 \| 18.0 \| 1926 \| 1.4980 \| 0.1828 \| 0.3932 \| 0.1529 \| 0.0898 \| 0.1312 \| 0.3023 \| 0.2189 \| 0.3885 \| 0.4139 \| 0.2014 \| 0.3349 \| 0.6208 \| 0.4426 \| 0.6338 \| 0.1163 \| 0.4 \| 0.085 \| 0.3286 \| 0.05 \| 0.3585 \| 0.2203 \| 0.3489 \| \| 1.2202 \| 19.0 \| 2033 \| 1.5525 \| 0.1768 \| 0.3837 \| 0.1496 \| 0.0654 \| 0.1163 \| 0.3329 \| 0.2189 \| 0.3765 \| 0.4027 \| 0.1528 \| 0.3314 \| 0.6205 \| 0.4413 \| 0.6171 \| 0.1071 \| 0.3975 \| 0.0922 \| 0.329 \| 0.0324 \| 0.3277 \| 0.211 \| 0.3422 \| \| 1.2601 \| 20.0 \| 2140 \| 1.5374 \| 0.1806 \| 0.3936 \| 0.1454 \| 0.0636 \| 0.1232 \| 0.2955 \| 0.2168 \| 0.373 \| 0.401 \| 0.1507 \| 0.3136 \| 0.6096 \| 0.4367 \| 0.6324 \| 0.1267 \| 0.4101 \| 0.0865 \| 0.2737 \| 0.0448 \| 0.3446 \| 0.2084 \| 0.344 \| \| 1.2382 \| 21.0 \| 2247 \| 1.5249 \| 0.1687 \| 0.3792 \| 0.1313 \| 0.0512 \| 0.1189 \| 0.3014 \| 0.2056 \| 0.3703 \| 0.394 \| 0.1416 \| 0.3245 \| 0.5803 \| 0.4207 \| 0.6266 \| 0.0906 \| 0.3886 \| 0.073 \| 0.3022 \| 0.0526 \| 0.3231 \| 0.2066 \| 0.3293 \| \| 1.1701 \| 22.0 \| 2354 \| 1.5312 \| 0.1891 \| 0.4048 \| 0.1572 \| 0.0608 \| 0.1315 \| 0.3261 \| 0.2291 \| 0.3843 \| 0.4069 \| 0.135 \| 0.3326 \| 0.6264 \| 0.4435 \| 0.6234 \| 0.1407 \| 0.3684 \| 0.0997 \| 0.35 \| 0.0451 \| 0.3492 \| 0.2168 \| 0.3436 \| \| 1.1604 \| 23.0 \| 2461 \| 1.4588 \| 0.1924 \| 0.4116 \| 0.1591 \| 0.0521 \| 0.1244 \| 0.3417 \| 0.2199 \| 0.3947 \| 0.4099 \| 0.1433 \| 0.3346 \| 0.6199 \| 0.4795 \| 0.6293 \| 0.1222 \| 0.381 \| 0.0932 \| 0.3335 \| 0.0501 \| 0.3508 \| 0.2167 \| 0.3551 \| \| 1.1605 \| 24.0 \| 2568 \| 1.4838 \| 0.1938 \| 0.4038 \| 0.159 \| 0.0595 \| 0.1262 \| 0.3412 \| 0.2126 \| 0.3878 \| 0.4032 \| 0.153 \| 0.3075 \| 0.6215 \| 0.4689 \| 0.6419 \| 0.1383 \| 0.3848 \| 0.1099 \| 0.354 \| 0.0458 \| 0.3092 \| 0.2061 \| 0.3262 \| \| 1.1148 \| 25.0 \| 2675 \| 1.4525 \| 0.19 \| 0.3952 \| 0.1518 \| 0.0459 \| 0.1462 \| 0.3308 \| 0.2158 \| 0.3855 \| 0.4048 \| 0.1365 \| 0.3526 \| 0.591 \| 0.4618 \| 0.6446 \| 0.1184 \| 0.4063 \| 0.0995 \| 0.35 \| 0.0523 \| 0.2862 \| 0.218 \| 0.3369 \| \| 1.1126 \| 26.0 \| 2782 \| 1.4628 \| 0.2043 \| 0.4292 \| 0.1697 \| 0.0581 \| 0.1509 \| 0.3362 \| 0.234 \| 0.403 \| 0.4236 \| 0.1471 \| 0.3592 \| 0.6312 \| 0.4892 \| 0.6635 \| 0.1204 \| 0.3924 \| 0.1185 \| 0.3442 \| 0.0491 \| 0.3692 \| 0.2444 \| 0.3489 \| \| 1.1128 \| 27.0 \| 2889 \| 1.4258 \| 0.2041 \| 0.4284 \| 0.1715 \| 0.0714 \| 0.1429 \| 0.3312 \| 0.232 \| 0.4125 \| 0.4299 \| 0.1504 \| 0.3629 \| 0.6398 \| 0.4813 \| 0.6635 \| 0.1375 \| 0.4392 \| 0.1243 \| 0.3491 \| 0.0419 \| 0.3292 \| 0.2358 \| 0.3684 \| \| 1.0908 \| 28.0 \| 2996 \| 1.4615 \| 0.2072 \| 0.425 \| 0.1828 \| 0.0839 \| 0.1465 \| 0.3402 \| 0.2404 \| 0.3906 \| 0.4027 \| 0.1514 \| 0.3253 \| 0.6275 \| 0.4933 \| 0.6482 \| 0.1083 \| 0.3886 \| 0.1146 \| 0.3339 \| 0.0681 \| 0.2908 \| 0.2517 \| 0.352 \| \| 1.0785 \| 29.0 \| 3103 \| 1.4452 \| 0.195 \| 0.4186 \| 0.1581 \| 0.0527 \| 0.1553 \| 0.3474 \| 0.2285 \| 0.3948 \| 0.4135 \| 0.1668 \| 0.3468 \| 0.6451 \| 0.4759 \| 0.6572 \| 0.108 \| 0.4152 \| 0.1055 \| 0.3357 \| 0.0649 \| 0.32 \| 0.2207 \| 0.3396 \| \| 1.0677 \| 30.0 \| 3210 \| 1.4368 \| 0.2105 \| 0.4321 \| 0.187 \| 0.0744 \| 0.1641 \| 0.3346 \| 0.243 \| 0.4065 \| 0.4272 \| 0.1789 \| 0.3693 \| 0.6408 \| 0.4924 \| 0.6662 \| 0.1364 \| 0.4152 \| 0.1271 \| 0.3388 \| 0.0754 \| 0.3785 \| 0.2213 \| 0.3373 \| \| 1.0448 \| 31.0 \| 3317 \| 1.4151 \| 0.2115 \| 0.436 \| 0.1687 \| 0.063 \| 0.1549 \| 0.3449 \| 0.2306 \| 0.4104 \| 0.4299 \| 0.1689 \| 0.3668 \| 0.6302 \| 0.4999 \| 0.6423 \| 0.1308 \| 0.4304 \| 0.1373 \| 0.3554 \| 0.0478 \| 0.3615 \| 0.2418 \| 0.36 \| \| 1.0656 \| 32.0 \| 3424 \| 1.4272 \| 0.2218 \| 0.449 \| 0.1816 \| 0.0807 \| 0.1638 \| 0.3717 \| 0.2564 \| 0.4207 \| 0.4405 \| 0.1786 \| 0.3802 \| 0.6742 \| 0.4992 \| 0.6563 \| 0.1638 \| 0.4633 \| 0.1285 \| 0.35 \| 0.0649 \| 0.3631 \| 0.2528 \| 0.3698 \| \| 1.0345 \| 33.0 \| 3531 \| 1.4501 \| 0.2144 \| 0.4477 \| 0.174 \| 0.0843 \| 0.1471 \| 0.3437 \| 0.242 \| 0.4054 \| 0.43 \| 0.1869 \| 0.3499 \| 0.6493 \| 0.5021 \| 0.645 \| 0.1414 \| 0.438 \| 0.1352 \| 0.3585 \| 0.0702 \| 0.3585 \| 0.2233 \| 0.3502 \| \| 1.0243 \| 34.0 \| 3638 \| 1.3969 \| 0.2248 \| 0.4842 \| 0.1808 \| 0.0848 \| 0.1805 \| 0.371 \| 0.2491 \| 0.4226 \| 0.4434 \| 0.1466 \| 0.405 \| 0.6755 \| 0.4944 \| 0.6721 \| 0.1569 \| 0.4443 \| 0.1482 \| 0.3634 \| 0.0832 \| 0.3754 \| 0.2412 \| 0.3618 \| \| 1.0221 \| 35.0 \| 3745 \| 1.4094 \| 0.2203 \| 0.4537 \| 0.1956 \| 0.0682 \| 0.1655 \| 0.3699 \| 0.2469 \| 0.4174 \| 0.4359 \| 0.1675 \| 0.3785 \| 0.6632 \| 0.5107 \| 0.6613 \| 0.1535 \| 0.4405 \| 0.1419 \| 0.3705 \| 0.0566 \| 0.3462 \| 0.239 \| 0.3609 \| \| 0.99 \| 36.0 \| 3852 \| 1.3827 \| 0.2092 \| 0.4456 \| 0.1798 \| 0.0706 \| 0.1619 \| 0.3708 \| 0.2594 \| 0.4185 \| 0.4385 \| 0.1516 \| 0.39 \| 0.6637 \| 0.4897 \| 0.6536 \| 0.1332 \| 0.4165 \| 0.154 \| 0.3674 \| 0.057 \| 0.4031 \| 0.2119 \| 0.352 \| \| 0.9819 \| 37.0 \| 3959 \| 1.4144 \| 0.2298 \| 0.4652 \| 0.1873 \| 0.0827 \| 0.1817 \| 0.3784 \| 0.2504 \| 0.4264 \| 0.4448 \| 0.1796 \| 0.3827 \| 0.68 \| 0.5135 \| 0.6743 \| 0.1888 \| 0.4392 \| 0.1466 \| 0.3714 \| 0.0676 \| 0.3769 \| 0.2325 \| 0.3622 \| \| 0.9652 \| 38.0 \| 4066 \| 1.3730 \| 0.2336 \| 0.487 \| 0.2047 \| 0.0886 \| 0.1914 \| 0.3765 \| 0.2472 \| 0.4256 \| 0.4448 \| 0.1851 \| 0.3925 \| 0.6692 \| 0.5115 \| 0.6743 \| 0.1967 \| 0.4544 \| 0.1575 \| 0.3759 \| 0.0675 \| 0.3554 \| 0.235 \| 0.364 \| \| 0.9397 \| 39.0 \| 4173 \| 1.3323 \| 0.2396 \| 0.4804 \| 0.215 \| 0.1072 \| 0.1857 \| 0.4298 \| 0.2672 \| 0.4452 \| 0.4634 \| 0.1984 \| 0.3967 \| 0.7004 \| 0.5192 \| 0.6806 \| 0.1547 \| 0.4785 \| 0.1653 \| 0.3848 \| 0.1059 \| 0.3908 \| 0.2531 \| 0.3822 \| \| 0.9346 \| 40.0 \| 4280 \| 1.3810 \| 0.2354 \| 0.488 \| 0.2112 \| 0.1037 \| 0.1849 \| 0.4009 \| 0.2623 \| 0.4279 \| 0.4404 \| 0.1807 \| 0.3652 \| 0.6944 \| 0.51 \| 0.6788 \| 0.1698 \| 0.4456 \| 0.1541 \| 0.3509 \| 0.098 \| 0.3569 \| 0.245 \| 0.3698 \| \| 0.9575 \| 41.0 \| 4387 \| 1.3592 \| 0.2396 \| 0.4808 \| 0.2072 \| 0.1173 \| 0.2005 \| 0.373 \| 0.2488 \| 0.4339 \| 0.4533 \| 0.2073 \| 0.4023 \| 0.6733 \| 0.5255 \| 0.6878 \| 0.1735 \| 0.4443 \| 0.1591 \| 0.383 \| 0.1032 \| 0.3862 \| 0.2368 \| 0.3653 \| \| 0.948 \| 42.0 \| 4494 \| 1.3716 \| 0.2295 \| 0.4945 \| 0.1711 \| 0.0793 \| 0.1755 \| 0.3977 \| 0.2508 \| 0.4201 \| 0.4391 \| 0.1373 \| 0.3812 \| 0.6851 \| 0.5015 \| 0.6644 \| 0.1825 \| 0.4443 \| 0.1456 \| 0.3696 \| 0.094 \| 0.3662 \| 0.2238 \| 0.3511 \| \| 0.9254 \| 43.0 \| 4601 \| 1.3677 \| 0.238 \| 0.4902 \| 0.2091 \| 0.1018 \| 0.1861 \| 0.4061 \| 0.2651 \| 0.4344 \| 0.4542 \| 0.1866 \| 0.389 \| 0.6789 \| 0.5221 \| 0.6968 \| 0.1681 \| 0.4481 \| 0.1749 \| 0.4013 \| 0.0885 \| 0.3677 \| 0.2363 \| 0.3569 \| \| 0.9162 \| 44.0 \| 4708 \| 1.4004 \| 0.2363 \| 0.491 \| 0.2079 \| 0.0949 \| 0.1897 \| 0.394 \| 0.2511 \| 0.4205 \| 0.4403 \| 0.1717 \| 0.391 \| 0.6663 \| 0.5106 \| 0.6919 \| 0.1837 \| 0.4241 \| 0.1573 \| 0.3732 \| 0.0733 \| 0.3385 \| 0.2566 \| 0.3738 \| \| 0.9186 \| 45.0 \| 4815 \| 1.3953 \| 0.2378 \| 0.4922 \| 0.2211 \| 0.1115 \| 0.1843 \| 0.3661 \| 0.2527 \| 0.4224 \| 0.4439 \| 0.2003 \| 0.3923 \| 0.6378 \| 0.4989 \| 0.6761 \| 0.2 \| 0.4418 \| 0.1678 \| 0.3821 \| 0.0843 \| 0.3677 \| 0.238 \| 0.3516 \| \| 0.9225 \| 46.0 \| 4922 \| 1.3936 \| 0.2395 \| 0.5114 \| 0.1973 \| 0.1111 \| 0.1805 \| 0.405 \| 0.2557 \| 0.4384 \| 0.4586 \| 0.2039 \| 0.3929 \| 0.6808 \| 0.4977 \| 0.6716 \| 0.1804 \| 0.4595 \| 0.1716 \| 0.3933 \| 0.0927 \| 0.4062 \| 0.2553 \| 0.3622 \| \| 0.9011 \| 47.0 \| 5029 \| 1.3632 \| 0.2437 \| 0.4962 \| 0.2148 \| 0.0766 \| 0.1902 \| 0.4155 \| 0.2687 \| 0.4318 \| 0.452 \| 0.1849 \| 0.4063 \| 0.6736 \| 0.528 \| 0.6703 \| 0.1841 \| 0.4696 \| 0.1692 \| 0.3772 \| 0.0796 \| 0.3754 \| 0.2579 \| 0.3676 \| \| 0.8909 \| 48.0 \| 5136 \| 1.3843 \| 0.2513 \| 0.5148 \| 0.211 \| 0.1097 \| 0.1896 \| 0.4229 \| 0.2602 \| 0.427 \| 0.4438 \| 0.1835 \| 0.3919 \| 0.6837 \| 0.5279 \| 0.6649 \| 0.1892 \| 0.4165 \| 0.168 \| 0.3862 \| 0.1023 \| 0.3677 \| 0.2692 \| 0.384 \| \| 0.9073 \| 49.0 \| 5243 \| 1.3763 \| 0.2411 \| 0.4936 \| 0.208 \| 0.1217 \| 0.1832 \| 0.4036 \| 0.2703 \| 0.4364 \| 0.4507 \| 0.2241 \| 0.3758 \| 0.6765 \| 0.5104 \| 0.6468 \| 0.18 \| 0.4747 \| 0.1656 \| 0.379 \| 0.0975 \| 0.3646 \| 0.2522 \| 0.3884 \| \| 0.8877 \| 50.0 \| 5350 \| 1.3689 \| 0.251 \| 0.5232 \| 0.2096 \| 0.1109 \| 0.1933 \| 0.4366 \| 0.2773 \| 0.4407 \| 0.4563 \| 0.208 \| 0.3948 \| 0.7056 \| 0.526 \| 0.6712 \| 0.1895 \| 0.4532 \| 0.1796 \| 0.392 \| 0.106 \| 0.3831 \| 0.254 \| 0.3822 \| \| 0.8917 \| 51.0 \| 5457 \| 1.3656 \| 0.2506 \| 0.51 \| 0.202 \| 0.1155 \| 0.1989 \| 0.4294 \| 0.2728 \| 0.4417 \| 0.4633 \| 0.2124 \| 0.4163 \| 0.6978 \| 0.524 \| 0.6797 \| 0.1921 \| 0.4519 \| 0.1789 \| 0.3924 \| 0.0954 \| 0.3954 \| 0.2627 \| 0.3969 \| \| 0.8844 \| 52.0 \| 5564 \| 1.3813 \| 0.249 \| 0.5001 \| 0.2201 \| 0.0869 \| 0.1916 \| 0.4365 \| 0.253 \| 0.4423 \| 0.4577 \| 0.2158 \| 0.3852 \| 0.696 \| 0.5307 \| 0.6829 \| 0.1786 \| 0.4595 \| 0.1711 \| 0.3607 \| 0.0967 \| 0.3892 \| 0.2677 \| 0.396 \| \| 0.8548 \| 53.0 \| 5671 \| 1.3952 \| 0.2509 \| 0.5076 \| 0.2131 \| 0.0846 \| 0.1989 \| 0.4249 \| 0.2738 \| 0.4475 \| 0.4634 \| 0.1904 \| 0.4007 \| 0.708 \| 0.5228 \| 0.6694 \| 0.2054 \| 0.4785 \| 0.1833 \| 0.3817 \| 0.0732 \| 0.3908 \| 0.27 \| 0.3964 \| \| 0.8677 \| 54.0 \| 5778 \| 1.4126 \| 0.2542 \| 0.5102 \| 0.2243 \| 0.1042 \| 0.1943 \| 0.4266 \| 0.2703 \| 0.4432 \| 0.464 \| 0.2001 \| 0.4123 \| 0.7115 \| 0.5149 \| 0.6689 \| 0.2095 \| 0.4797 \| 0.1891 \| 0.3973 \| 0.0973 \| 0.3908 \| 0.2605 \| 0.3831 \| \| 0.8411 \| 55.0 \| 5885 \| 1.3719 \| 0.2622 \| 0.5302 \| 0.2162 \| 0.1064 \| 0.1973 \| 0.4546 \| 0.2722 \| 0.4387 \| 0.4582 \| 0.2055 \| 0.4015 \| 0.6793 \| 0.5294 \| 0.6721 \| 0.2128 \| 0.4658 \| 0.1936 \| 0.3853 \| 0.1067 \| 0.38 \| 0.2684 \| 0.388 \| \| 0.8304 \| 56.0 \| 5992 \| 1.3720 \| 0.2574 \| 0.5284 \| 0.2123 \| 0.111 \| 0.2098 \| 0.4288 \| 0.2714 \| 0.4422 \| 0.4608 \| 0.2192 \| 0.4254 \| 0.678 \| 0.513 \| 0.673 \| 0.2099 \| 0.457 \| 0.1897 \| 0.3884 \| 0.1077 \| 0.3908 \| 0.2668 \| 0.3947 \| \| 0.8494 \| 57.0 \| 6099 \| 1.3436 \| 0.2688 \| 0.5468 \| 0.2296 \| 0.1174 \| 0.2014 \| 0.487 \| 0.2786 \| 0.4496 \| 0.4712 \| 0.2191 \| 0.4218 \| 0.7035 \| 0.5323 \| 0.6797 \| 0.2373 \| 0.4949 \| 0.1761 \| 0.3746 \| 0.1257 \| 0.4169 \| 0.2728 \| 0.3898 \| \| 0.8505 \| 58.0 \| 6206 \| 1.3279 \| 0.2665 \| 0.522 \| 0.237 \| 0.1173 \| 0.2102 \| 0.4605 \| 0.2776 \| 0.4479 \| 0.4679 \| 0.2063 \| 0.4194 \| 0.6983 \| 0.5201 \| 0.6833 \| 0.2246 \| 0.4772 \| 0.1797 \| 0.3857 \| 0.1267 \| 0.3954 \| 0.2814 \| 0.3978 \| \| 0.8227 \| 59.0 \| 6313 \| 1.3279 \| 0.2668 \| 0.5267 \| 0.2222 \| 0.1304 \| 0.208 \| 0.4523 \| 0.2823 \| 0.4514 \| 0.4696 \| 0.223 \| 0.4233 \| 0.6969 \| 0.5244 \| 0.6757 \| 0.2493 \| 0.5089 \| 0.1784 \| 0.3982 \| 0.1102 \| 0.3723 \| 0.2717 \| 0.3929 \| \| 0.8129 \| 60.0 \| 6420 \| 1.3400 \| 0.2673 \| 0.5348 \| 0.2388 \| 0.1185 \| 0.2189 \| 0.4501 \| 0.2807 \| 0.4554 \| 0.4708 \| 0.2163 \| 0.4207 \| 0.7048 \| 0.5286 \| 0.6761 \| 0.2222 \| 0.4646 \| 0.1809 \| 0.3969 \| 0.1413 \| 0.4231 \| 0.2635 \| 0.3933 \| \| 0.8054 \| 61.0 \| 6527 \| 1.3815 \| 0.2734 \| 0.548 \| 0.2313 \| 0.1199 \| 0.2321 \| 0.4526 \| 0.282 \| 0.4534 \| 0.4687 \| 0.2029 \| 0.427 \| 0.6887 \| 0.5306 \| 0.6797 \| 0.2432 \| 0.4734 \| 0.1897 \| 0.3942 \| 0.1207 \| 0.4 \| 0.2825 \| 0.396 \| \| 0.7911 \| 62.0 \| 6634 \| 1.3294 \| 0.2704 \| 0.5431 \| 0.2301 \| 0.1107 \| 0.2143 \| 0.4745 \| 0.2791 \| 0.4557 \| 0.4704 \| 0.2158 \| 0.4193 \| 0.7055 \| 0.5363 \| 0.691 \| 0.2281 \| 0.4532 \| 0.1879 \| 0.3929 \| 0.1242 \| 0.4138 \| 0.2756 \| 0.4013 \| \| 0.7883 \| 63.0 \| 6741 \| 1.3769 \| 0.2605 \| 0.5374 \| 0.2202 \| 0.1197 \| 0.2211 \| 0.4287 \| 0.2717 \| 0.4422 \| 0.459 \| 0.2065 \| 0.4214 \| 0.7033 \| 0.5196 \| 0.6698 \| 0.2206 \| 0.4646 \| 0.176 \| 0.3696 \| 0.1092 \| 0.4108 \| 0.2772 \| 0.3804 \| \| 0.786 \| 64.0 \| 6848 \| 1.3379 \| 0.2666 \| 0.5441 \| 0.2257 \| 0.1268 \| 0.2197 \| 0.468 \| 0.2779 \| 0.4595 \| 0.479 \| 0.2326 \| 0.4344 \| 0.7088 \| 0.5226 \| 0.6779 \| 0.2319 \| 0.5076 \| 0.172 \| 0.3933 \| 0.1309 \| 0.4169 \| 0.2755 \| 0.3991 \| \| 0.7776 \| 65.0 \| 6955 \| 1.3192 \| 0.2708 \| 0.5498 \| 0.2165 \| 0.1242 \| 0.2149 \| 0.4638 \| 0.2795 \| 0.4604 \| 0.4736 \| 0.2291 \| 0.4245 \| 0.6993 \| 0.5326 \| 0.6761 \| 0.2312 \| 0.4823 \| 0.194 \| 0.3951 \| 0.1255 \| 0.4138 \| 0.2704 \| 0.4004 \| \| 0.7615 \| 66.0 \| 7062 \| 1.3282 \| 0.2745 \| 0.5488 \| 0.2276 \| 0.1299 \| 0.2271 \| 0.459 \| 0.2828 \| 0.458 \| 0.4734 \| 0.2233 \| 0.4263 \| 0.695 \| 0.5327 \| 0.6725 \| 0.2393 \| 0.4949 \| 0.1862 \| 0.3946 \| 0.1283 \| 0.4015 \| 0.2862 \| 0.4036 \| \| 0.7625 \| 67.0 \| 7169 \| 1.3395 \| 0.2778 \| 0.5506 \| 0.2384 \| 0.1129 \| 0.2216 \| 0.4549 \| 0.2754 \| 0.4565 \| 0.4698 \| 0.2099 \| 0.4264 \| 0.7016 \| 0.5473 \| 0.6829 \| 0.2375 \| 0.4835 \| 0.1937 \| 0.3866 \| 0.1235 \| 0.3938 \| 0.2872 \| 0.4022 \| \| 0.7495 \| 68.0 \| 7276 \| 1.3261 \| 0.2763 \| 0.5487 \| 0.2482 \| 0.1388 \| 0.2211 \| 0.4822 \| 0.2841 \| 0.4541 \| 0.4678 \| 0.2334 \| 0.4186 \| 0.6913 \| 0.5344 \| 0.6752 \| 0.2438 \| 0.4633 \| 0.1956 \| 0.4027 \| 0.12 \| 0.3954 \| 0.2876 \| 0.4027 \| \| 0.752 \| 69.0 \| 7383 \| 1.3089 \| 0.2816 \| 0.5614 \| 0.2464 \| 0.1287 \| 0.2309 \| 0.4715 \| 0.2833 \| 0.4535 \| 0.4704 \| 0.2142 \| 0.4297 \| 0.6813 \| 0.5332 \| 0.6775 \| 0.2508 \| 0.4785 \| 0.199 \| 0.4062 \| 0.1332 \| 0.3831 \| 0.2915 \| 0.4067 \| \| 0.7329 \| 70.0 \| 7490 \| 1.3402 \| 0.2703 \| 0.5482 \| 0.2299 \| 0.1397 \| 0.2188 \| 0.459 \| 0.2748 \| 0.4504 \| 0.4671 \| 0.2478 \| 0.4077 \| 0.6824 \| 0.5322 \| 0.6788 \| 0.2376 \| 0.4544 \| 0.181 \| 0.3955 \| 0.1111 \| 0.3985 \| 0.2895 \| 0.4084 \| \| 0.7383 \| 71.0 \| 7597 \| 1.3367 \| 0.2789 \| 0.559 \| 0.2514 \| 0.1478 \| 0.2189 \| 0.4587 \| 0.2852 \| 0.462 \| 0.4785 \| 0.2426 \| 0.4202 \| 0.6802 \| 0.5409 \| 0.6928 \| 0.2435 \| 0.4595 \| 0.1959 \| 0.4 \| 0.1287 \| 0.4262 \| 0.2855 \| 0.4142 \| \| 0.7223 \| 72.0 \| 7704 \| 1.3356 \| 0.2774 \| 0.5589 \| 0.2312 \| 0.1569 \| 0.2185 \| 0.4519 \| 0.2852 \| 0.4558 \| 0.4692 \| 0.2374 \| 0.4173 \| 0.6811 \| 0.5452 \| 0.6883 \| 0.2422 \| 0.4646 \| 0.1951 \| 0.3888 \| 0.1131 \| 0.3923 \| 0.2915 \| 0.412 \| \| 0.7155 \| 73.0 \| 7811 \| 1.3295 \| 0.2745 \| 0.5553 \| 0.2355 \| 0.1444 \| 0.2142 \| 0.4732 \| 0.2876 \| 0.4614 \| 0.4763 \| 0.2288 \| 0.4301 \| 0.6877 \| 0.5413 \| 0.6851 \| 0.2356 \| 0.4835 \| 0.1922 \| 0.4013 \| 0.1264 \| 0.4077 \| 0.2768 \| 0.404 \| \| 0.7123 \| 74.0 \| 7918 \| 1.3261 \| 0.2725 \| 0.546 \| 0.2292 \| 0.1447 \| 0.2054 \| 0.464 \| 0.2856 \| 0.4561 \| 0.4731 \| 0.2266 \| 0.4088 \| 0.695 \| 0.542 \| 0.6779 \| 0.2295 \| 0.4696 \| 0.186 \| 0.4138 \| 0.121 \| 0.4062 \| 0.2842 \| 0.3978 \| \| 0.7044 \| 75.0 \| 8025 \| 1.3641 \| 0.274 \| 0.5524 \| 0.2362 \| 0.1446 \| 0.2029 \| 0.4664 \| 0.29 \| 0.4518 \| 0.4683 \| 0.2221 \| 0.416 \| 0.6934 \| 0.5351 \| 0.6761 \| 0.2387 \| 0.481 \| 0.1923 \| 0.3933 \| 0.1144 \| 0.3969 \| 0.2895 \| 0.3942 \| \| 0.6946 \| 76.0 \| 8132 \| 1.3353 \| 0.2769 \| 0.5585 \| 0.2302 \| 0.1549 \| 0.2175 \| 0.457 \| 0.2855 \| 0.4546 \| 0.4677 \| 0.221 \| 0.4138 \| 0.6847 \| 0.5438 \| 0.6833 \| 0.2514 \| 0.4709 \| 0.1899 \| 0.392 \| 0.1209 \| 0.4 \| 0.2785 \| 0.3924 \| \| 0.692 \| 77.0 \| 8239 \| 1.3378 \| 0.2818 \| 0.5631 \| 0.2419 \| 0.1771 \| 0.2198 \| 0.4555 \| 0.2929 \| 0.4592 \| 0.4761 \| 0.2936 \| 0.4141 \| 0.6873 \| 0.5437 \| 0.6815 \| 0.2651 \| 0.4899 \| 0.2011 \| 0.4062 \| 0.1198 \| 0.4108 \| 0.2795 \| 0.392 \| \| 0.6912 \| 78.0 \| 8346 \| 1.3104 \| 0.2801 \| 0.5626 \| 0.2337 \| 0.1692 \| 0.2139 \| 0.4725 \| 0.2856 \| 0.4588 \| 0.476 \| 0.2346 \| 0.4302 \| 0.6936 \| 0.5434 \| 0.6919 \| 0.2599 \| 0.462 \| 0.1845 \| 0.4071 \| 0.1339 \| 0.42 \| 0.2787 \| 0.3991 \| \| 0.6841 \| 79.0 \| 8453 \| 1.3479 \| 0.2835 \| 0.568 \| 0.2334 \| 0.1536 \| 0.2137 \| 0.483 \| 0.2937 \| 0.4524 \| 0.4712 \| 0.2171 \| 0.4099 \| 0.7032 \| 0.5365 \| 0.6811 \| 0.2659 \| 0.4709 \| 0.1912 \| 0.4027 \| 0.1389 \| 0.4108 \| 0.2852 \| 0.3907 \| \| 0.6772 \| 80.0 \| 8560 \| 1.3513 \| 0.2853 \| 0.5623 \| 0.2438 \| 0.1542 \| 0.2224 \| 0.4758 \| 0.2944 \| 0.4557 \| 0.4709 \| 0.2264 \| 0.4131 \| 0.7013 \| 0.5309 \| 0.6784 \| 0.266 \| 0.4671 \| 0.1991 \| 0.4103 \| 0.1429 \| 0.4 \| 0.2877 \| 0.3987 \| \| 0.6748 \| 81.0 \| 8667 \| 1.3345 \| 0.2853 \| 0.5691 \| 0.2438 \| 0.1607 \| 0.2242 \| 0.4818 \| 0.2931 \| 0.4543 \| 0.469 \| 0.2335 \| 0.421 \| 0.6924 \| 0.5383 \| 0.6851 \| 0.2584 \| 0.4519 \| 0.2001 \| 0.4031 \| 0.1403 \| 0.4 \| 0.2894 \| 0.4049 \| \| 0.6601 \| 82.0 \| 8774 \| 1.3315 \| 0.291 \| 0.574 \| 0.2597 \| 0.1627 \| 0.2333 \| 0.4837 \| 0.2899 \| 0.4605 \| 0.4754 \| 0.2354 \| 0.4348 \| 0.6868 \| 0.5364 \| 0.6797 \| 0.2761 \| 0.4797 \| 0.1991 \| 0.404 \| 0.143 \| 0.4031 \| 0.3003 \| 0.4102 \| \| 0.6598 \| 83.0 \| 8881 \| 1.3230 \| 0.2917 \| 0.5794 \| 0.2537 \| 0.1385 \| 0.2287 \| 0.4916 \| 0.2912 \| 0.4621 \| 0.477 \| 0.2305 \| 0.4339 \| 0.6913 \| 0.5473 \| 0.6892 \| 0.2669 \| 0.4747 \| 0.1975 \| 0.4205 \| 0.1506 \| 0.3938 \| 0.296 \| 0.4067 \| \| 0.6577 \| 84.0 \| 8988 \| 1.3283 \| 0.2947 \| 0.5809 \| 0.2681 \| 0.1583 \| 0.23 \| 0.4971 \| 0.2972 \| 0.4633 \| 0.4771 \| 0.2237 \| 0.4317 \| 0.7008 \| 0.5445 \| 0.6829 \| 0.2753 \| 0.4937 \| 0.2028 \| 0.4098 \| 0.151 \| 0.3938 \| 0.3002 \| 0.4053 \| \| 0.6657 \| 85.0 \| 9095 \| 1.3270 \| 0.292 \| 0.5805 \| 0.2547 \| 0.1669 \| 0.224 \| 0.4878 \| 0.2968 \| 0.46 \| 0.4757 \| 0.2361 \| 0.4245 \| 0.699 \| 0.5422 \| 0.6865 \| 0.2658 \| 0.4696 \| 0.2014 \| 0.4107 \| 0.1552 \| 0.4 \| 0.2954 \| 0.4116 \| \| 0.6451 \| 86.0 \| 9202 \| 1.3250 \| 0.2898 \| 0.5695 \| 0.2528 \| 0.1706 \| 0.2256 \| 0.4978 \| 0.2932 \| 0.4548 \| 0.4709 \| 0.225 \| 0.4229 \| 0.6988 \| 0.5352 \| 0.6766 \| 0.2675 \| 0.4797 \| 0.1967 \| 0.396 \| 0.1485 \| 0.3938 \| 0.3011 \| 0.4084 \| \| 0.6501 \| 87.0 \| 9309 \| 1.3576 \| 0.2934 \| 0.5824 \| 0.2498 \| 0.1556 \| 0.2243 \| 0.4893 \| 0.2938 \| 0.4633 \| 0.4791 \| 0.2446 \| 0.4291 \| 0.702 \| 0.5461 \| 0.6847 \| 0.2651 \| 0.4899 \| 0.2 \| 0.3969 \| 0.1569 \| 0.4108 \| 0.2989 \| 0.4133 \| \| 0.6444 \| 88.0 \| 9416 \| 1.3638 \| 0.2911 \| 0.5818 \| 0.2514 \| 0.1715 \| 0.2217 \| 0.4913 \| 0.2945 \| 0.4576 \| 0.4745 \| 0.2477 \| 0.4226 \| 0.6918 \| 0.5451 \| 0.6788 \| 0.2577 \| 0.4835 \| 0.2045 \| 0.4049 \| 0.1554 \| 0.3969 \| 0.2929 \| 0.4084 \| \| 0.6275 \| 89.0 \| 9523 \| 1.3529 \| 0.2908 \| 0.5777 \| 0.2417 \| 0.1722 \| 0.2221 \| 0.4779 \| 0.2948 \| 0.4575 \| 0.4722 \| 0.2504 \| 0.4186 \| 0.6857 \| 0.5422 \| 0.6734 \| 0.2696 \| 0.481 \| 0.2097 \| 0.4036 \| 0.1463 \| 0.3938 \| 0.2861 \| 0.4093 \| \| 0.6394 \| 90.0 \| 9630 \| 1.3503 \| 0.2913 \| 0.5775 \| 0.2445 \| 0.159 \| 0.2181 \| 0.4954 \| 0.2935 \| 0.4579 \| 0.4713 \| 0.2362 \| 0.4156 \| 0.6884 \| 0.5438 \| 0.6784 \| 0.2697 \| 0.4835 \| 0.2041 \| 0.4045 \| 0.1519 \| 0.3862 \| 0.2869 \| 0.404 \| \| 0.6301 \| 91.0 \| 9737 \| 1.3381 \| 0.2914 \| 0.5775 \| 0.2397 \| 0.1611 \| 0.2246 \| 0.4956 \| 0.2963 \| 0.4591 \| 0.4737 \| 0.241 \| 0.426 \| 0.6906 \| 0.5477 \| 0.6802 \| 0.262 \| 0.4772 \| 0.2076 \| 0.408 \| 0.1554 \| 0.4 \| 0.2843 \| 0.4031 \| \| 0.632 \| 92.0 \| 9844 \| 1.3426 \| 0.2911 \| 0.573 \| 0.2416 \| 0.1699 \| 0.2282 \| 0.4914 \| 0.2991 \| 0.4619 \| 0.4751 \| 0.2436 \| 0.4282 \| 0.6894 \| 0.5428 \| 0.6833 \| 0.2679 \| 0.4886 \| 0.2091 \| 0.3955 \| 0.1519 \| 0.4077 \| 0.2836 \| 0.4004 \| \| 0.6231 \| 93.0 \| 9951 \| 1.3458 \| 0.294 \| 0.5787 \| 0.2491 \| 0.1695 \| 0.2263 \| 0.4898 \| 0.298 \| 0.4621 \| 0.4764 \| 0.2407 \| 0.4302 \| 0.6841 \| 0.546 \| 0.6784 \| 0.2697 \| 0.4797 \| 0.2138 \| 0.404 \| 0.1485 \| 0.4062 \| 0.292 \| 0.4138 \| \| 0.6162 \| 94.0 \| 10058 \| 1.3339 \| 0.2915 \| 0.5819 \| 0.2501 \| 0.1638 \| 0.2224 \| 0.491 \| 0.2978 \| 0.4607 \| 0.477 \| 0.2425 \| 0.4217 \| 0.697 \| 0.5445 \| 0.6793 \| 0.2713 \| 0.4759 \| 0.2092 \| 0.404 \| 0.1482 \| 0.4215 \| 0.2844 \| 0.404 \| \| 0.6249 \| 95.0 \| 10165 \| 1.3501 \| 0.2905 \| 0.583 \| 0.2494 \| 0.1697 \| 0.2233 \| 0.4881 \| 0.2969 \| 0.4602 \| 0.4746 \| 0.243 \| 0.4209 \| 0.6892 \| 0.5373 \| 0.6739 \| 0.2717 \| 0.4937 \| 0.2109 \| 0.4022 \| 0.1501 \| 0.4046 \| 0.2826 \| 0.3987 \| \| 0.6189 \| 96.0 \| 10272 \| 1.3529 \| 0.2904 \| 0.5798 \| 0.2422 \| 0.1658 \| 0.2242 \| 0.4877 \| 0.2938 \| 0.4631 \| 0.4772 \| 0.2482 \| 0.4268 \| 0.6855 \| 0.5395 \| 0.6788 \| 0.279 \| 0.4924 \| 0.2083 \| 0.3973 \| 0.1388 \| 0.4154 \| 0.2865 \| 0.4022 \| \| 0.6135 \| 97.0 \| 10379 \| 1.3553 \| 0.2929 \| 0.5853 \| 0.248 \| 0.167 \| 0.2251 \| 0.4901 \| 0.2972 \| 0.4593 \| 0.4765 \| 0.2379 \| 0.4222 \| 0.6929 \| 0.5369 \| 0.6788 \| 0.2785 \| 0.4861 \| 0.2083 \| 0.3987 \| 0.1524 \| 0.4138 \| 0.2887 \| 0.4049 \| \| 0.613 \| 98.0 \| 10486 \| 1.3622 \| 0.2938 \| 0.5851 \| 0.2432 \| 0.1642 \| 0.2285 \| 0.4894 \| 0.2958 \| 0.4603 \| 0.4747 \| 0.2408 \| 0.4212 \| 0.6916 \| 0.5406 \| 0.6766 \| 0.2774 \| 0.4848 \| 0.2088 \| 0.3938 \| 0.1525 \| 0.4092 \| 0.2899 \| 0.4089 \| \| 0.6144 \| 99.0 \| 10593 \| 1.3536 \| 0.2933 \| 0.5833 \| 0.245 \| 0.1634 \| 0.2282 \| 0.4935 \| 0.2962 \| 0.4589 \| 0.4745 \| 0.2419 \| 0.4225 \| 0.6914 \| 0.5414 \| 0.6793 \| 0.277 \| 0.4873 \| 0.2061 \| 0.3942 \| 0.1554 \| 0.4092 \| 0.2869 \| 0.4027 \| \| 0.613 \| 100.0 \| 10700 \| 1.3550 \| 0.2936 \| 0.5835 \| 0.2461 \| 0.1635 \| 0.2284 \| 0.4956 \| 0.2943 \| 0.4587 \| 0.4737 \| 0.2424 \| 0.4204 \| 0.6907 \| 0.5418 \| 0.6788 \| 0.2801 \| 0.481 \| 0.2075 \| 0.3955 \| 0.1534 \| 0.4108 \| 0.2852 \| 0.4022 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-manual-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/feu7hgwm) # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5-manual-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/3q9cy9us) # jozhang97-deta-resnet-50-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-manual-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/nzag6oys) # sensetime-deformable-detr-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/lgrnumaa) # hustvl-yolos-small-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
Spatiallysaying/detr-finetuned-rwymarkings-horizontal-v1	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6" ]
nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session8	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session8 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]
tooltest/detr	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "clothing", "shoes", "bags", "accessories" ]
qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-auto-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/gno0q6ox) # sensetime-deformable-detr-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/vn7jioeq) # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
Spatiallysaying/detr-finetuned-runwaymarkings-Horizontal-v1	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6" ]
qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/6eb20ojg) # facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5-auto-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/lcafrooz) # jozhang97-deta-resnet-50-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-auto-pad	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/d58yz1w7) # hustvl-yolos-small-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
schoonhovenra/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
schoonhovenra/transformer-OD	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # transformer-OD This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
schoonhovenra/20240429_115939	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 20240429_115939 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-reproduce	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/tuning-sota-cppe5/runs/w975f3w4) # sensetime-deformable-detr-finetuned-10k-cppe5-reproduce This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0727 - Map: 0.3471 - Map 50: 0.6376 - Map 75: 0.3269 - Map Small: 0.2239 - Map Medium: 0.2687 - Map Large: 0.5542 - Mar 1: 0.3146 - Mar 10: 0.485 - Mar 100: 0.5045 - Mar Small: 0.3033 - Mar Medium: 0.4315 - Mar Large: 0.7048 - Map Coverall: 0.556 - Mar 100 Coverall: 0.6671 - Map Face Shield: 0.3346 - Mar 100 Face Shield: 0.4873 - Map Gloves: 0.2879 - Mar 100 Gloves: 0.4625 - Map Goggles: 0.2348 - Mar 100 Goggles: 0.44 - Map Mask: 0.3221 - Mar 100 Mask: 0.4653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 11.6745 \| 0.9953 \| 106 \| 1.7743 \| 0.0167 \| 0.0443 \| 0.0098 \| 0.0113 \| 0.0253 \| 0.0122 \| 0.0331 \| 0.1258 \| 0.1673 \| 0.0575 \| 0.155 \| 0.178 \| 0.0056 \| 0.2622 \| 0.008 \| 0.1329 \| 0.0044 \| 0.1192 \| 0.0001 \| 0.0292 \| 0.0654 \| 0.2929 \| \| 1.5556 \| 2.0 \| 213 \| 1.5655 \| 0.0293 \| 0.0676 \| 0.0249 \| 0.0265 \| 0.0367 \| 0.0521 \| 0.042 \| 0.1789 \| 0.2423 \| 0.1235 \| 0.2148 \| 0.3336 \| 0.0138 \| 0.3158 \| 0.0073 \| 0.2038 \| 0.0123 \| 0.2844 \| 0.0001 \| 0.0185 \| 0.1131 \| 0.3889 \| \| 1.4143 \| 2.9953 \| 319 \| 1.4570 \| 0.074 \| 0.1565 \| 0.0634 \| 0.0315 \| 0.0468 \| 0.0894 \| 0.1088 \| 0.2778 \| 0.3228 \| 0.1222 \| 0.2836 \| 0.3768 \| 0.2084 \| 0.5955 \| 0.013 \| 0.2203 \| 0.0184 \| 0.3112 \| 0.0033 \| 0.1246 \| 0.1267 \| 0.3627 \| \| 1.2557 \| 4.0 \| 426 \| 1.3799 \| 0.1076 \| 0.212 \| 0.099 \| 0.0439 \| 0.0618 \| 0.1269 \| 0.1323 \| 0.3206 \| 0.3609 \| 0.1514 \| 0.3238 \| 0.4669 \| 0.3272 \| 0.6257 \| 0.0165 \| 0.257 \| 0.0337 \| 0.3634 \| 0.0069 \| 0.16 \| 0.1537 \| 0.3987 \| \| 1.1941 \| 4.9953 \| 532 \| 1.3209 \| 0.1144 \| 0.2176 \| 0.1087 \| 0.0438 \| 0.0713 \| 0.151 \| 0.1434 \| 0.3609 \| 0.4018 \| 0.2001 \| 0.3457 \| 0.564 \| 0.3425 \| 0.6644 \| 0.0144 \| 0.2949 \| 0.0407 \| 0.3665 \| 0.0154 \| 0.2585 \| 0.1591 \| 0.4244 \| \| 1.155 \| 6.0 \| 639 \| 1.2990 \| 0.1515 \| 0.2906 \| 0.1425 \| 0.0672 \| 0.1129 \| 0.1945 \| 0.1753 \| 0.3935 \| 0.4237 \| 0.1877 \| 0.368 \| 0.598 \| 0.4158 \| 0.6707 \| 0.0406 \| 0.3203 \| 0.0925 \| 0.3951 \| 0.0189 \| 0.3138 \| 0.1894 \| 0.4187 \| \| 1.1215 \| 6.9953 \| 745 \| 1.2747 \| 0.1571 \| 0.309 \| 0.143 \| 0.0696 \| 0.125 \| 0.1889 \| 0.1724 \| 0.3909 \| 0.43 \| 0.2033 \| 0.3851 \| 0.604 \| 0.4395 \| 0.6743 \| 0.0498 \| 0.381 \| 0.0871 \| 0.4036 \| 0.018 \| 0.2692 \| 0.1911 \| 0.4218 \| \| 1.0717 \| 8.0 \| 852 \| 1.2178 \| 0.1571 \| 0.3227 \| 0.1375 \| 0.0901 \| 0.1195 \| 0.2164 \| 0.198 \| 0.4041 \| 0.4433 \| 0.2476 \| 0.4 \| 0.5814 \| 0.4163 \| 0.6811 \| 0.0407 \| 0.3582 \| 0.0893 \| 0.4116 \| 0.049 \| 0.3246 \| 0.1903 \| 0.4409 \| \| 1.0565 \| 8.9953 \| 958 \| 1.2809 \| 0.1655 \| 0.3272 \| 0.1587 \| 0.091 \| 0.1352 \| 0.2086 \| 0.2214 \| 0.4057 \| 0.4395 \| 0.2224 \| 0.3794 \| 0.6548 \| 0.3741 \| 0.6495 \| 0.0995 \| 0.4316 \| 0.098 \| 0.3714 \| 0.0485 \| 0.3277 \| 0.2076 \| 0.4173 \| \| 1.0112 \| 10.0 \| 1065 \| 1.1774 \| 0.2008 \| 0.3844 \| 0.2 \| 0.0948 \| 0.1963 \| 0.2942 \| 0.2258 \| 0.4368 \| 0.4664 \| 0.273 \| 0.4293 \| 0.6294 \| 0.4777 \| 0.6856 \| 0.0681 \| 0.343 \| 0.1224 \| 0.4321 \| 0.09 \| 0.3938 \| 0.246 \| 0.4773 \| \| 0.9978 \| 10.9953 \| 1171 \| 1.1756 \| 0.2299 \| 0.4406 \| 0.2158 \| 0.0987 \| 0.208 \| 0.3558 \| 0.2403 \| 0.4529 \| 0.4784 \| 0.2767 \| 0.4338 \| 0.6759 \| 0.474 \| 0.6856 \| 0.1359 \| 0.4127 \| 0.1528 \| 0.4388 \| 0.1218 \| 0.3923 \| 0.2652 \| 0.4627 \| \| 0.9543 \| 12.0 \| 1278 \| 1.1263 \| 0.2416 \| 0.459 \| 0.226 \| 0.129 \| 0.2064 \| 0.3378 \| 0.2497 \| 0.4603 \| 0.4893 \| 0.3098 \| 0.439 \| 0.6639 \| 0.51 \| 0.6892 \| 0.133 \| 0.4228 \| 0.1824 \| 0.4393 \| 0.116 \| 0.4385 \| 0.2665 \| 0.4569 \| \| 0.9578 \| 12.9953 \| 1384 \| 1.1217 \| 0.2622 \| 0.4835 \| 0.2467 \| 0.1248 \| 0.2321 \| 0.4226 \| 0.2676 \| 0.4575 \| 0.4884 \| 0.3023 \| 0.4572 \| 0.661 \| 0.5304 \| 0.6977 \| 0.172 \| 0.4139 \| 0.1784 \| 0.4603 \| 0.1386 \| 0.4062 \| 0.2917 \| 0.464 \| \| 0.9196 \| 14.0 \| 1491 \| 1.1083 \| 0.26 \| 0.4885 \| 0.2469 \| 0.1338 \| 0.2185 \| 0.3915 \| 0.2684 \| 0.4657 \| 0.4912 \| 0.2804 \| 0.4423 \| 0.6744 \| 0.5478 \| 0.7005 \| 0.1698 \| 0.4684 \| 0.1808 \| 0.4246 \| 0.1119 \| 0.4 \| 0.2895 \| 0.4627 \| \| 0.8938 \| 14.9953 \| 1597 \| 1.1487 \| 0.2506 \| 0.4849 \| 0.2305 \| 0.1269 \| 0.2174 \| 0.3739 \| 0.2597 \| 0.4527 \| 0.4777 \| 0.2813 \| 0.4375 \| 0.6726 \| 0.5043 \| 0.6748 \| 0.1642 \| 0.462 \| 0.1753 \| 0.4321 \| 0.125 \| 0.3785 \| 0.2842 \| 0.4413 \| \| 0.8907 \| 16.0 \| 1704 \| 1.1091 \| 0.2545 \| 0.4905 \| 0.2477 \| 0.1267 \| 0.2276 \| 0.392 \| 0.2634 \| 0.4574 \| 0.4881 \| 0.3075 \| 0.4439 \| 0.6852 \| 0.524 \| 0.695 \| 0.1588 \| 0.4443 \| 0.188 \| 0.4634 \| 0.1298 \| 0.3815 \| 0.2717 \| 0.4564 \| \| 0.8669 \| 16.9953 \| 1810 \| 1.0938 \| 0.2838 \| 0.5311 \| 0.274 \| 0.1446 \| 0.2359 \| 0.4327 \| 0.2853 \| 0.4676 \| 0.49 \| 0.3005 \| 0.444 \| 0.6873 \| 0.5439 \| 0.6905 \| 0.2054 \| 0.457 \| 0.2177 \| 0.4487 \| 0.1542 \| 0.4046 \| 0.2976 \| 0.4493 \| \| 0.8495 \| 18.0 \| 1917 \| 1.0866 \| 0.2755 \| 0.5134 \| 0.2554 \| 0.1362 \| 0.2355 \| 0.4541 \| 0.2681 \| 0.4548 \| 0.4799 \| 0.2886 \| 0.4362 \| 0.6823 \| 0.5533 \| 0.6995 \| 0.1514 \| 0.4418 \| 0.2165 \| 0.4598 \| 0.159 \| 0.3492 \| 0.2974 \| 0.4493 \| \| 0.8421 \| 18.9953 \| 2023 \| 1.0856 \| 0.2719 \| 0.5259 \| 0.2519 \| 0.1366 \| 0.2341 \| 0.4521 \| 0.28 \| 0.4539 \| 0.4769 \| 0.3123 \| 0.4265 \| 0.6567 \| 0.5057 \| 0.6459 \| 0.1693 \| 0.4418 \| 0.2233 \| 0.4629 \| 0.1574 \| 0.3677 \| 0.304 \| 0.4662 \| \| 0.813 \| 20.0 \| 2130 \| 1.0726 \| 0.2846 \| 0.5348 \| 0.2736 \| 0.1497 \| 0.2304 \| 0.4655 \| 0.2851 \| 0.4696 \| 0.4932 \| 0.3084 \| 0.4391 \| 0.6959 \| 0.5259 \| 0.6586 \| 0.1864 \| 0.5051 \| 0.2468 \| 0.4406 \| 0.1622 \| 0.3969 \| 0.3018 \| 0.4649 \| \| 0.8215 \| 20.9953 \| 2236 \| 1.0397 \| 0.2966 \| 0.5453 \| 0.2992 \| 0.1696 \| 0.2338 \| 0.4806 \| 0.2887 \| 0.4835 \| 0.5056 \| 0.3419 \| 0.4607 \| 0.694 \| 0.5695 \| 0.6973 \| 0.1967 \| 0.5051 \| 0.2348 \| 0.4647 \| 0.1719 \| 0.3815 \| 0.3103 \| 0.4796 \| \| 0.7876 \| 22.0 \| 2343 \| 1.0477 \| 0.2908 \| 0.5476 \| 0.2759 \| 0.1466 \| 0.2439 \| 0.4577 \| 0.287 \| 0.4747 \| 0.5011 \| 0.3174 \| 0.4614 \| 0.6895 \| 0.5722 \| 0.6959 \| 0.1964 \| 0.4848 \| 0.2358 \| 0.4656 \| 0.1462 \| 0.3892 \| 0.3034 \| 0.4698 \| \| 0.8012 \| 22.9953 \| 2449 \| 1.0780 \| 0.2829 \| 0.5255 \| 0.2685 \| 0.137 \| 0.2255 \| 0.4389 \| 0.2786 \| 0.4624 \| 0.4898 \| 0.2655 \| 0.44 \| 0.689 \| 0.5429 \| 0.6676 \| 0.2043 \| 0.4772 \| 0.2418 \| 0.4616 \| 0.1156 \| 0.3646 \| 0.3101 \| 0.4782 \| \| 0.7738 \| 24.0 \| 2556 \| 1.0447 \| 0.3086 \| 0.5717 \| 0.296 \| 0.18 \| 0.2464 \| 0.4829 \| 0.286 \| 0.4777 \| 0.5067 \| 0.3497 \| 0.4605 \| 0.6955 \| 0.5613 \| 0.6815 \| 0.2118 \| 0.4797 \| 0.2567 \| 0.4679 \| 0.1977 \| 0.4123 \| 0.3156 \| 0.492 \| \| 0.7738 \| 24.9953 \| 2662 \| 1.0471 \| 0.3099 \| 0.5827 \| 0.2977 \| 0.1699 \| 0.2539 \| 0.4816 \| 0.2908 \| 0.465 \| 0.4846 \| 0.3136 \| 0.4363 \| 0.6683 \| 0.5595 \| 0.6761 \| 0.2114 \| 0.4468 \| 0.2579 \| 0.4571 \| 0.2039 \| 0.3846 \| 0.3166 \| 0.4582 \| \| 0.7549 \| 26.0 \| 2769 \| 1.0618 \| 0.3113 \| 0.5754 \| 0.289 \| 0.1962 \| 0.2495 \| 0.4786 \| 0.2938 \| 0.4705 \| 0.4954 \| 0.2923 \| 0.4491 \| 0.6872 \| 0.5627 \| 0.6802 \| 0.2508 \| 0.4899 \| 0.2579 \| 0.45 \| 0.1777 \| 0.3954 \| 0.3074 \| 0.4613 \| \| 0.7715 \| 26.9953 \| 2875 \| 1.0328 \| 0.313 \| 0.5867 \| 0.2898 \| 0.1915 \| 0.2419 \| 0.4874 \| 0.2935 \| 0.4789 \| 0.5051 \| 0.3092 \| 0.4417 \| 0.7034 \| 0.5449 \| 0.6815 \| 0.2329 \| 0.4861 \| 0.26 \| 0.4741 \| 0.1915 \| 0.4 \| 0.3356 \| 0.484 \| \| 0.7386 \| 28.0 \| 2982 \| 1.0318 \| 0.3182 \| 0.5894 \| 0.2969 \| 0.2087 \| 0.2584 \| 0.4936 \| 0.3043 \| 0.4905 \| 0.5203 \| 0.3489 \| 0.463 \| 0.6977 \| 0.5581 \| 0.6761 \| 0.2404 \| 0.5 \| 0.2663 \| 0.4871 \| 0.1996 \| 0.4431 \| 0.3264 \| 0.4951 \| \| 0.7388 \| 28.9953 \| 3088 \| 1.0309 \| 0.3151 \| 0.5908 \| 0.2953 \| 0.1714 \| 0.2506 \| 0.4845 \| 0.299 \| 0.4833 \| 0.507 \| 0.3136 \| 0.4511 \| 0.7004 \| 0.5507 \| 0.6874 \| 0.2508 \| 0.4937 \| 0.2607 \| 0.4696 \| 0.191 \| 0.4138 \| 0.3224 \| 0.4707 \| \| 0.7092 \| 30.0 \| 3195 \| 1.0388 \| 0.3165 \| 0.5937 \| 0.2909 \| 0.1496 \| 0.2446 \| 0.5126 \| 0.2976 \| 0.4785 \| 0.5024 \| 0.2835 \| 0.4463 \| 0.6904 \| 0.5549 \| 0.6838 \| 0.2498 \| 0.4962 \| 0.2718 \| 0.467 \| 0.2004 \| 0.4062 \| 0.3053 \| 0.4591 \| \| 0.7114 \| 30.9953 \| 3301 \| 1.0427 \| 0.3102 \| 0.5886 \| 0.286 \| 0.1976 \| 0.2561 \| 0.4823 \| 0.2985 \| 0.4676 \| 0.4946 \| 0.2994 \| 0.445 \| 0.7112 \| 0.5535 \| 0.6806 \| 0.2505 \| 0.4975 \| 0.2679 \| 0.4621 \| 0.167 \| 0.3769 \| 0.3123 \| 0.456 \| \| 0.7032 \| 32.0 \| 3408 \| 1.0404 \| 0.3168 \| 0.5916 \| 0.3 \| 0.2018 \| 0.2554 \| 0.5038 \| 0.2933 \| 0.4776 \| 0.5102 \| 0.3669 \| 0.4527 \| 0.7007 \| 0.5593 \| 0.6887 \| 0.244 \| 0.4823 \| 0.2684 \| 0.4853 \| 0.1801 \| 0.4123 \| 0.332 \| 0.4822 \| \| 0.7006 \| 32.9953 \| 3514 \| 1.0246 \| 0.3287 \| 0.5948 \| 0.3158 \| 0.21 \| 0.2597 \| 0.5103 \| 0.304 \| 0.4967 \| 0.522 \| 0.3417 \| 0.4715 \| 0.7227 \| 0.5832 \| 0.7014 \| 0.2496 \| 0.519 \| 0.2767 \| 0.475 \| 0.1936 \| 0.4308 \| 0.3405 \| 0.484 \| \| 0.6886 \| 34.0 \| 3621 \| 1.0414 \| 0.3186 \| 0.5938 \| 0.2917 \| 0.1831 \| 0.2501 \| 0.5168 \| 0.2984 \| 0.4753 \| 0.5011 \| 0.2782 \| 0.4546 \| 0.689 \| 0.5766 \| 0.7086 \| 0.2695 \| 0.5089 \| 0.2707 \| 0.4629 \| 0.1621 \| 0.3738 \| 0.3141 \| 0.4511 \| \| 0.7005 \| 34.9953 \| 3727 \| 1.0315 \| 0.3403 \| 0.6141 \| 0.3297 \| 0.1953 \| 0.264 \| 0.5513 \| 0.3007 \| 0.4799 \| 0.5053 \| 0.2818 \| 0.4534 \| 0.7013 \| 0.5757 \| 0.6896 \| 0.3081 \| 0.5063 \| 0.268 \| 0.4621 \| 0.2108 \| 0.3908 \| 0.339 \| 0.4778 \| \| 0.6736 \| 36.0 \| 3834 \| 1.0174 \| 0.3302 \| 0.5993 \| 0.3115 \| 0.2041 \| 0.263 \| 0.5133 \| 0.3003 \| 0.4861 \| 0.5088 \| 0.2864 \| 0.4674 \| 0.69 \| 0.5769 \| 0.6973 \| 0.2593 \| 0.4684 \| 0.278 \| 0.492 \| 0.2139 \| 0.4231 \| 0.323 \| 0.4636 \| \| 0.6713 \| 36.9953 \| 3940 \| 1.0260 \| 0.3312 \| 0.5975 \| 0.3118 \| 0.2167 \| 0.2652 \| 0.5174 \| 0.3112 \| 0.4891 \| 0.5127 \| 0.3276 \| 0.4565 \| 0.7097 \| 0.5684 \| 0.6914 \| 0.2784 \| 0.5063 \| 0.2671 \| 0.4603 \| 0.2226 \| 0.4354 \| 0.3192 \| 0.4702 \| \| 0.6572 \| 38.0 \| 4047 \| 1.0167 \| 0.3431 \| 0.6228 \| 0.3377 \| 0.2027 \| 0.2728 \| 0.5576 \| 0.3079 \| 0.4877 \| 0.5142 \| 0.3312 \| 0.4538 \| 0.7063 \| 0.5705 \| 0.6811 \| 0.2904 \| 0.5063 \| 0.2847 \| 0.4817 \| 0.2318 \| 0.4169 \| 0.3381 \| 0.4849 \| \| 0.6608 \| 38.9953 \| 4153 \| 1.0307 \| 0.3428 \| 0.6126 \| 0.3359 \| 0.21 \| 0.2775 \| 0.5477 \| 0.3043 \| 0.4884 \| 0.515 \| 0.314 \| 0.4668 \| 0.7124 \| 0.5706 \| 0.691 \| 0.3056 \| 0.5367 \| 0.2919 \| 0.479 \| 0.2123 \| 0.3923 \| 0.3336 \| 0.476 \| \| 0.63 \| 40.0 \| 4260 \| 1.0264 \| 0.3443 \| 0.6177 \| 0.3276 \| 0.2114 \| 0.2806 \| 0.5421 \| 0.3121 \| 0.4916 \| 0.5202 \| 0.3188 \| 0.4705 \| 0.7166 \| 0.5783 \| 0.691 \| 0.3117 \| 0.5291 \| 0.2732 \| 0.4817 \| 0.2299 \| 0.4308 \| 0.3282 \| 0.4684 \| \| 0.6396 \| 40.9953 \| 4366 \| 1.0215 \| 0.3425 \| 0.621 \| 0.3171 \| 0.2082 \| 0.2728 \| 0.5362 \| 0.3092 \| 0.4871 \| 0.5079 \| 0.2951 \| 0.4554 \| 0.7131 \| 0.5877 \| 0.7027 \| 0.3052 \| 0.4937 \| 0.288 \| 0.475 \| 0.1991 \| 0.4 \| 0.3324 \| 0.468 \| \| 0.625 \| 42.0 \| 4473 \| 1.0178 \| 0.3484 \| 0.6218 \| 0.3461 \| 0.2131 \| 0.2829 \| 0.5369 \| 0.3012 \| 0.4949 \| 0.5168 \| 0.3244 \| 0.4631 \| 0.7096 \| 0.5735 \| 0.6932 \| 0.3088 \| 0.5127 \| 0.2923 \| 0.479 \| 0.2422 \| 0.4246 \| 0.3251 \| 0.4742 \| \| 0.6251 \| 42.9953 \| 4579 \| 1.0301 \| 0.3461 \| 0.6247 \| 0.3439 \| 0.2131 \| 0.2702 \| 0.5491 \| 0.306 \| 0.4934 \| 0.5121 \| 0.2848 \| 0.4461 \| 0.7165 \| 0.5833 \| 0.6901 \| 0.301 \| 0.5051 \| 0.2873 \| 0.4835 \| 0.2335 \| 0.4123 \| 0.3255 \| 0.4693 \| \| 0.6135 \| 44.0 \| 4686 \| 1.0139 \| 0.3493 \| 0.6317 \| 0.3372 \| 0.2248 \| 0.2862 \| 0.5572 \| 0.3072 \| 0.4894 \| 0.5117 \| 0.3177 \| 0.4626 \| 0.7082 \| 0.5812 \| 0.6914 \| 0.3109 \| 0.5152 \| 0.2954 \| 0.4839 \| 0.223 \| 0.3846 \| 0.3361 \| 0.4836 \| \| 0.6144 \| 44.9953 \| 4792 \| 1.0285 \| 0.3509 \| 0.6356 \| 0.3476 \| 0.224 \| 0.2788 \| 0.5406 \| 0.3077 \| 0.4964 \| 0.5158 \| 0.3294 \| 0.4636 \| 0.7093 \| 0.5641 \| 0.6739 \| 0.3102 \| 0.5013 \| 0.2928 \| 0.4839 \| 0.2503 \| 0.4338 \| 0.3372 \| 0.4862 \| \| 0.6108 \| 46.0 \| 4899 \| 1.0337 \| 0.3399 \| 0.6192 \| 0.3317 \| 0.2009 \| 0.2756 \| 0.5243 \| 0.2977 \| 0.4824 \| 0.503 \| 0.2895 \| 0.4587 \| 0.6878 \| 0.5765 \| 0.6788 \| 0.3125 \| 0.4975 \| 0.2873 \| 0.4719 \| 0.2004 \| 0.3923 \| 0.323 \| 0.4747 \| \| 0.6081 \| 46.9953 \| 5005 \| 1.0228 \| 0.3444 \| 0.6169 \| 0.341 \| 0.2173 \| 0.281 \| 0.5443 \| 0.3041 \| 0.4928 \| 0.5181 \| 0.3604 \| 0.462 \| 0.7082 \| 0.5721 \| 0.6887 \| 0.313 \| 0.5038 \| 0.2941 \| 0.4754 \| 0.2061 \| 0.4292 \| 0.3367 \| 0.4933 \| \| 0.5857 \| 48.0 \| 5112 \| 1.0137 \| 0.348 \| 0.6196 \| 0.3419 \| 0.2091 \| 0.2733 \| 0.5542 \| 0.308 \| 0.4916 \| 0.5113 \| 0.297 \| 0.4554 \| 0.7015 \| 0.5836 \| 0.6905 \| 0.3218 \| 0.5089 \| 0.2964 \| 0.4848 \| 0.214 \| 0.3908 \| 0.3241 \| 0.4813 \| \| 0.5963 \| 48.9953 \| 5218 \| 1.0341 \| 0.3433 \| 0.6262 \| 0.3411 \| 0.218 \| 0.2703 \| 0.5395 \| 0.3019 \| 0.4899 \| 0.5166 \| 0.3304 \| 0.4661 \| 0.7068 \| 0.5829 \| 0.6941 \| 0.2951 \| 0.5013 \| 0.2938 \| 0.4888 \| 0.2044 \| 0.4123 \| 0.3405 \| 0.4862 \| \| 0.5828 \| 50.0 \| 5325 \| 1.0220 \| 0.3402 \| 0.6157 \| 0.3433 \| 0.2175 \| 0.2748 \| 0.5347 \| 0.3054 \| 0.496 \| 0.5184 \| 0.321 \| 0.4733 \| 0.7146 \| 0.5809 \| 0.6977 \| 0.3071 \| 0.4975 \| 0.29 \| 0.4857 \| 0.1992 \| 0.4292 \| 0.3238 \| 0.4818 \| \| 0.583 \| 50.9953 \| 5431 \| 1.0163 \| 0.3483 \| 0.6272 \| 0.3378 \| 0.2054 \| 0.2819 \| 0.554 \| 0.3081 \| 0.4954 \| 0.5174 \| 0.3028 \| 0.4757 \| 0.701 \| 0.5748 \| 0.6896 \| 0.3137 \| 0.5165 \| 0.2908 \| 0.4866 \| 0.234 \| 0.4185 \| 0.3284 \| 0.476 \| \| 0.5574 \| 52.0 \| 5538 \| 1.0144 \| 0.3462 \| 0.6241 \| 0.3344 \| 0.2138 \| 0.2757 \| 0.5384 \| 0.3036 \| 0.4917 \| 0.5163 \| 0.3087 \| 0.4663 \| 0.7117 \| 0.5794 \| 0.6941 \| 0.2983 \| 0.4987 \| 0.3086 \| 0.4853 \| 0.222 \| 0.4385 \| 0.3225 \| 0.4649 \| \| 0.565 \| 52.9953 \| 5644 \| 1.0384 \| 0.3361 \| 0.6159 \| 0.3305 \| 0.2078 \| 0.2691 \| 0.5349 \| 0.3004 \| 0.4874 \| 0.511 \| 0.3151 \| 0.4652 \| 0.7071 \| 0.5649 \| 0.6851 \| 0.2748 \| 0.4886 \| 0.2937 \| 0.4888 \| 0.222 \| 0.42 \| 0.325 \| 0.4724 \| \| 0.5545 \| 54.0 \| 5751 \| 1.0353 \| 0.338 \| 0.6151 \| 0.3341 \| 0.2055 \| 0.2616 \| 0.5487 \| 0.3045 \| 0.4796 \| 0.5039 \| 0.334 \| 0.4435 \| 0.7153 \| 0.5729 \| 0.6779 \| 0.2751 \| 0.4797 \| 0.2887 \| 0.4714 \| 0.2189 \| 0.4092 \| 0.3342 \| 0.4813 \| \| 0.5533 \| 54.9953 \| 5857 \| 1.0273 \| 0.3478 \| 0.6322 \| 0.3282 \| 0.2019 \| 0.2765 \| 0.5498 \| 0.3091 \| 0.4827 \| 0.5049 \| 0.289 \| 0.4568 \| 0.7001 \| 0.5784 \| 0.6815 \| 0.3102 \| 0.4975 \| 0.2974 \| 0.4799 \| 0.2306 \| 0.4031 \| 0.3223 \| 0.4627 \| \| 0.5343 \| 56.0 \| 5964 \| 1.0379 \| 0.3489 \| 0.631 \| 0.3353 \| 0.2161 \| 0.2711 \| 0.5601 \| 0.3114 \| 0.4885 \| 0.5098 \| 0.3114 \| 0.4584 \| 0.721 \| 0.5659 \| 0.6824 \| 0.3237 \| 0.5051 \| 0.3084 \| 0.4853 \| 0.2272 \| 0.4092 \| 0.3193 \| 0.4671 \| \| 0.5468 \| 56.9953 \| 6070 \| 1.0393 \| 0.3454 \| 0.6401 \| 0.3253 \| 0.2075 \| 0.2698 \| 0.5397 \| 0.3079 \| 0.4848 \| 0.5033 \| 0.3134 \| 0.4366 \| 0.686 \| 0.5642 \| 0.668 \| 0.3186 \| 0.4797 \| 0.2974 \| 0.4915 \| 0.2271 \| 0.4092 \| 0.3196 \| 0.468 \| \| 0.5431 \| 58.0 \| 6177 \| 1.0278 \| 0.344 \| 0.63 \| 0.3249 \| 0.2067 \| 0.2747 \| 0.5383 \| 0.3093 \| 0.4896 \| 0.5083 \| 0.2966 \| 0.4465 \| 0.7067 \| 0.5634 \| 0.6671 \| 0.3113 \| 0.4962 \| 0.2989 \| 0.4902 \| 0.2229 \| 0.4138 \| 0.3235 \| 0.4742 \| \| 0.5339 \| 58.9953 \| 6283 \| 1.0319 \| 0.3408 \| 0.6341 \| 0.3086 \| 0.214 \| 0.2713 \| 0.5379 \| 0.3031 \| 0.4853 \| 0.505 \| 0.2896 \| 0.4521 \| 0.7106 \| 0.5694 \| 0.6815 \| 0.2998 \| 0.4709 \| 0.3092 \| 0.4978 \| 0.2052 \| 0.4092 \| 0.3205 \| 0.4658 \| \| 0.5334 \| 60.0 \| 6390 \| 1.0279 \| 0.3496 \| 0.6412 \| 0.3359 \| 0.2139 \| 0.2859 \| 0.5352 \| 0.3089 \| 0.4929 \| 0.5133 \| 0.3019 \| 0.4679 \| 0.7023 \| 0.5635 \| 0.6802 \| 0.3222 \| 0.5 \| 0.3064 \| 0.4879 \| 0.2309 \| 0.4277 \| 0.3249 \| 0.4707 \| \| 0.5289 \| 60.9953 \| 6496 \| 1.0478 \| 0.3506 \| 0.6433 \| 0.321 \| 0.2228 \| 0.2723 \| 0.5336 \| 0.304 \| 0.4892 \| 0.5068 \| 0.297 \| 0.4413 \| 0.693 \| 0.5761 \| 0.6838 \| 0.3275 \| 0.4873 \| 0.303 \| 0.4696 \| 0.2246 \| 0.4308 \| 0.3221 \| 0.4622 \| \| 0.5091 \| 62.0 \| 6603 \| 1.0321 \| 0.3484 \| 0.6408 \| 0.3242 \| 0.2247 \| 0.2711 \| 0.5639 \| 0.309 \| 0.4896 \| 0.5119 \| 0.3139 \| 0.4471 \| 0.7109 \| 0.5561 \| 0.6779 \| 0.3219 \| 0.4848 \| 0.297 \| 0.4951 \| 0.245 \| 0.4308 \| 0.3222 \| 0.4707 \| \| 0.5239 \| 62.9953 \| 6709 \| 1.0277 \| 0.3516 \| 0.6421 \| 0.3371 \| 0.2329 \| 0.2764 \| 0.5504 \| 0.3045 \| 0.491 \| 0.5096 \| 0.3178 \| 0.4378 \| 0.7055 \| 0.5655 \| 0.6766 \| 0.3156 \| 0.4646 \| 0.3137 \| 0.4973 \| 0.2267 \| 0.44 \| 0.3365 \| 0.4693 \| \| 0.5025 \| 64.0 \| 6816 \| 1.0331 \| 0.3474 \| 0.6385 \| 0.3144 \| 0.2295 \| 0.286 \| 0.5378 \| 0.3095 \| 0.4949 \| 0.5131 \| 0.3183 \| 0.4571 \| 0.7047 \| 0.5603 \| 0.6878 \| 0.3093 \| 0.4722 \| 0.3043 \| 0.4866 \| 0.235 \| 0.4538 \| 0.328 \| 0.4649 \| \| 0.4991 \| 64.9953 \| 6922 \| 1.0323 \| 0.3457 \| 0.6359 \| 0.3158 \| 0.2193 \| 0.2808 \| 0.5367 \| 0.3091 \| 0.485 \| 0.507 \| 0.2986 \| 0.4511 \| 0.7043 \| 0.5653 \| 0.6838 \| 0.3106 \| 0.481 \| 0.2987 \| 0.4804 \| 0.2308 \| 0.4169 \| 0.3229 \| 0.4729 \| \| 0.4969 \| 66.0 \| 7029 \| 1.0250 \| 0.3448 \| 0.6468 \| 0.317 \| 0.23 \| 0.2781 \| 0.5449 \| 0.3031 \| 0.4919 \| 0.5109 \| 0.3493 \| 0.4479 \| 0.6951 \| 0.5477 \| 0.6707 \| 0.311 \| 0.4911 \| 0.3046 \| 0.4924 \| 0.2424 \| 0.4369 \| 0.318 \| 0.4631 \| \| 0.496 \| 66.9953 \| 7135 \| 1.0300 \| 0.3461 \| 0.6272 \| 0.325 \| 0.2262 \| 0.2772 \| 0.5318 \| 0.3097 \| 0.4913 \| 0.5118 \| 0.3102 \| 0.4598 \| 0.6967 \| 0.5751 \| 0.6941 \| 0.3067 \| 0.4696 \| 0.3018 \| 0.4812 \| 0.2244 \| 0.4415 \| 0.3224 \| 0.4724 \| \| 0.4788 \| 68.0 \| 7242 \| 1.0452 \| 0.3402 \| 0.6215 \| 0.3124 \| 0.2249 \| 0.2665 \| 0.5439 \| 0.3048 \| 0.4923 \| 0.5102 \| 0.338 \| 0.4531 \| 0.7045 \| 0.5618 \| 0.6806 \| 0.3051 \| 0.4797 \| 0.2956 \| 0.4741 \| 0.2261 \| 0.4492 \| 0.3127 \| 0.4671 \| \| 0.4807 \| 68.9953 \| 7348 \| 1.0359 \| 0.3461 \| 0.6413 \| 0.3141 \| 0.2239 \| 0.2736 \| 0.5437 \| 0.3079 \| 0.4836 \| 0.5028 \| 0.3072 \| 0.4486 \| 0.6881 \| 0.5605 \| 0.6685 \| 0.3124 \| 0.4696 \| 0.3014 \| 0.4808 \| 0.2347 \| 0.4231 \| 0.3216 \| 0.472 \| \| 0.4756 \| 70.0 \| 7455 \| 1.0310 \| 0.3522 \| 0.6459 \| 0.3281 \| 0.222 \| 0.2816 \| 0.5535 \| 0.3078 \| 0.4878 \| 0.5082 \| 0.2998 \| 0.4607 \| 0.7001 \| 0.5784 \| 0.6919 \| 0.3348 \| 0.4823 \| 0.2935 \| 0.4763 \| 0.2294 \| 0.4169 \| 0.325 \| 0.4738 \| \| 0.4738 \| 70.9953 \| 7561 \| 1.0440 \| 0.3422 \| 0.6271 \| 0.3121 \| 0.2157 \| 0.2757 \| 0.5429 \| 0.31 \| 0.483 \| 0.5072 \| 0.3103 \| 0.4482 \| 0.6956 \| 0.5563 \| 0.6703 \| 0.3091 \| 0.4835 \| 0.2995 \| 0.4848 \| 0.225 \| 0.4277 \| 0.3208 \| 0.4698 \| \| 0.4674 \| 72.0 \| 7668 \| 1.0492 \| 0.3474 \| 0.6321 \| 0.3275 \| 0.2241 \| 0.2692 \| 0.548 \| 0.314 \| 0.4834 \| 0.5043 \| 0.2946 \| 0.4371 \| 0.707 \| 0.5626 \| 0.6703 \| 0.3187 \| 0.4975 \| 0.295 \| 0.4754 \| 0.2413 \| 0.4123 \| 0.3192 \| 0.4658 \| \| 0.4592 \| 72.9953 \| 7774 \| 1.0344 \| 0.3545 \| 0.6457 \| 0.3418 \| 0.2373 \| 0.2789 \| 0.559 \| 0.314 \| 0.4912 \| 0.5084 \| 0.3124 \| 0.4394 \| 0.6999 \| 0.5673 \| 0.6766 \| 0.3326 \| 0.4949 \| 0.3024 \| 0.4728 \| 0.2468 \| 0.4277 \| 0.3234 \| 0.4702 \| \| 0.4584 \| 74.0 \| 7881 \| 1.0381 \| 0.3451 \| 0.6271 \| 0.3398 \| 0.2388 \| 0.2702 \| 0.5483 \| 0.3144 \| 0.4882 \| 0.5113 \| 0.3169 \| 0.4468 \| 0.7077 \| 0.5631 \| 0.6788 \| 0.3154 \| 0.4873 \| 0.2912 \| 0.4812 \| 0.2251 \| 0.4323 \| 0.3307 \| 0.4769 \| \| 0.4648 \| 74.9953 \| 7987 \| 1.0515 \| 0.3506 \| 0.6384 \| 0.3369 \| 0.2269 \| 0.2659 \| 0.5582 \| 0.3168 \| 0.4842 \| 0.5067 \| 0.3154 \| 0.4328 \| 0.6958 \| 0.5646 \| 0.6694 \| 0.3313 \| 0.4886 \| 0.2944 \| 0.4741 \| 0.2264 \| 0.4262 \| 0.3365 \| 0.4751 \| \| 0.4487 \| 76.0 \| 8094 \| 1.0408 \| 0.3479 \| 0.6399 \| 0.3232 \| 0.2223 \| 0.2783 \| 0.5555 \| 0.3149 \| 0.486 \| 0.5077 \| 0.322 \| 0.4466 \| 0.699 \| 0.5573 \| 0.6671 \| 0.3233 \| 0.4924 \| 0.2944 \| 0.4795 \| 0.2408 \| 0.4369 \| 0.3235 \| 0.4627 \| \| 0.4482 \| 76.9953 \| 8200 \| 1.0368 \| 0.3505 \| 0.6396 \| 0.3153 \| 0.2229 \| 0.2796 \| 0.5552 \| 0.3173 \| 0.487 \| 0.508 \| 0.3088 \| 0.4459 \| 0.7042 \| 0.5604 \| 0.6716 \| 0.3201 \| 0.4911 \| 0.2959 \| 0.4786 \| 0.2532 \| 0.4292 \| 0.323 \| 0.4693 \| \| 0.4359 \| 78.0 \| 8307 \| 1.0456 \| 0.3488 \| 0.6434 \| 0.3181 \| 0.2308 \| 0.2708 \| 0.549 \| 0.3117 \| 0.4823 \| 0.5029 \| 0.3098 \| 0.4397 \| 0.6933 \| 0.5704 \| 0.6716 \| 0.3174 \| 0.4823 \| 0.2983 \| 0.4777 \| 0.2353 \| 0.4185 \| 0.3227 \| 0.4644 \| \| 0.445 \| 78.9953 \| 8413 \| 1.0598 \| 0.3472 \| 0.638 \| 0.3149 \| 0.2236 \| 0.2719 \| 0.5492 \| 0.313 \| 0.4873 \| 0.5051 \| 0.2966 \| 0.4461 \| 0.7045 \| 0.5497 \| 0.6649 \| 0.3331 \| 0.5013 \| 0.2907 \| 0.4737 \| 0.2397 \| 0.4231 \| 0.3226 \| 0.4627 \| \| 0.4328 \| 80.0 \| 8520 \| 1.0558 \| 0.3477 \| 0.6419 \| 0.3326 \| 0.2335 \| 0.2701 \| 0.5483 \| 0.3112 \| 0.4865 \| 0.506 \| 0.3232 \| 0.432 \| 0.7048 \| 0.5599 \| 0.668 \| 0.3244 \| 0.4823 \| 0.292 \| 0.4772 \| 0.2396 \| 0.4369 \| 0.3228 \| 0.4658 \| \| 0.4321 \| 80.9953 \| 8626 \| 1.0447 \| 0.3474 \| 0.6387 \| 0.3308 \| 0.2348 \| 0.2749 \| 0.5457 \| 0.3155 \| 0.485 \| 0.506 \| 0.3189 \| 0.4442 \| 0.6897 \| 0.5604 \| 0.6689 \| 0.3165 \| 0.4785 \| 0.2932 \| 0.4777 \| 0.2362 \| 0.4338 \| 0.3307 \| 0.4711 \| \| 0.4318 \| 82.0 \| 8733 \| 1.0476 \| 0.3496 \| 0.6408 \| 0.3292 \| 0.2301 \| 0.2767 \| 0.543 \| 0.3118 \| 0.4895 \| 0.51 \| 0.3248 \| 0.4397 \| 0.6942 \| 0.5553 \| 0.6734 \| 0.3188 \| 0.4797 \| 0.2965 \| 0.479 \| 0.2481 \| 0.4462 \| 0.3296 \| 0.4716 \| \| 0.4273 \| 82.9953 \| 8839 \| 1.0585 \| 0.348 \| 0.6396 \| 0.3286 \| 0.2309 \| 0.2694 \| 0.5526 \| 0.3126 \| 0.4843 \| 0.5035 \| 0.318 \| 0.4372 \| 0.6912 \| 0.5532 \| 0.6622 \| 0.3262 \| 0.4861 \| 0.2999 \| 0.4763 \| 0.2363 \| 0.4246 \| 0.3245 \| 0.4684 \| \| 0.4252 \| 84.0 \| 8946 \| 1.0618 \| 0.3439 \| 0.6332 \| 0.3285 \| 0.2312 \| 0.272 \| 0.5316 \| 0.3148 \| 0.4854 \| 0.5036 \| 0.3173 \| 0.4412 \| 0.6932 \| 0.5538 \| 0.6626 \| 0.3153 \| 0.4848 \| 0.2879 \| 0.4723 \| 0.2455 \| 0.4431 \| 0.3168 \| 0.4551 \| \| 0.4158 \| 84.9953 \| 9052 \| 1.0631 \| 0.3484 \| 0.6387 \| 0.3257 \| 0.2285 \| 0.2694 \| 0.5474 \| 0.3152 \| 0.4841 \| 0.5047 \| 0.3141 \| 0.4346 \| 0.6906 \| 0.5595 \| 0.6671 \| 0.3351 \| 0.4823 \| 0.287 \| 0.4732 \| 0.2374 \| 0.4354 \| 0.323 \| 0.4653 \| \| 0.4088 \| 86.0 \| 9159 \| 1.0561 \| 0.3525 \| 0.6421 \| 0.3247 \| 0.2286 \| 0.2768 \| 0.553 \| 0.3143 \| 0.4856 \| 0.5067 \| 0.311 \| 0.4405 \| 0.7017 \| 0.5626 \| 0.6694 \| 0.3299 \| 0.4861 \| 0.2942 \| 0.4719 \| 0.249 \| 0.4446 \| 0.3266 \| 0.4618 \| \| 0.4194 \| 86.9953 \| 9265 \| 1.0613 \| 0.3499 \| 0.6399 \| 0.3269 \| 0.227 \| 0.2727 \| 0.5463 \| 0.3132 \| 0.4821 \| 0.5012 \| 0.3048 \| 0.4328 \| 0.6896 \| 0.562 \| 0.664 \| 0.3359 \| 0.4911 \| 0.2915 \| 0.4638 \| 0.2386 \| 0.4246 \| 0.3216 \| 0.4627 \| \| 0.4087 \| 88.0 \| 9372 \| 1.0764 \| 0.3484 \| 0.6358 \| 0.3282 \| 0.222 \| 0.2707 \| 0.556 \| 0.3145 \| 0.4806 \| 0.4996 \| 0.2991 \| 0.4302 \| 0.6907 \| 0.5541 \| 0.6599 \| 0.3344 \| 0.4848 \| 0.2866 \| 0.4701 \| 0.2455 \| 0.4185 \| 0.3215 \| 0.4649 \| \| 0.4031 \| 88.9953 \| 9478 \| 1.0731 \| 0.3494 \| 0.6316 \| 0.3283 \| 0.2258 \| 0.2685 \| 0.5512 \| 0.3144 \| 0.4852 \| 0.5048 \| 0.3054 \| 0.4275 \| 0.7027 \| 0.5574 \| 0.6617 \| 0.3338 \| 0.4911 \| 0.2962 \| 0.4679 \| 0.2356 \| 0.4354 \| 0.3238 \| 0.468 \| \| 0.3968 \| 90.0 \| 9585 \| 1.0613 \| 0.35 \| 0.6386 \| 0.3379 \| 0.2254 \| 0.2739 \| 0.5538 \| 0.3105 \| 0.4855 \| 0.5043 \| 0.3148 \| 0.4376 \| 0.6953 \| 0.559 \| 0.6721 \| 0.335 \| 0.4835 \| 0.292 \| 0.4679 \| 0.2386 \| 0.4323 \| 0.3253 \| 0.4658 \| \| 0.398 \| 90.9953 \| 9691 \| 1.0688 \| 0.345 \| 0.6347 \| 0.3302 \| 0.2158 \| 0.2666 \| 0.5543 \| 0.3107 \| 0.4786 \| 0.4958 \| 0.2918 \| 0.4253 \| 0.6988 \| 0.5566 \| 0.6622 \| 0.3228 \| 0.4772 \| 0.2946 \| 0.4643 \| 0.2317 \| 0.4154 \| 0.319 \| 0.46 \| \| 0.3964 \| 92.0 \| 9798 \| 1.0728 \| 0.3467 \| 0.6344 \| 0.3302 \| 0.2195 \| 0.265 \| 0.5541 \| 0.313 \| 0.4814 \| 0.5 \| 0.2978 \| 0.4227 \| 0.7024 \| 0.5625 \| 0.6703 \| 0.3321 \| 0.4873 \| 0.2908 \| 0.458 \| 0.2296 \| 0.4246 \| 0.3187 \| 0.4596 \| \| 0.3947 \| 92.9953 \| 9904 \| 1.0710 \| 0.3458 \| 0.6355 \| 0.3264 \| 0.2211 \| 0.267 \| 0.5534 \| 0.3141 \| 0.4837 \| 0.5022 \| 0.3055 \| 0.4276 \| 0.7005 \| 0.5555 \| 0.6667 \| 0.3352 \| 0.4899 \| 0.2911 \| 0.4616 \| 0.2274 \| 0.4292 \| 0.3198 \| 0.4636 \| \| 0.3903 \| 94.0 \| 10011 \| 1.0691 \| 0.3465 \| 0.6375 \| 0.3308 \| 0.227 \| 0.2659 \| 0.5562 \| 0.3099 \| 0.4822 \| 0.502 \| 0.3012 \| 0.431 \| 0.7001 \| 0.5563 \| 0.6626 \| 0.3354 \| 0.4911 \| 0.2883 \| 0.4643 \| 0.229 \| 0.4231 \| 0.3234 \| 0.4689 \| \| 0.3943 \| 94.9953 \| 10117 \| 1.0708 \| 0.3489 \| 0.6366 \| 0.3274 \| 0.2278 \| 0.2647 \| 0.5588 \| 0.3154 \| 0.4838 \| 0.502 \| 0.3023 \| 0.4258 \| 0.704 \| 0.5611 \| 0.6653 \| 0.3382 \| 0.4873 \| 0.2922 \| 0.4643 \| 0.2312 \| 0.4292 \| 0.3216 \| 0.464 \| \| 0.3855 \| 96.0 \| 10224 \| 1.0744 \| 0.349 \| 0.6353 \| 0.3315 \| 0.2215 \| 0.2689 \| 0.5566 \| 0.318 \| 0.4813 \| 0.5011 \| 0.2983 \| 0.431 \| 0.6985 \| 0.5574 \| 0.664 \| 0.3432 \| 0.4848 \| 0.2908 \| 0.4621 \| 0.231 \| 0.4292 \| 0.3224 \| 0.4653 \| \| 0.3833 \| 96.9953 \| 10330 \| 1.0692 \| 0.3474 \| 0.6341 \| 0.3282 \| 0.2244 \| 0.2693 \| 0.5567 \| 0.3167 \| 0.4833 \| 0.5038 \| 0.3031 \| 0.4318 \| 0.7037 \| 0.5553 \| 0.6649 \| 0.3348 \| 0.4861 \| 0.2918 \| 0.4652 \| 0.2323 \| 0.4385 \| 0.3227 \| 0.4644 \| \| 0.3838 \| 98.0 \| 10437 \| 1.0700 \| 0.3464 \| 0.6366 \| 0.3231 \| 0.2234 \| 0.2692 \| 0.554 \| 0.3155 \| 0.4842 \| 0.5026 \| 0.3011 \| 0.4319 \| 0.702 \| 0.5559 \| 0.6658 \| 0.3326 \| 0.4873 \| 0.2878 \| 0.4594 \| 0.232 \| 0.4323 \| 0.3239 \| 0.4684 \| \| 0.385 \| 98.9953 \| 10543 \| 1.0722 \| 0.3471 \| 0.6369 \| 0.3259 \| 0.2192 \| 0.2683 \| 0.5535 \| 0.3142 \| 0.4823 \| 0.5023 \| 0.2976 \| 0.4298 \| 0.7033 \| 0.5569 \| 0.6676 \| 0.3329 \| 0.4823 \| 0.2887 \| 0.4612 \| 0.2342 \| 0.4354 \| 0.3225 \| 0.4649 \| \| 0.3644 \| 99.5305 \| 10600 \| 1.0727 \| 0.3471 \| 0.6376 \| 0.3269 \| 0.2239 \| 0.2687 \| 0.5542 \| 0.3146 \| 0.485 \| 0.5045 \| 0.3033 \| 0.4315 \| 0.7048 \| 0.556 \| 0.6671 \| 0.3346 \| 0.4873 \| 0.2879 \| 0.4625 \| 0.2348 \| 0.44 \| 0.3221 \| 0.4653 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]
qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-more-augs	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/tuning-sota-cppe5/runs/2jpwvl0x) # sensetime-deformable-detr-finetuned-10k-cppe5-more-augs This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9911 - Map: 0.3714 - Map 50: 0.6742 - Map 75: 0.3545 - Map Small: 0.226 - Map Medium: 0.2836 - Map Large: 0.5849 - Mar 1: 0.3191 - Mar 10: 0.502 - Mar 100: 0.5266 - Mar Small: 0.3445 - Mar Medium: 0.4443 - Mar Large: 0.7237 - Map Coverall: 0.5834 - Mar 100 Coverall: 0.6797 - Map Face Shield: 0.3648 - Mar 100 Face Shield: 0.5241 - Map Gloves: 0.3122 - Mar 100 Gloves: 0.5071 - Map Goggles: 0.2315 - Mar 100 Goggles: 0.4338 - Map Mask: 0.3649 - Mar 100 Mask: 0.4884 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 8.5293 \| 0.9953 \| 106 \| 1.7296 \| 0.0265 \| 0.0666 \| 0.0164 \| 0.0103 \| 0.0138 \| 0.0516 \| 0.0559 \| 0.1869 \| 0.2518 \| 0.0592 \| 0.2249 \| 0.3316 \| 0.0774 \| 0.514 \| 0.0017 \| 0.1671 \| 0.0136 \| 0.2487 \| 0.0006 \| 0.0815 \| 0.0392 \| 0.2476 \| \| 1.5394 \| 2.0 \| 213 \| 1.5095 \| 0.0359 \| 0.0836 \| 0.0262 \| 0.0293 \| 0.0264 \| 0.0769 \| 0.0626 \| 0.2334 \| 0.2933 \| 0.1525 \| 0.2717 \| 0.3217 \| 0.0785 \| 0.555 \| 0.0028 \| 0.1886 \| 0.0201 \| 0.2732 \| 0.0011 \| 0.0908 \| 0.0769 \| 0.3591 \| \| 1.3761 \| 2.9953 \| 319 \| 1.5410 \| 0.0604 \| 0.123 \| 0.0555 \| 0.0256 \| 0.036 \| 0.0604 \| 0.0966 \| 0.2526 \| 0.2998 \| 0.0803 \| 0.258 \| 0.4789 \| 0.1865 \| 0.5874 \| 0.0016 \| 0.1177 \| 0.0185 \| 0.2812 \| 0.0026 \| 0.1569 \| 0.0928 \| 0.3556 \| \| 1.2615 \| 4.0 \| 426 \| 1.3671 \| 0.0888 \| 0.1756 \| 0.0873 \| 0.043 \| 0.058 \| 0.1298 \| 0.1317 \| 0.3164 \| 0.3635 \| 0.1763 \| 0.3076 \| 0.5184 \| 0.2402 \| 0.6176 \| 0.0094 \| 0.2759 \| 0.0577 \| 0.3406 \| 0.0097 \| 0.2031 \| 0.1273 \| 0.3804 \| \| 1.2042 \| 4.9953 \| 532 \| 1.2824 \| 0.1069 \| 0.2127 \| 0.101 \| 0.0374 \| 0.075 \| 0.1509 \| 0.1626 \| 0.3623 \| 0.4001 \| 0.1967 \| 0.3445 \| 0.5716 \| 0.2957 \| 0.6477 \| 0.0247 \| 0.3342 \| 0.0617 \| 0.3442 \| 0.0194 \| 0.2477 \| 0.1331 \| 0.4267 \| \| 1.1568 \| 6.0 \| 639 \| 1.2616 \| 0.124 \| 0.2451 \| 0.117 \| 0.0346 \| 0.098 \| 0.2056 \| 0.1652 \| 0.3623 \| 0.3997 \| 0.2053 \| 0.3386 \| 0.5458 \| 0.3583 \| 0.6477 \| 0.0184 \| 0.2823 \| 0.0803 \| 0.3799 \| 0.0102 \| 0.2369 \| 0.1528 \| 0.4516 \| \| 1.1207 \| 6.9953 \| 745 \| 1.2273 \| 0.1506 \| 0.291 \| 0.1409 \| 0.0686 \| 0.1205 \| 0.224 \| 0.1843 \| 0.3925 \| 0.4294 \| 0.252 \| 0.385 \| 0.5833 \| 0.4045 \| 0.6541 \| 0.0588 \| 0.3506 \| 0.086 \| 0.404 \| 0.014 \| 0.2738 \| 0.1897 \| 0.4644 \| \| 1.0994 \| 8.0 \| 852 \| 1.1945 \| 0.1725 \| 0.3216 \| 0.1674 \| 0.076 \| 0.1565 \| 0.242 \| 0.2047 \| 0.4149 \| 0.4486 \| 0.2657 \| 0.3955 \| 0.6151 \| 0.4191 \| 0.6739 \| 0.0764 \| 0.3848 \| 0.0904 \| 0.3902 \| 0.0539 \| 0.3338 \| 0.2226 \| 0.4604 \| \| 1.0347 \| 8.9953 \| 958 \| 1.1692 \| 0.1923 \| 0.3626 \| 0.1728 \| 0.0719 \| 0.1544 \| 0.2953 \| 0.2277 \| 0.4381 \| 0.4654 \| 0.2678 \| 0.3933 \| 0.6539 \| 0.4415 \| 0.6595 \| 0.0846 \| 0.4278 \| 0.1251 \| 0.4179 \| 0.0711 \| 0.3492 \| 0.2391 \| 0.4724 \| \| 1.0089 \| 10.0 \| 1065 \| 1.1513 \| 0.2033 \| 0.3793 \| 0.1906 \| 0.0771 \| 0.1521 \| 0.317 \| 0.2301 \| 0.4234 \| 0.4558 \| 0.2662 \| 0.3725 \| 0.6609 \| 0.451 \| 0.6441 \| 0.1041 \| 0.3987 \| 0.1202 \| 0.4237 \| 0.0738 \| 0.3231 \| 0.2676 \| 0.4893 \| \| 1.0118 \| 10.9953 \| 1171 \| 1.1533 \| 0.2141 \| 0.408 \| 0.1966 \| 0.0794 \| 0.1793 \| 0.3118 \| 0.2235 \| 0.4187 \| 0.458 \| 0.2509 \| 0.3958 \| 0.6438 \| 0.4976 \| 0.6775 \| 0.0865 \| 0.4494 \| 0.1302 \| 0.4045 \| 0.0835 \| 0.2969 \| 0.2726 \| 0.4618 \| \| 0.995 \| 12.0 \| 1278 \| 1.1530 \| 0.2198 \| 0.4281 \| 0.2038 \| 0.1102 \| 0.168 \| 0.3425 \| 0.2464 \| 0.4322 \| 0.4683 \| 0.2781 \| 0.3951 \| 0.6383 \| 0.4638 \| 0.6595 \| 0.1443 \| 0.4544 \| 0.1525 \| 0.4473 \| 0.0843 \| 0.3123 \| 0.2539 \| 0.468 \| \| 0.9732 \| 12.9953 \| 1384 \| 1.1279 \| 0.232 \| 0.4426 \| 0.2192 \| 0.0938 \| 0.1726 \| 0.3843 \| 0.25 \| 0.448 \| 0.4811 \| 0.2912 \| 0.4102 \| 0.6636 \| 0.4878 \| 0.655 \| 0.1329 \| 0.4785 \| 0.1618 \| 0.4272 \| 0.09 \| 0.3538 \| 0.2876 \| 0.4911 \| \| 0.9398 \| 14.0 \| 1491 \| 1.1457 \| 0.2351 \| 0.4554 \| 0.2215 \| 0.1128 \| 0.174 \| 0.4144 \| 0.2565 \| 0.4372 \| 0.4627 \| 0.2462 \| 0.3896 \| 0.688 \| 0.5015 \| 0.6554 \| 0.1352 \| 0.5203 \| 0.1645 \| 0.4165 \| 0.1219 \| 0.3031 \| 0.2522 \| 0.4182 \| \| 0.9281 \| 14.9953 \| 1597 \| 1.1128 \| 0.2545 \| 0.4835 \| 0.2451 \| 0.1241 \| 0.2078 \| 0.3884 \| 0.2638 \| 0.4494 \| 0.4776 \| 0.2831 \| 0.4274 \| 0.6403 \| 0.4969 \| 0.6563 \| 0.1496 \| 0.4734 \| 0.195 \| 0.4353 \| 0.1352 \| 0.3554 \| 0.2958 \| 0.4676 \| \| 0.9162 \| 16.0 \| 1704 \| 1.1145 \| 0.2482 \| 0.4755 \| 0.2398 \| 0.1053 \| 0.2017 \| 0.4362 \| 0.2749 \| 0.4637 \| 0.4867 \| 0.2986 \| 0.4219 \| 0.6875 \| 0.4836 \| 0.6743 \| 0.1557 \| 0.4848 \| 0.1714 \| 0.433 \| 0.1453 \| 0.3554 \| 0.2851 \| 0.4858 \| \| 0.9038 \| 16.9953 \| 1810 \| 1.0968 \| 0.2746 \| 0.5143 \| 0.2687 \| 0.167 \| 0.2122 \| 0.4583 \| 0.2825 \| 0.4576 \| 0.4827 \| 0.2696 \| 0.4182 \| 0.6881 \| 0.5159 \| 0.6509 \| 0.1481 \| 0.457 \| 0.2179 \| 0.4509 \| 0.1544 \| 0.3631 \| 0.3367 \| 0.4916 \| \| 0.8973 \| 18.0 \| 1917 \| 1.0895 \| 0.2688 \| 0.5085 \| 0.2595 \| 0.1549 \| 0.2178 \| 0.4381 \| 0.2743 \| 0.4561 \| 0.481 \| 0.2921 \| 0.4282 \| 0.6551 \| 0.5211 \| 0.6617 \| 0.1517 \| 0.4481 \| 0.1978 \| 0.4384 \| 0.1679 \| 0.3892 \| 0.3053 \| 0.4676 \| \| 0.892 \| 18.9953 \| 2023 \| 1.0987 \| 0.2736 \| 0.5209 \| 0.2565 \| 0.1568 \| 0.2152 \| 0.4343 \| 0.2802 \| 0.4541 \| 0.482 \| 0.2782 \| 0.4233 \| 0.6792 \| 0.5088 \| 0.6518 \| 0.187 \| 0.5025 \| 0.1949 \| 0.4187 \| 0.1581 \| 0.3692 \| 0.3194 \| 0.4676 \| \| 0.8851 \| 20.0 \| 2130 \| 1.0649 \| 0.2813 \| 0.5321 \| 0.2756 \| 0.1914 \| 0.2223 \| 0.4563 \| 0.2901 \| 0.4698 \| 0.4932 \| 0.3123 \| 0.4281 \| 0.6792 \| 0.5127 \| 0.6532 \| 0.17 \| 0.4924 \| 0.223 \| 0.4576 \| 0.1749 \| 0.3846 \| 0.3261 \| 0.4782 \| \| 0.8862 \| 20.9953 \| 2236 \| 1.0438 \| 0.2999 \| 0.5575 \| 0.2865 \| 0.1862 \| 0.2439 \| 0.4748 \| 0.2862 \| 0.4739 \| 0.4955 \| 0.2754 \| 0.4518 \| 0.6711 \| 0.558 \| 0.6874 \| 0.1831 \| 0.5 \| 0.2399 \| 0.4504 \| 0.1933 \| 0.3723 \| 0.3251 \| 0.4671 \| \| 0.8636 \| 22.0 \| 2343 \| 1.0833 \| 0.2853 \| 0.5355 \| 0.2675 \| 0.192 \| 0.2404 \| 0.4267 \| 0.271 \| 0.4606 \| 0.4886 \| 0.3272 \| 0.4255 \| 0.637 \| 0.5164 \| 0.6748 \| 0.204 \| 0.4823 \| 0.2425 \| 0.4688 \| 0.1493 \| 0.3631 \| 0.3145 \| 0.4542 \| \| 0.8638 \| 22.9953 \| 2449 \| 1.0502 \| 0.296 \| 0.5487 \| 0.2823 \| 0.1887 \| 0.2344 \| 0.475 \| 0.2926 \| 0.4802 \| 0.5039 \| 0.3273 \| 0.4325 \| 0.6911 \| 0.5345 \| 0.6752 \| 0.2049 \| 0.4949 \| 0.2274 \| 0.4589 \| 0.1935 \| 0.4108 \| 0.32 \| 0.4796 \| \| 0.8337 \| 24.0 \| 2556 \| 1.0479 \| 0.2998 \| 0.5571 \| 0.2814 \| 0.152 \| 0.2356 \| 0.4876 \| 0.2856 \| 0.4771 \| 0.4998 \| 0.2975 \| 0.4335 \| 0.704 \| 0.5409 \| 0.6707 \| 0.2135 \| 0.4633 \| 0.2491 \| 0.4518 \| 0.1759 \| 0.4246 \| 0.3197 \| 0.4884 \| \| 0.8504 \| 24.9953 \| 2662 \| 1.0265 \| 0.3073 \| 0.5537 \| 0.3079 \| 0.1999 \| 0.2561 \| 0.4489 \| 0.2932 \| 0.4857 \| 0.5159 \| 0.3003 \| 0.4643 \| 0.6932 \| 0.5294 \| 0.6991 \| 0.2215 \| 0.5076 \| 0.2618 \| 0.4674 \| 0.1766 \| 0.4123 \| 0.3472 \| 0.4929 \| \| 0.8299 \| 26.0 \| 2769 \| 1.0412 \| 0.308 \| 0.5736 \| 0.299 \| 0.2122 \| 0.2355 \| 0.4804 \| 0.2938 \| 0.4854 \| 0.5069 \| 0.3271 \| 0.4385 \| 0.6925 \| 0.5397 \| 0.6712 \| 0.2425 \| 0.5139 \| 0.2551 \| 0.4598 \| 0.1792 \| 0.4046 \| 0.3235 \| 0.4849 \| \| 0.8284 \| 26.9953 \| 2875 \| 1.0276 \| 0.3105 \| 0.5678 \| 0.2962 \| 0.2051 \| 0.2464 \| 0.469 \| 0.2956 \| 0.4851 \| 0.513 \| 0.3045 \| 0.4736 \| 0.6767 \| 0.5523 \| 0.6833 \| 0.2282 \| 0.5139 \| 0.2525 \| 0.4679 \| 0.185 \| 0.3969 \| 0.3346 \| 0.5031 \| \| 0.8092 \| 28.0 \| 2982 \| 1.0400 \| 0.309 \| 0.573 \| 0.2986 \| 0.1643 \| 0.2486 \| 0.479 \| 0.2934 \| 0.4887 \| 0.5072 \| 0.2957 \| 0.4481 \| 0.6842 \| 0.5404 \| 0.6743 \| 0.2025 \| 0.5063 \| 0.2585 \| 0.4554 \| 0.1976 \| 0.4092 \| 0.346 \| 0.4907 \| \| 0.8156 \| 28.9953 \| 3088 \| 1.0271 \| 0.3208 \| 0.5894 \| 0.305 \| 0.2031 \| 0.2619 \| 0.4853 \| 0.3051 \| 0.5006 \| 0.5193 \| 0.3246 \| 0.4605 \| 0.7046 \| 0.5503 \| 0.6833 \| 0.2421 \| 0.5076 \| 0.2555 \| 0.467 \| 0.2047 \| 0.4323 \| 0.3513 \| 0.5062 \| \| 0.8037 \| 30.0 \| 3195 \| 1.0355 \| 0.3162 \| 0.5986 \| 0.295 \| 0.1994 \| 0.2532 \| 0.4821 \| 0.2986 \| 0.4877 \| 0.5097 \| 0.286 \| 0.4508 \| 0.6977 \| 0.5315 \| 0.6595 \| 0.2647 \| 0.4924 \| 0.2635 \| 0.4647 \| 0.188 \| 0.4508 \| 0.3335 \| 0.4809 \| \| 0.797 \| 30.9953 \| 3301 \| 1.0333 \| 0.3091 \| 0.5947 \| 0.2852 \| 0.1864 \| 0.2525 \| 0.4721 \| 0.2971 \| 0.4774 \| 0.5051 \| 0.2963 \| 0.4411 \| 0.6837 \| 0.5442 \| 0.6788 \| 0.2436 \| 0.4975 \| 0.2568 \| 0.4714 \| 0.1677 \| 0.4092 \| 0.3331 \| 0.4684 \| \| 0.7778 \| 32.0 \| 3408 \| 1.0285 \| 0.3325 \| 0.6019 \| 0.3123 \| 0.2079 \| 0.262 \| 0.5106 \| 0.304 \| 0.4879 \| 0.5138 \| 0.3375 \| 0.4535 \| 0.7007 \| 0.5546 \| 0.6784 \| 0.3003 \| 0.5342 \| 0.2837 \| 0.4719 \| 0.2064 \| 0.4092 \| 0.3176 \| 0.4756 \| \| 0.7839 \| 32.9953 \| 3514 \| 1.0155 \| 0.3302 \| 0.6038 \| 0.3114 \| 0.2003 \| 0.2756 \| 0.4914 \| 0.3 \| 0.4923 \| 0.5099 \| 0.3212 \| 0.4624 \| 0.6844 \| 0.5733 \| 0.6955 \| 0.2739 \| 0.4987 \| 0.2816 \| 0.4808 \| 0.1803 \| 0.3969 \| 0.342 \| 0.4778 \| \| 0.7687 \| 34.0 \| 3621 \| 1.0158 \| 0.3284 \| 0.6116 \| 0.2986 \| 0.2103 \| 0.2695 \| 0.4791 \| 0.2998 \| 0.4992 \| 0.5258 \| 0.3411 \| 0.4725 \| 0.7092 \| 0.5692 \| 0.6959 \| 0.2751 \| 0.5304 \| 0.2654 \| 0.4746 \| 0.1916 \| 0.4462 \| 0.3409 \| 0.4818 \| \| 0.7798 \| 34.9953 \| 3727 \| 1.0094 \| 0.3286 \| 0.5951 \| 0.3134 \| 0.1983 \| 0.2685 \| 0.5138 \| 0.301 \| 0.4942 \| 0.5227 \| 0.3001 \| 0.4738 \| 0.7313 \| 0.5752 \| 0.7009 \| 0.2566 \| 0.5367 \| 0.2753 \| 0.4902 \| 0.203 \| 0.4108 \| 0.3327 \| 0.4751 \| \| 0.7476 \| 36.0 \| 3834 \| 1.0584 \| 0.3212 \| 0.5923 \| 0.291 \| 0.1856 \| 0.2576 \| 0.5242 \| 0.3008 \| 0.4828 \| 0.5067 \| 0.3268 \| 0.4238 \| 0.7241 \| 0.5335 \| 0.6509 \| 0.2728 \| 0.4987 \| 0.2713 \| 0.479 \| 0.1889 \| 0.4292 \| 0.3393 \| 0.4756 \| \| 0.758 \| 36.9953 \| 3940 \| 1.0163 \| 0.3381 \| 0.6177 \| 0.3258 \| 0.2113 \| 0.2655 \| 0.5359 \| 0.3041 \| 0.4926 \| 0.5221 \| 0.3397 \| 0.4575 \| 0.7092 \| 0.5643 \| 0.6802 \| 0.2738 \| 0.5139 \| 0.2752 \| 0.496 \| 0.2291 \| 0.4277 \| 0.3483 \| 0.4929 \| \| 0.7328 \| 38.0 \| 4047 \| 1.0104 \| 0.3349 \| 0.6295 \| 0.3152 \| 0.2034 \| 0.2794 \| 0.5199 \| 0.2966 \| 0.5007 \| 0.5226 \| 0.314 \| 0.4783 \| 0.6913 \| 0.5632 \| 0.6856 \| 0.2802 \| 0.5228 \| 0.2773 \| 0.4942 \| 0.2194 \| 0.4262 \| 0.3344 \| 0.4844 \| \| 0.7374 \| 38.9953 \| 4153 \| 1.0134 \| 0.3422 \| 0.6331 \| 0.3235 \| 0.204 \| 0.2763 \| 0.5299 \| 0.3076 \| 0.4982 \| 0.5229 \| 0.3264 \| 0.4727 \| 0.7249 \| 0.5615 \| 0.6757 \| 0.3006 \| 0.5215 \| 0.2717 \| 0.4844 \| 0.2348 \| 0.4446 \| 0.3427 \| 0.4884 \| \| 0.7173 \| 40.0 \| 4260 \| 1.0198 \| 0.334 \| 0.6183 \| 0.3207 \| 0.1992 \| 0.2761 \| 0.5124 \| 0.305 \| 0.4893 \| 0.5111 \| 0.3233 \| 0.4566 \| 0.697 \| 0.5651 \| 0.6802 \| 0.2821 \| 0.5253 \| 0.261 \| 0.4621 \| 0.2182 \| 0.4046 \| 0.3437 \| 0.4831 \| \| 0.7148 \| 40.9953 \| 4366 \| 0.9978 \| 0.3482 \| 0.6318 \| 0.342 \| 0.2046 \| 0.2859 \| 0.5363 \| 0.3085 \| 0.5022 \| 0.5245 \| 0.3357 \| 0.4768 \| 0.7011 \| 0.574 \| 0.6955 \| 0.3025 \| 0.5304 \| 0.284 \| 0.4938 \| 0.229 \| 0.42 \| 0.3517 \| 0.4831 \| \| 0.7127 \| 42.0 \| 4473 \| 1.0042 \| 0.3485 \| 0.6345 \| 0.3259 \| 0.2234 \| 0.2791 \| 0.5416 \| 0.3111 \| 0.4972 \| 0.5216 \| 0.3453 \| 0.4666 \| 0.7047 \| 0.5772 \| 0.6856 \| 0.3145 \| 0.538 \| 0.2813 \| 0.479 \| 0.2297 \| 0.4369 \| 0.3399 \| 0.4684 \| \| 0.7189 \| 42.9953 \| 4579 \| 0.9994 \| 0.3444 \| 0.6235 \| 0.3368 \| 0.2151 \| 0.2735 \| 0.5286 \| 0.3104 \| 0.5028 \| 0.5274 \| 0.3398 \| 0.4767 \| 0.6915 \| 0.5846 \| 0.6923 \| 0.3048 \| 0.5241 \| 0.2814 \| 0.4871 \| 0.2047 \| 0.44 \| 0.3467 \| 0.4933 \| \| 0.7085 \| 44.0 \| 4686 \| 1.0234 \| 0.3415 \| 0.6232 \| 0.3203 \| 0.2001 \| 0.271 \| 0.5241 \| 0.3101 \| 0.4907 \| 0.5137 \| 0.3153 \| 0.4478 \| 0.6936 \| 0.5731 \| 0.6811 \| 0.3072 \| 0.5152 \| 0.2762 \| 0.4799 \| 0.2148 \| 0.4185 \| 0.3363 \| 0.4738 \| \| 0.6929 \| 44.9953 \| 4792 \| 1.0076 \| 0.3564 \| 0.6437 \| 0.3355 \| 0.2144 \| 0.2869 \| 0.538 \| 0.3097 \| 0.4962 \| 0.5197 \| 0.3187 \| 0.4774 \| 0.7046 \| 0.5891 \| 0.7 \| 0.3213 \| 0.5139 \| 0.2797 \| 0.4746 \| 0.2404 \| 0.4246 \| 0.3516 \| 0.4853 \| \| 0.6949 \| 46.0 \| 4899 \| 1.0051 \| 0.3548 \| 0.6513 \| 0.3319 \| 0.2273 \| 0.2876 \| 0.5374 \| 0.3102 \| 0.5104 \| 0.5319 \| 0.3603 \| 0.4717 \| 0.7153 \| 0.5864 \| 0.6995 \| 0.3127 \| 0.5241 \| 0.2817 \| 0.4982 \| 0.2386 \| 0.4385 \| 0.3546 \| 0.4991 \| \| 0.6895 \| 46.9953 \| 5005 \| 1.0220 \| 0.3454 \| 0.6389 \| 0.3235 \| 0.2023 \| 0.2764 \| 0.5265 \| 0.3074 \| 0.49 \| 0.5133 \| 0.3227 \| 0.4464 \| 0.7115 \| 0.5772 \| 0.6887 \| 0.3159 \| 0.5127 \| 0.2738 \| 0.4839 \| 0.2054 \| 0.3938 \| 0.3547 \| 0.4876 \| \| 0.6709 \| 48.0 \| 5112 \| 1.0272 \| 0.3473 \| 0.6374 \| 0.3205 \| 0.2262 \| 0.2689 \| 0.527 \| 0.3029 \| 0.4893 \| 0.5139 \| 0.3524 \| 0.431 \| 0.7094 \| 0.5839 \| 0.6793 \| 0.3089 \| 0.4949 \| 0.2779 \| 0.4879 \| 0.2197 \| 0.4338 \| 0.3461 \| 0.4733 \| \| 0.7002 \| 48.9953 \| 5218 \| 1.0188 \| 0.349 \| 0.645 \| 0.3255 \| 0.2188 \| 0.2713 \| 0.5228 \| 0.3034 \| 0.4888 \| 0.5107 \| 0.3435 \| 0.4357 \| 0.6984 \| 0.5732 \| 0.6788 \| 0.316 \| 0.4873 \| 0.2867 \| 0.4938 \| 0.2202 \| 0.4185 \| 0.3488 \| 0.4751 \| \| 0.6732 \| 50.0 \| 5325 \| 1.0171 \| 0.35 \| 0.6378 \| 0.3213 \| 0.2193 \| 0.2835 \| 0.5176 \| 0.3053 \| 0.4928 \| 0.5157 \| 0.3323 \| 0.4532 \| 0.7 \| 0.5792 \| 0.682 \| 0.3271 \| 0.4937 \| 0.2805 \| 0.4897 \| 0.2054 \| 0.4231 \| 0.3579 \| 0.4902 \| \| 0.6866 \| 50.9953 \| 5431 \| 1.0090 \| 0.3538 \| 0.6428 \| 0.3434 \| 0.2264 \| 0.2822 \| 0.5369 \| 0.3131 \| 0.5026 \| 0.5245 \| 0.3388 \| 0.4515 \| 0.7113 \| 0.5798 \| 0.6883 \| 0.3469 \| 0.5203 \| 0.2896 \| 0.5009 \| 0.2072 \| 0.4462 \| 0.3458 \| 0.4667 \| \| 0.6538 \| 52.0 \| 5538 \| 1.0059 \| 0.3516 \| 0.6406 \| 0.3322 \| 0.2332 \| 0.2843 \| 0.5078 \| 0.314 \| 0.5022 \| 0.5281 \| 0.3696 \| 0.4706 \| 0.6918 \| 0.5761 \| 0.6793 \| 0.3438 \| 0.5203 \| 0.2854 \| 0.5058 \| 0.2064 \| 0.4554 \| 0.3465 \| 0.4796 \| \| 0.6531 \| 52.9953 \| 5644 \| 1.0035 \| 0.3628 \| 0.6559 \| 0.3368 \| 0.2265 \| 0.293 \| 0.5513 \| 0.3135 \| 0.501 \| 0.5225 \| 0.3514 \| 0.4637 \| 0.715 \| 0.5788 \| 0.6901 \| 0.3587 \| 0.5228 \| 0.2939 \| 0.4906 \| 0.2326 \| 0.4323 \| 0.3498 \| 0.4764 \| \| 0.6406 \| 54.0 \| 5751 \| 0.9991 \| 0.3588 \| 0.6417 \| 0.3544 \| 0.2267 \| 0.2905 \| 0.5306 \| 0.3222 \| 0.5071 \| 0.5287 \| 0.351 \| 0.4625 \| 0.7189 \| 0.5678 \| 0.6784 \| 0.3426 \| 0.5038 \| 0.2858 \| 0.4933 \| 0.2254 \| 0.4662 \| 0.3725 \| 0.5018 \| \| 0.657 \| 54.9953 \| 5857 \| 1.0076 \| 0.3542 \| 0.6512 \| 0.339 \| 0.2209 \| 0.2857 \| 0.538 \| 0.3069 \| 0.5015 \| 0.5231 \| 0.3524 \| 0.4679 \| 0.7062 \| 0.5718 \| 0.6784 \| 0.3414 \| 0.5177 \| 0.2952 \| 0.4996 \| 0.2124 \| 0.4385 \| 0.3501 \| 0.4813 \| \| 0.6402 \| 56.0 \| 5964 \| 0.9918 \| 0.3605 \| 0.652 \| 0.3421 \| 0.2311 \| 0.2913 \| 0.5183 \| 0.3186 \| 0.5075 \| 0.5283 \| 0.3531 \| 0.4621 \| 0.7194 \| 0.5814 \| 0.6919 \| 0.339 \| 0.5203 \| 0.2968 \| 0.4996 \| 0.2286 \| 0.4369 \| 0.357 \| 0.4929 \| \| 0.6484 \| 56.9953 \| 6070 \| 0.9921 \| 0.3573 \| 0.649 \| 0.3516 \| 0.2157 \| 0.2891 \| 0.5269 \| 0.3079 \| 0.4988 \| 0.5203 \| 0.3319 \| 0.4464 \| 0.7003 \| 0.5739 \| 0.686 \| 0.3577 \| 0.5228 \| 0.2922 \| 0.4964 \| 0.2123 \| 0.4169 \| 0.3501 \| 0.4791 \| \| 0.6532 \| 58.0 \| 6177 \| 1.0018 \| 0.358 \| 0.6383 \| 0.3572 \| 0.2156 \| 0.2825 \| 0.5278 \| 0.3075 \| 0.4984 \| 0.5223 \| 0.331 \| 0.4471 \| 0.6955 \| 0.5757 \| 0.6838 \| 0.3443 \| 0.5127 \| 0.2947 \| 0.5009 \| 0.2097 \| 0.4277 \| 0.3656 \| 0.4867 \| \| 0.6334 \| 58.9953 \| 6283 \| 1.0088 \| 0.3543 \| 0.6515 \| 0.3324 \| 0.2214 \| 0.2868 \| 0.5213 \| 0.3055 \| 0.4962 \| 0.5197 \| 0.3428 \| 0.4442 \| 0.7049 \| 0.571 \| 0.6802 \| 0.3352 \| 0.5076 \| 0.2883 \| 0.4862 \| 0.2197 \| 0.4431 \| 0.3572 \| 0.4813 \| \| 0.6236 \| 60.0 \| 6390 \| 0.9933 \| 0.3612 \| 0.6485 \| 0.3449 \| 0.2121 \| 0.2941 \| 0.523 \| 0.3161 \| 0.5046 \| 0.5262 \| 0.3305 \| 0.4712 \| 0.7022 \| 0.5812 \| 0.6905 \| 0.345 \| 0.519 \| 0.2932 \| 0.4978 \| 0.2336 \| 0.4477 \| 0.3528 \| 0.476 \| \| 0.6294 \| 60.9953 \| 6496 \| 0.9987 \| 0.3579 \| 0.652 \| 0.3353 \| 0.2127 \| 0.2929 \| 0.5445 \| 0.3108 \| 0.4964 \| 0.5231 \| 0.3383 \| 0.4649 \| 0.7097 \| 0.5713 \| 0.6815 \| 0.3413 \| 0.5127 \| 0.3004 \| 0.4955 \| 0.2301 \| 0.4538 \| 0.3466 \| 0.472 \| \| 0.6214 \| 62.0 \| 6603 \| 1.0151 \| 0.3581 \| 0.6551 \| 0.3316 \| 0.2173 \| 0.2886 \| 0.549 \| 0.3136 \| 0.4965 \| 0.513 \| 0.3241 \| 0.4447 \| 0.7 \| 0.5721 \| 0.6707 \| 0.3341 \| 0.4911 \| 0.2976 \| 0.4938 \| 0.2294 \| 0.4246 \| 0.3571 \| 0.4849 \| \| 0.6336 \| 62.9953 \| 6709 \| 1.0027 \| 0.3592 \| 0.658 \| 0.3345 \| 0.2247 \| 0.2869 \| 0.5429 \| 0.3145 \| 0.4986 \| 0.5246 \| 0.334 \| 0.4603 \| 0.7119 \| 0.5739 \| 0.682 \| 0.3459 \| 0.5342 \| 0.2995 \| 0.4996 \| 0.2242 \| 0.4262 \| 0.3524 \| 0.4813 \| \| 0.621 \| 64.0 \| 6816 \| 1.0044 \| 0.3609 \| 0.6589 \| 0.3461 \| 0.2236 \| 0.2818 \| 0.5455 \| 0.3162 \| 0.4982 \| 0.5221 \| 0.3418 \| 0.462 \| 0.7027 \| 0.5796 \| 0.6865 \| 0.3514 \| 0.5228 \| 0.2969 \| 0.5013 \| 0.2149 \| 0.4108 \| 0.3615 \| 0.4889 \| \| 0.6101 \| 64.9953 \| 6922 \| 1.0033 \| 0.3676 \| 0.668 \| 0.3447 \| 0.2296 \| 0.2977 \| 0.5585 \| 0.3226 \| 0.4976 \| 0.5239 \| 0.3524 \| 0.4703 \| 0.7025 \| 0.5679 \| 0.6842 \| 0.3594 \| 0.5152 \| 0.3062 \| 0.4942 \| 0.2428 \| 0.4338 \| 0.3615 \| 0.492 \| \| 0.6076 \| 66.0 \| 7029 \| 0.9941 \| 0.3689 \| 0.6645 \| 0.3522 \| 0.2319 \| 0.2985 \| 0.5601 \| 0.3186 \| 0.5003 \| 0.5252 \| 0.3508 \| 0.4711 \| 0.6945 \| 0.5753 \| 0.6905 \| 0.3515 \| 0.5101 \| 0.3076 \| 0.5058 \| 0.2471 \| 0.4338 \| 0.3631 \| 0.4858 \| \| 0.6004 \| 66.9953 \| 7135 \| 0.9888 \| 0.3638 \| 0.6631 \| 0.3417 \| 0.2283 \| 0.3053 \| 0.5454 \| 0.31 \| 0.499 \| 0.5247 \| 0.3405 \| 0.4752 \| 0.6956 \| 0.5704 \| 0.6847 \| 0.3435 \| 0.5089 \| 0.3084 \| 0.5045 \| 0.2381 \| 0.4462 \| 0.3585 \| 0.4791 \| \| 0.5985 \| 68.0 \| 7242 \| 0.9908 \| 0.3642 \| 0.6615 \| 0.34 \| 0.227 \| 0.2876 \| 0.541 \| 0.3139 \| 0.4954 \| 0.5252 \| 0.3562 \| 0.4679 \| 0.694 \| 0.5786 \| 0.6919 \| 0.3348 \| 0.4987 \| 0.3017 \| 0.4924 \| 0.232 \| 0.4431 \| 0.3737 \| 0.5 \| \| 0.5962 \| 68.9953 \| 7348 \| 0.9841 \| 0.3689 \| 0.6699 \| 0.3442 \| 0.2293 \| 0.2957 \| 0.5557 \| 0.3212 \| 0.5088 \| 0.5314 \| 0.3522 \| 0.4687 \| 0.7093 \| 0.5826 \| 0.6865 \| 0.363 \| 0.5215 \| 0.3027 \| 0.5018 \| 0.2322 \| 0.4585 \| 0.364 \| 0.4889 \| \| 0.5967 \| 70.0 \| 7455 \| 1.0001 \| 0.3636 \| 0.6702 \| 0.3307 \| 0.2242 \| 0.29 \| 0.5608 \| 0.3134 \| 0.4967 \| 0.5249 \| 0.3454 \| 0.4636 \| 0.7088 \| 0.5712 \| 0.686 \| 0.3459 \| 0.5177 \| 0.3085 \| 0.5089 \| 0.2384 \| 0.4354 \| 0.3539 \| 0.4764 \| \| 0.5867 \| 70.9953 \| 7561 \| 0.9964 \| 0.3622 \| 0.6648 \| 0.3244 \| 0.2245 \| 0.2915 \| 0.5393 \| 0.3143 \| 0.4964 \| 0.5191 \| 0.3377 \| 0.4607 \| 0.6897 \| 0.5824 \| 0.6865 \| 0.3328 \| 0.5101 \| 0.3052 \| 0.5004 \| 0.2342 \| 0.4308 \| 0.3566 \| 0.4676 \| \| 0.5868 \| 72.0 \| 7668 \| 0.9980 \| 0.3643 \| 0.665 \| 0.3393 \| 0.2257 \| 0.2947 \| 0.5463 \| 0.3163 \| 0.5009 \| 0.5215 \| 0.3281 \| 0.4579 \| 0.6978 \| 0.586 \| 0.6869 \| 0.3453 \| 0.5089 \| 0.3085 \| 0.5013 \| 0.2219 \| 0.4246 \| 0.3597 \| 0.4858 \| \| 0.5774 \| 72.9953 \| 7774 \| 0.9955 \| 0.3707 \| 0.6702 \| 0.3441 \| 0.2287 \| 0.303 \| 0.551 \| 0.3221 \| 0.5013 \| 0.5222 \| 0.3255 \| 0.4583 \| 0.7021 \| 0.5911 \| 0.691 \| 0.3537 \| 0.5089 \| 0.3071 \| 0.4982 \| 0.2425 \| 0.4354 \| 0.3593 \| 0.4773 \| \| 0.5671 \| 74.0 \| 7881 \| 0.9984 \| 0.3679 \| 0.6699 \| 0.3348 \| 0.221 \| 0.3006 \| 0.5606 \| 0.3158 \| 0.497 \| 0.5228 \| 0.3193 \| 0.4645 \| 0.7144 \| 0.585 \| 0.6892 \| 0.3592 \| 0.5316 \| 0.2977 \| 0.4884 \| 0.2421 \| 0.4246 \| 0.3556 \| 0.48 \| \| 0.5757 \| 74.9953 \| 7987 \| 0.9951 \| 0.3698 \| 0.6791 \| 0.3427 \| 0.2276 \| 0.2908 \| 0.5622 \| 0.3161 \| 0.5019 \| 0.5273 \| 0.3439 \| 0.4567 \| 0.7122 \| 0.5872 \| 0.6892 \| 0.3566 \| 0.5291 \| 0.304 \| 0.5027 \| 0.2395 \| 0.4262 \| 0.3615 \| 0.4893 \| \| 0.5622 \| 76.0 \| 8094 \| 1.0045 \| 0.366 \| 0.6724 \| 0.3297 \| 0.2095 \| 0.2988 \| 0.5485 \| 0.3126 \| 0.4983 \| 0.5187 \| 0.3127 \| 0.4549 \| 0.6987 \| 0.5883 \| 0.6896 \| 0.3453 \| 0.5152 \| 0.3063 \| 0.4951 \| 0.2414 \| 0.4231 \| 0.3489 \| 0.4707 \| \| 0.5692 \| 76.9953 \| 8200 \| 0.9920 \| 0.372 \| 0.6785 \| 0.3435 \| 0.229 \| 0.2999 \| 0.5517 \| 0.3169 \| 0.5042 \| 0.5272 \| 0.3422 \| 0.4511 \| 0.7139 \| 0.5897 \| 0.6892 \| 0.3452 \| 0.5089 \| 0.3025 \| 0.5018 \| 0.2578 \| 0.4431 \| 0.3646 \| 0.4929 \| \| 0.5633 \| 78.0 \| 8307 \| 0.9977 \| 0.3663 \| 0.6788 \| 0.3341 \| 0.2171 \| 0.2959 \| 0.5507 \| 0.3143 \| 0.4984 \| 0.5189 \| 0.3155 \| 0.4583 \| 0.6929 \| 0.5866 \| 0.691 \| 0.3494 \| 0.5038 \| 0.3 \| 0.4893 \| 0.2388 \| 0.4369 \| 0.3569 \| 0.4733 \| \| 0.5671 \| 78.9953 \| 8413 \| 0.9957 \| 0.3649 \| 0.6697 \| 0.3343 \| 0.2222 \| 0.2848 \| 0.5576 \| 0.3146 \| 0.5011 \| 0.5227 \| 0.3147 \| 0.4604 \| 0.7049 \| 0.5839 \| 0.6901 \| 0.3487 \| 0.5114 \| 0.3043 \| 0.496 \| 0.234 \| 0.4431 \| 0.3538 \| 0.4729 \| \| 0.5496 \| 80.0 \| 8520 \| 0.9874 \| 0.3671 \| 0.667 \| 0.3476 \| 0.2313 \| 0.2869 \| 0.5656 \| 0.3153 \| 0.503 \| 0.5282 \| 0.3407 \| 0.4584 \| 0.7089 \| 0.5876 \| 0.6964 \| 0.3491 \| 0.5089 \| 0.3027 \| 0.504 \| 0.2307 \| 0.4338 \| 0.3655 \| 0.4978 \| \| 0.5628 \| 80.9953 \| 8626 \| 0.9996 \| 0.3664 \| 0.6683 \| 0.3343 \| 0.2148 \| 0.288 \| 0.5608 \| 0.3162 \| 0.4997 \| 0.5215 \| 0.3092 \| 0.4619 \| 0.6996 \| 0.5885 \| 0.6896 \| 0.3541 \| 0.5203 \| 0.3081 \| 0.4951 \| 0.2333 \| 0.4277 \| 0.3478 \| 0.4747 \| \| 0.5609 \| 82.0 \| 8733 \| 0.9844 \| 0.3712 \| 0.6712 \| 0.3547 \| 0.2264 \| 0.2906 \| 0.5841 \| 0.3206 \| 0.5043 \| 0.5334 \| 0.361 \| 0.4736 \| 0.7136 \| 0.5874 \| 0.6982 \| 0.3723 \| 0.5443 \| 0.3037 \| 0.5031 \| 0.2299 \| 0.4338 \| 0.3626 \| 0.4876 \| \| 0.5581 \| 82.9953 \| 8839 \| 0.9873 \| 0.3699 \| 0.6706 \| 0.3568 \| 0.2302 \| 0.2896 \| 0.5803 \| 0.3224 \| 0.5115 \| 0.5333 \| 0.3533 \| 0.4764 \| 0.7146 \| 0.5853 \| 0.6905 \| 0.3735 \| 0.5481 \| 0.3054 \| 0.5036 \| 0.2339 \| 0.44 \| 0.3517 \| 0.4844 \| \| 0.5539 \| 84.0 \| 8946 \| 0.9930 \| 0.3686 \| 0.6638 \| 0.354 \| 0.2285 \| 0.2868 \| 0.565 \| 0.3166 \| 0.5006 \| 0.5228 \| 0.3556 \| 0.4537 \| 0.6896 \| 0.5846 \| 0.6784 \| 0.3534 \| 0.5127 \| 0.3075 \| 0.4929 \| 0.2357 \| 0.4323 \| 0.362 \| 0.4978 \| \| 0.5481 \| 84.9953 \| 9052 \| 0.9930 \| 0.3714 \| 0.6746 \| 0.3588 \| 0.221 \| 0.2916 \| 0.5803 \| 0.3177 \| 0.4979 \| 0.5222 \| 0.3152 \| 0.4599 \| 0.711 \| 0.5915 \| 0.6883 \| 0.355 \| 0.5038 \| 0.3077 \| 0.5013 \| 0.2504 \| 0.4338 \| 0.3525 \| 0.4836 \| \| 0.5405 \| 86.0 \| 9159 \| 0.9839 \| 0.3808 \| 0.6833 \| 0.3759 \| 0.236 \| 0.2986 \| 0.595 \| 0.3208 \| 0.5112 \| 0.5343 \| 0.3523 \| 0.4722 \| 0.7192 \| 0.5949 \| 0.6937 \| 0.3826 \| 0.538 \| 0.3108 \| 0.504 \| 0.2475 \| 0.4385 \| 0.3685 \| 0.4973 \| \| 0.5532 \| 86.9953 \| 9265 \| 0.9859 \| 0.3782 \| 0.677 \| 0.3672 \| 0.2331 \| 0.3023 \| 0.5736 \| 0.322 \| 0.5076 \| 0.5317 \| 0.348 \| 0.471 \| 0.7091 \| 0.5907 \| 0.6865 \| 0.3714 \| 0.5228 \| 0.315 \| 0.5112 \| 0.2551 \| 0.4492 \| 0.3588 \| 0.4889 \| \| 0.5478 \| 88.0 \| 9372 \| 0.9918 \| 0.3702 \| 0.6746 \| 0.3544 \| 0.2255 \| 0.2911 \| 0.5666 \| 0.3203 \| 0.5101 \| 0.5326 \| 0.3492 \| 0.4616 \| 0.7194 \| 0.589 \| 0.6923 \| 0.3545 \| 0.5354 \| 0.3092 \| 0.5018 \| 0.2419 \| 0.4369 \| 0.3566 \| 0.4964 \| \| 0.5532 \| 88.9953 \| 9478 \| 0.9928 \| 0.3715 \| 0.6745 \| 0.3518 \| 0.2266 \| 0.2887 \| 0.5828 \| 0.3232 \| 0.5087 \| 0.5288 \| 0.3494 \| 0.4602 \| 0.7206 \| 0.5857 \| 0.6874 \| 0.365 \| 0.5228 \| 0.3092 \| 0.5031 \| 0.2387 \| 0.4369 \| 0.359 \| 0.4938 \| \| 0.5285 \| 90.0 \| 9585 \| 0.9974 \| 0.3706 \| 0.6768 \| 0.3474 \| 0.2167 \| 0.2854 \| 0.5773 \| 0.3197 \| 0.5014 \| 0.5226 \| 0.333 \| 0.4542 \| 0.7111 \| 0.5885 \| 0.6869 \| 0.362 \| 0.5152 \| 0.3083 \| 0.4924 \| 0.2354 \| 0.4369 \| 0.3588 \| 0.4818 \| \| 0.5262 \| 90.9953 \| 9691 \| 0.9878 \| 0.3712 \| 0.6715 \| 0.3515 \| 0.2274 \| 0.2869 \| 0.5817 \| 0.319 \| 0.5012 \| 0.522 \| 0.3362 \| 0.4556 \| 0.7103 \| 0.5862 \| 0.6833 \| 0.364 \| 0.5165 \| 0.3136 \| 0.5004 \| 0.235 \| 0.4277 \| 0.3573 \| 0.4822 \| \| 0.5282 \| 92.0 \| 9798 \| 0.9987 \| 0.3678 \| 0.6722 \| 0.3388 \| 0.2241 \| 0.2803 \| 0.5825 \| 0.3168 \| 0.4989 \| 0.5214 \| 0.3418 \| 0.4403 \| 0.7094 \| 0.5843 \| 0.6793 \| 0.3555 \| 0.5089 \| 0.3106 \| 0.5063 \| 0.2378 \| 0.4323 \| 0.351 \| 0.4804 \| \| 0.5294 \| 92.9953 \| 9904 \| 0.9893 \| 0.3692 \| 0.6677 \| 0.3468 \| 0.2254 \| 0.2829 \| 0.5824 \| 0.3175 \| 0.5027 \| 0.5261 \| 0.3528 \| 0.4565 \| 0.7133 \| 0.5836 \| 0.6833 \| 0.3524 \| 0.5076 \| 0.3107 \| 0.5067 \| 0.238 \| 0.4415 \| 0.3611 \| 0.4911 \| \| 0.5122 \| 94.0 \| 10011 \| 0.9880 \| 0.3716 \| 0.6687 \| 0.3577 \| 0.2239 \| 0.2891 \| 0.5818 \| 0.3172 \| 0.5027 \| 0.5285 \| 0.3487 \| 0.4577 \| 0.7144 \| 0.5848 \| 0.6869 \| 0.3595 \| 0.5127 \| 0.3123 \| 0.5094 \| 0.2382 \| 0.44 \| 0.3634 \| 0.4933 \| \| 0.5358 \| 94.9953 \| 10117 \| 0.9913 \| 0.3717 \| 0.6662 \| 0.3481 \| 0.2242 \| 0.2874 \| 0.5813 \| 0.3208 \| 0.5052 \| 0.5301 \| 0.3474 \| 0.4647 \| 0.7192 \| 0.587 \| 0.6905 \| 0.3641 \| 0.5177 \| 0.3139 \| 0.508 \| 0.2337 \| 0.4415 \| 0.3599 \| 0.4929 \| \| 0.5233 \| 96.0 \| 10224 \| 0.9908 \| 0.3704 \| 0.6692 \| 0.3508 \| 0.2253 \| 0.2858 \| 0.5802 \| 0.318 \| 0.5023 \| 0.5274 \| 0.3491 \| 0.4604 \| 0.7148 \| 0.5802 \| 0.682 \| 0.3639 \| 0.5215 \| 0.3112 \| 0.5063 \| 0.2356 \| 0.4415 \| 0.3612 \| 0.4858 \| \| 0.5136 \| 96.9953 \| 10330 \| 0.9903 \| 0.3724 \| 0.6732 \| 0.3511 \| 0.2265 \| 0.2847 \| 0.5814 \| 0.3191 \| 0.5027 \| 0.5268 \| 0.3445 \| 0.4537 \| 0.7195 \| 0.5812 \| 0.6824 \| 0.3691 \| 0.5304 \| 0.3142 \| 0.5067 \| 0.2354 \| 0.4308 \| 0.3619 \| 0.4836 \| \| 0.5204 \| 98.0 \| 10437 \| 0.9903 \| 0.3722 \| 0.674 \| 0.352 \| 0.2277 \| 0.2851 \| 0.5843 \| 0.3197 \| 0.5036 \| 0.5272 \| 0.3448 \| 0.4543 \| 0.7207 \| 0.5841 \| 0.6824 \| 0.3658 \| 0.5228 \| 0.3146 \| 0.508 \| 0.2303 \| 0.4308 \| 0.3661 \| 0.492 \| \| 0.5237 \| 98.9953 \| 10543 \| 0.9917 \| 0.3721 \| 0.6715 \| 0.3545 \| 0.2264 \| 0.2841 \| 0.5841 \| 0.3197 \| 0.5021 \| 0.527 \| 0.3454 \| 0.4453 \| 0.7226 \| 0.5826 \| 0.6797 \| 0.3684 \| 0.5241 \| 0.3111 \| 0.5063 \| 0.2332 \| 0.4369 \| 0.3648 \| 0.488 \| \| 0.4776 \| 99.5305 \| 10600 \| 0.9911 \| 0.3714 \| 0.6742 \| 0.3545 \| 0.226 \| 0.2836 \| 0.5849 \| 0.3191 \| 0.502 \| 0.5266 \| 0.3445 \| 0.4443 \| 0.7237 \| 0.5834 \| 0.6797 \| 0.3648 \| 0.5241 \| 0.3122 \| 0.5071 \| 0.2315 \| 0.4338 \| 0.3649 \| 0.4884 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u73br2l9) # facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.2371 - Map: 0.2873 - Map 50: 0.5678 - Map 75: 0.2501 - Map Small: 0.126 - Map Medium: 0.2327 - Map Large: 0.4873 - Mar 1: 0.2843 - Mar 10: 0.4643 - Mar 100: 0.4762 - Mar Small: 0.2338 - Mar Medium: 0.4167 - Mar Large: 0.7114 - Map Coverall: 0.5461 - Mar 100 Coverall: 0.6932 - Map Face Shield: 0.2167 - Mar 100 Face Shield: 0.4785 - Map Gloves: 0.2135 - Mar 100 Gloves: 0.4094 - Map Goggles: 0.173 - Mar 100 Goggles: 0.4092 - Map Mask: 0.2871 - Mar 100 Mask: 0.3907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 2.6324 \| 1.0 \| 107 \| 2.3696 \| 0.0355 \| 0.0772 \| 0.0305 \| 0.0094 \| 0.0044 \| 0.0388 \| 0.0606 \| 0.1313 \| 0.1643 \| 0.0314 \| 0.0921 \| 0.1907 \| 0.1559 \| 0.536 \| 0.0 \| 0.0 \| 0.0064 \| 0.1433 \| 0.0 \| 0.0 \| 0.0151 \| 0.1422 \| \| 2.0851 \| 2.0 \| 214 \| 2.1468 \| 0.0518 \| 0.1164 \| 0.0389 \| 0.0106 \| 0.0151 \| 0.0683 \| 0.0809 \| 0.1628 \| 0.1881 \| 0.023 \| 0.1069 \| 0.2515 \| 0.2068 \| 0.5842 \| 0.0 \| 0.0 \| 0.0174 \| 0.167 \| 0.0 \| 0.0 \| 0.035 \| 0.1893 \| \| 1.9849 \| 3.0 \| 321 \| 2.0694 \| 0.0592 \| 0.1436 \| 0.0377 \| 0.0187 \| 0.023 \| 0.0609 \| 0.0796 \| 0.1666 \| 0.191 \| 0.0527 \| 0.1032 \| 0.2411 \| 0.2119 \| 0.5486 \| 0.0 \| 0.0 \| 0.0226 \| 0.1799 \| 0.0 \| 0.0 \| 0.0615 \| 0.2262 \| \| 1.8364 \| 4.0 \| 428 \| 2.0431 \| 0.0715 \| 0.1666 \| 0.0625 \| 0.0031 \| 0.0232 \| 0.09 \| 0.0775 \| 0.1641 \| 0.183 \| 0.0083 \| 0.1006 \| 0.2708 \| 0.2923 \| 0.5604 \| 0.0012 \| 0.0177 \| 0.0203 \| 0.1612 \| 0.0 \| 0.0 \| 0.0437 \| 0.1756 \| \| 1.7433 \| 5.0 \| 535 \| 1.9001 \| 0.0938 \| 0.2115 \| 0.0758 \| 0.0229 \| 0.0473 \| 0.1031 \| 0.1066 \| 0.216 \| 0.2311 \| 0.0451 \| 0.1423 \| 0.3186 \| 0.3168 \| 0.5847 \| 0.0064 \| 0.0937 \| 0.05 \| 0.2277 \| 0.0 \| 0.0 \| 0.0959 \| 0.2493 \| \| 1.7113 \| 6.0 \| 642 \| 1.8483 \| 0.1007 \| 0.2225 \| 0.0773 \| 0.0203 \| 0.0593 \| 0.1341 \| 0.1109 \| 0.2213 \| 0.2463 \| 0.0646 \| 0.1711 \| 0.3303 \| 0.3462 \| 0.5914 \| 0.0132 \| 0.0987 \| 0.0451 \| 0.2571 \| 0.0002 \| 0.0062 \| 0.0987 \| 0.2782 \| \| 1.6518 \| 7.0 \| 749 \| 1.8376 \| 0.1055 \| 0.2347 \| 0.0807 \| 0.0199 \| 0.0596 \| 0.1405 \| 0.1127 \| 0.2364 \| 0.2598 \| 0.0778 \| 0.1949 \| 0.3276 \| 0.3693 \| 0.5869 \| 0.0168 \| 0.1367 \| 0.0417 \| 0.2397 \| 0.0046 \| 0.0523 \| 0.0953 \| 0.2831 \| \| 1.6048 \| 8.0 \| 856 \| 1.8041 \| 0.1098 \| 0.2576 \| 0.0857 \| 0.0155 \| 0.0772 \| 0.1578 \| 0.1284 \| 0.2423 \| 0.2708 \| 0.0602 \| 0.2057 \| 0.3631 \| 0.3512 \| 0.6149 \| 0.033 \| 0.1342 \| 0.0402 \| 0.2491 \| 0.004 \| 0.0862 \| 0.1204 \| 0.2698 \| \| 1.6026 \| 9.0 \| 963 \| 1.7173 \| 0.1243 \| 0.2773 \| 0.1041 \| 0.0415 \| 0.0688 \| 0.1767 \| 0.1426 \| 0.2852 \| 0.3101 \| 0.0857 \| 0.2275 \| 0.4249 \| 0.3889 \| 0.6378 \| 0.0347 \| 0.2253 \| 0.0547 \| 0.271 \| 0.0159 \| 0.1308 \| 0.1274 \| 0.2858 \| \| 1.5298 \| 10.0 \| 1070 \| 1.7915 \| 0.1154 \| 0.2763 \| 0.0804 \| 0.018 \| 0.0789 \| 0.1762 \| 0.1447 \| 0.2629 \| 0.2913 \| 0.0774 \| 0.2191 \| 0.3935 \| 0.3615 \| 0.618 \| 0.0415 \| 0.2127 \| 0.0351 \| 0.2603 \| 0.0076 \| 0.0846 \| 0.1315 \| 0.2809 \| \| 1.5114 \| 11.0 \| 1177 \| 1.7964 \| 0.1232 \| 0.2755 \| 0.0915 \| 0.0396 \| 0.0849 \| 0.1728 \| 0.144 \| 0.2876 \| 0.3007 \| 0.0868 \| 0.2161 \| 0.4535 \| 0.3588 \| 0.5923 \| 0.035 \| 0.2291 \| 0.057 \| 0.2241 \| 0.0195 \| 0.1877 \| 0.1455 \| 0.2702 \| \| 1.5283 \| 12.0 \| 1284 \| 1.7017 \| 0.1444 \| 0.3075 \| 0.1223 \| 0.0215 \| 0.0985 \| 0.2396 \| 0.1803 \| 0.3244 \| 0.3456 \| 0.0531 \| 0.282 \| 0.5554 \| 0.4122 \| 0.632 \| 0.0666 \| 0.3215 \| 0.0618 \| 0.2625 \| 0.0086 \| 0.2046 \| 0.1727 \| 0.3076 \| \| 1.5034 \| 13.0 \| 1391 \| 1.6344 \| 0.1538 \| 0.3418 \| 0.1226 \| 0.0423 \| 0.1165 \| 0.2381 \| 0.1871 \| 0.337 \| 0.3578 \| 0.1195 \| 0.2901 \| 0.5214 \| 0.4204 \| 0.6284 \| 0.0705 \| 0.319 \| 0.0634 \| 0.2884 \| 0.0191 \| 0.2277 \| 0.1959 \| 0.3253 \| \| 1.445 \| 14.0 \| 1498 \| 1.5685 \| 0.167 \| 0.3626 \| 0.15 \| 0.0561 \| 0.1266 \| 0.2715 \| 0.1998 \| 0.357 \| 0.3701 \| 0.1068 \| 0.2922 \| 0.5693 \| 0.4227 \| 0.6342 \| 0.0873 \| 0.3797 \| 0.0744 \| 0.2951 \| 0.0441 \| 0.2108 \| 0.2065 \| 0.3307 \| \| 1.3998 \| 15.0 \| 1605 \| 1.5679 \| 0.1645 \| 0.3515 \| 0.1383 \| 0.0269 \| 0.117 \| 0.2592 \| 0.1992 \| 0.3295 \| 0.3539 \| 0.0645 \| 0.2751 \| 0.5533 \| 0.4535 \| 0.6703 \| 0.0887 \| 0.3494 \| 0.0866 \| 0.3116 \| 0.0276 \| 0.1554 \| 0.1659 \| 0.2827 \| \| 1.398 \| 16.0 \| 1712 \| 1.6191 \| 0.1578 \| 0.3459 \| 0.1205 \| 0.0623 \| 0.0967 \| 0.258 \| 0.1729 \| 0.3255 \| 0.3386 \| 0.1106 \| 0.2686 \| 0.5066 \| 0.4363 \| 0.6 \| 0.0971 \| 0.3089 \| 0.0703 \| 0.2839 \| 0.0258 \| 0.2 \| 0.1593 \| 0.3004 \| \| 1.369 \| 17.0 \| 1819 \| 1.6099 \| 0.1654 \| 0.3517 \| 0.1371 \| 0.0341 \| 0.099 \| 0.2543 \| 0.1852 \| 0.3085 \| 0.3232 \| 0.0587 \| 0.2331 \| 0.5126 \| 0.4663 \| 0.6329 \| 0.0784 \| 0.2785 \| 0.0761 \| 0.2906 \| 0.019 \| 0.12 \| 0.187 \| 0.2942 \| \| 1.3264 \| 18.0 \| 1926 \| 1.5610 \| 0.1784 \| 0.3752 \| 0.1594 \| 0.036 \| 0.1249 \| 0.3082 \| 0.2061 \| 0.3388 \| 0.3561 \| 0.0959 \| 0.2702 \| 0.5669 \| 0.5 \| 0.6707 \| 0.0768 \| 0.2937 \| 0.0899 \| 0.2933 \| 0.0291 \| 0.2108 \| 0.1962 \| 0.312 \| \| 1.3513 \| 19.0 \| 2033 \| 1.4462 \| 0.1864 \| 0.3933 \| 0.154 \| 0.0559 \| 0.1364 \| 0.2738 \| 0.2135 \| 0.3767 \| 0.3931 \| 0.1216 \| 0.3178 \| 0.5744 \| 0.4834 \| 0.677 \| 0.0976 \| 0.3595 \| 0.1022 \| 0.3438 \| 0.0248 \| 0.2554 \| 0.224 \| 0.3298 \| \| 1.3034 \| 20.0 \| 2140 \| 1.4527 \| 0.1965 \| 0.412 \| 0.1713 \| 0.051 \| 0.1463 \| 0.2967 \| 0.2087 \| 0.3643 \| 0.3805 \| 0.1422 \| 0.2931 \| 0.5833 \| 0.4982 \| 0.6495 \| 0.1144 \| 0.362 \| 0.0997 \| 0.3263 \| 0.0497 \| 0.2277 \| 0.2204 \| 0.3369 \| \| 1.2823 \| 21.0 \| 2247 \| 1.4872 \| 0.1835 \| 0.3972 \| 0.1556 \| 0.0596 \| 0.1308 \| 0.282 \| 0.2148 \| 0.3924 \| 0.4147 \| 0.1236 \| 0.3657 \| 0.5903 \| 0.4622 \| 0.6455 \| 0.0727 \| 0.3873 \| 0.1195 \| 0.3558 \| 0.0544 \| 0.3415 \| 0.2084 \| 0.3431 \| \| 1.3099 \| 22.0 \| 2354 \| 1.4723 \| 0.1903 \| 0.407 \| 0.1632 \| 0.0594 \| 0.1343 \| 0.2937 \| 0.2158 \| 0.3817 \| 0.396 \| 0.1357 \| 0.3222 \| 0.5737 \| 0.4719 \| 0.6486 \| 0.1089 \| 0.3722 \| 0.1153 \| 0.3161 \| 0.0404 \| 0.3092 \| 0.2149 \| 0.3338 \| \| 1.2691 \| 23.0 \| 2461 \| 1.4648 \| 0.1897 \| 0.403 \| 0.1626 \| 0.0598 \| 0.1476 \| 0.2933 \| 0.2129 \| 0.3676 \| 0.3851 \| 0.1154 \| 0.3247 \| 0.592 \| 0.4836 \| 0.6658 \| 0.0711 \| 0.3025 \| 0.1217 \| 0.3679 \| 0.0594 \| 0.2615 \| 0.2127 \| 0.328 \| \| 1.3018 \| 24.0 \| 2568 \| 1.5593 \| 0.174 \| 0.3964 \| 0.1358 \| 0.0568 \| 0.1344 \| 0.2776 \| 0.2076 \| 0.3571 \| 0.3754 \| 0.1412 \| 0.2946 \| 0.5732 \| 0.4208 \| 0.6347 \| 0.0782 \| 0.2987 \| 0.1125 \| 0.3304 \| 0.0444 \| 0.2831 \| 0.2141 \| 0.3302 \| \| 1.2744 \| 25.0 \| 2675 \| 1.4785 \| 0.1863 \| 0.3983 \| 0.1556 \| 0.0706 \| 0.1314 \| 0.3032 \| 0.221 \| 0.3784 \| 0.3988 \| 0.1453 \| 0.3331 \| 0.5824 \| 0.4831 \| 0.6716 \| 0.0934 \| 0.3392 \| 0.1155 \| 0.3647 \| 0.0612 \| 0.3277 \| 0.1783 \| 0.2907 \| \| 1.3089 \| 26.0 \| 2782 \| 1.4503 \| 0.1969 \| 0.4129 \| 0.166 \| 0.0904 \| 0.1342 \| 0.3274 \| 0.2303 \| 0.3907 \| 0.4057 \| 0.154 \| 0.3252 \| 0.6182 \| 0.4773 \| 0.6653 \| 0.109 \| 0.4114 \| 0.1176 \| 0.3504 \| 0.0758 \| 0.2877 \| 0.2046 \| 0.3138 \| \| 1.2391 \| 27.0 \| 2889 \| 1.4961 \| 0.1992 \| 0.4097 \| 0.1714 \| 0.067 \| 0.1354 \| 0.3227 \| 0.2308 \| 0.3803 \| 0.3971 \| 0.1333 \| 0.3179 \| 0.623 \| 0.4826 \| 0.6473 \| 0.114 \| 0.3329 \| 0.1169 \| 0.3621 \| 0.0538 \| 0.2862 \| 0.2286 \| 0.3569 \| \| 1.2156 \| 28.0 \| 2996 \| 1.4349 \| 0.1926 \| 0.4121 \| 0.1612 \| 0.0655 \| 0.1425 \| 0.2933 \| 0.2279 \| 0.4047 \| 0.425 \| 0.1521 \| 0.36 \| 0.6217 \| 0.4698 \| 0.6797 \| 0.0974 \| 0.3924 \| 0.1097 \| 0.3402 \| 0.061 \| 0.3677 \| 0.2253 \| 0.3449 \| \| 1.2131 \| 29.0 \| 3103 \| 1.4869 \| 0.2002 \| 0.4106 \| 0.1706 \| 0.0662 \| 0.1516 \| 0.3142 \| 0.2342 \| 0.3959 \| 0.4175 \| 0.1389 \| 0.3525 \| 0.6269 \| 0.4846 \| 0.6514 \| 0.0845 \| 0.3899 \| 0.1196 \| 0.3701 \| 0.0699 \| 0.3277 \| 0.2425 \| 0.3484 \| \| 1.2368 \| 30.0 \| 3210 \| 1.3779 \| 0.218 \| 0.4403 \| 0.185 \| 0.084 \| 0.169 \| 0.3567 \| 0.241 \| 0.4115 \| 0.4326 \| 0.148 \| 0.3738 \| 0.6216 \| 0.5063 \| 0.6815 \| 0.122 \| 0.4342 \| 0.1497 \| 0.3969 \| 0.066 \| 0.3031 \| 0.2462 \| 0.3471 \| \| 1.1998 \| 31.0 \| 3317 \| 1.4472 \| 0.198 \| 0.4299 \| 0.16 \| 0.0595 \| 0.1492 \| 0.3264 \| 0.2239 \| 0.3883 \| 0.3992 \| 0.127 \| 0.3237 \| 0.6434 \| 0.4806 \| 0.6455 \| 0.1023 \| 0.3722 \| 0.1246 \| 0.3509 \| 0.0601 \| 0.3154 \| 0.2224 \| 0.312 \| \| 1.1681 \| 32.0 \| 3424 \| 1.4495 \| 0.2221 \| 0.4518 \| 0.1953 \| 0.0777 \| 0.1696 \| 0.3691 \| 0.2318 \| 0.3938 \| 0.4082 \| 0.1272 \| 0.3478 \| 0.6191 \| 0.5124 \| 0.6757 \| 0.1293 \| 0.362 \| 0.148 \| 0.3531 \| 0.0893 \| 0.32 \| 0.2318 \| 0.3302 \| \| 1.1764 \| 33.0 \| 3531 \| 1.4152 \| 0.2008 \| 0.4169 \| 0.1755 \| 0.0844 \| 0.154 \| 0.3627 \| 0.2432 \| 0.4092 \| 0.4304 \| 0.182 \| 0.3751 \| 0.6587 \| 0.4828 \| 0.6802 \| 0.0883 \| 0.3886 \| 0.1488 \| 0.3884 \| 0.069 \| 0.3692 \| 0.2154 \| 0.3258 \| \| 1.1979 \| 34.0 \| 3638 \| 1.4428 \| 0.2013 \| 0.4058 \| 0.1813 \| 0.0525 \| 0.1428 \| 0.3315 \| 0.2318 \| 0.3933 \| 0.4123 \| 0.1116 \| 0.3492 \| 0.6431 \| 0.4904 \| 0.6486 \| 0.0817 \| 0.3785 \| 0.1434 \| 0.3576 \| 0.0536 \| 0.3277 \| 0.2373 \| 0.3493 \| \| 1.1635 \| 35.0 \| 3745 \| 1.3778 \| 0.2114 \| 0.4473 \| 0.1701 \| 0.0619 \| 0.1497 \| 0.372 \| 0.2475 \| 0.3926 \| 0.4125 \| 0.1264 \| 0.3367 \| 0.6545 \| 0.4973 \| 0.6577 \| 0.1031 \| 0.3595 \| 0.1518 \| 0.379 \| 0.0604 \| 0.3262 \| 0.2444 \| 0.34 \| \| 1.1449 \| 36.0 \| 3852 \| 1.3933 \| 0.2136 \| 0.4427 \| 0.1846 \| 0.0791 \| 0.1615 \| 0.3765 \| 0.2354 \| 0.4041 \| 0.4206 \| 0.1575 \| 0.3565 \| 0.6543 \| 0.4915 \| 0.6626 \| 0.1052 \| 0.4101 \| 0.1672 \| 0.3795 \| 0.0773 \| 0.3169 \| 0.227 \| 0.3338 \| \| 1.1453 \| 37.0 \| 3959 \| 1.3954 \| 0.205 \| 0.4224 \| 0.176 \| 0.0648 \| 0.1498 \| 0.3456 \| 0.2412 \| 0.3984 \| 0.4095 \| 0.1209 \| 0.3492 \| 0.6536 \| 0.492 \| 0.6423 \| 0.1063 \| 0.3633 \| 0.1628 \| 0.3893 \| 0.0527 \| 0.3292 \| 0.2114 \| 0.3236 \| \| 1.1424 \| 38.0 \| 4066 \| 1.3781 \| 0.2137 \| 0.4373 \| 0.1839 \| 0.0774 \| 0.1634 \| 0.3815 \| 0.2488 \| 0.4149 \| 0.4281 \| 0.1632 \| 0.3589 \| 0.6559 \| 0.4926 \| 0.6559 \| 0.101 \| 0.4 \| 0.1576 \| 0.3616 \| 0.059 \| 0.3415 \| 0.258 \| 0.3813 \| \| 1.1118 \| 39.0 \| 4173 \| 1.3672 \| 0.2154 \| 0.4547 \| 0.1737 \| 0.0746 \| 0.1538 \| 0.3736 \| 0.2448 \| 0.3939 \| 0.4125 \| 0.1372 \| 0.3446 \| 0.6372 \| 0.5024 \| 0.6779 \| 0.1303 \| 0.3608 \| 0.1493 \| 0.3719 \| 0.0607 \| 0.3123 \| 0.2341 \| 0.3396 \| \| 1.088 \| 40.0 \| 4280 \| 1.3414 \| 0.2193 \| 0.4552 \| 0.1867 \| 0.0824 \| 0.1711 \| 0.3886 \| 0.2451 \| 0.4043 \| 0.4178 \| 0.1678 \| 0.3501 \| 0.6465 \| 0.4978 \| 0.6658 \| 0.1297 \| 0.3557 \| 0.1644 \| 0.404 \| 0.0618 \| 0.3077 \| 0.2426 \| 0.356 \| \| 1.1114 \| 41.0 \| 4387 \| 1.3819 \| 0.2228 \| 0.4721 \| 0.175 \| 0.0879 \| 0.1609 \| 0.3706 \| 0.255 \| 0.4006 \| 0.4146 \| 0.1458 \| 0.3427 \| 0.6389 \| 0.4924 \| 0.6689 \| 0.1164 \| 0.3734 \| 0.169 \| 0.3679 \| 0.0961 \| 0.3138 \| 0.2402 \| 0.3489 \| \| 1.0758 \| 42.0 \| 4494 \| 1.3459 \| 0.2286 \| 0.4696 \| 0.2048 \| 0.0897 \| 0.1806 \| 0.3734 \| 0.2526 \| 0.4245 \| 0.4366 \| 0.1566 \| 0.3898 \| 0.6341 \| 0.5112 \| 0.6815 \| 0.1497 \| 0.4038 \| 0.1742 \| 0.3938 \| 0.059 \| 0.3338 \| 0.2491 \| 0.3702 \| \| 1.0747 \| 43.0 \| 4601 \| 1.4411 \| 0.2178 \| 0.4397 \| 0.1951 \| 0.0581 \| 0.1557 \| 0.3977 \| 0.232 \| 0.3933 \| 0.4086 \| 0.1258 \| 0.3301 \| 0.6699 \| 0.4929 \| 0.6604 \| 0.1317 \| 0.3633 \| 0.1632 \| 0.3598 \| 0.1014 \| 0.34 \| 0.1997 \| 0.3196 \| \| 1.0909 \| 44.0 \| 4708 \| 1.3192 \| 0.2424 \| 0.4916 \| 0.2152 \| 0.0842 \| 0.1953 \| 0.4032 \| 0.26 \| 0.4237 \| 0.4387 \| 0.1597 \| 0.3801 \| 0.66 \| 0.5041 \| 0.6842 \| 0.1765 \| 0.4038 \| 0.1901 \| 0.3879 \| 0.0983 \| 0.3585 \| 0.2431 \| 0.3591 \| \| 1.0527 \| 45.0 \| 4815 \| 1.3110 \| 0.2362 \| 0.4902 \| 0.2009 \| 0.1033 \| 0.1842 \| 0.4042 \| 0.2505 \| 0.4306 \| 0.4496 \| 0.1869 \| 0.3896 \| 0.6601 \| 0.5178 \| 0.6797 \| 0.1379 \| 0.4215 \| 0.1828 \| 0.4098 \| 0.0848 \| 0.3754 \| 0.2576 \| 0.3613 \| \| 1.0421 \| 46.0 \| 4922 \| 1.3186 \| 0.2376 \| 0.4944 \| 0.213 \| 0.097 \| 0.19 \| 0.3833 \| 0.2587 \| 0.4303 \| 0.4487 \| 0.2199 \| 0.399 \| 0.643 \| 0.5174 \| 0.6824 \| 0.1569 \| 0.4114 \| 0.171 \| 0.3902 \| 0.0818 \| 0.3923 \| 0.261 \| 0.3671 \| \| 1.0428 \| 47.0 \| 5029 \| 1.3096 \| 0.2421 \| 0.4797 \| 0.2235 \| 0.0976 \| 0.196 \| 0.4236 \| 0.2574 \| 0.4299 \| 0.4437 \| 0.2217 \| 0.3688 \| 0.6646 \| 0.5242 \| 0.6824 \| 0.155 \| 0.419 \| 0.1757 \| 0.396 \| 0.1006 \| 0.3585 \| 0.255 \| 0.3627 \| \| 1.0369 \| 48.0 \| 5136 \| 1.3049 \| 0.2447 \| 0.4953 \| 0.2069 \| 0.0929 \| 0.1971 \| 0.421 \| 0.2651 \| 0.4284 \| 0.4393 \| 0.1395 \| 0.3866 \| 0.6669 \| 0.5228 \| 0.6892 \| 0.1539 \| 0.419 \| 0.1757 \| 0.3763 \| 0.1091 \| 0.3492 \| 0.2619 \| 0.3627 \| \| 1.0315 \| 49.0 \| 5243 \| 1.3189 \| 0.2417 \| 0.4851 \| 0.2149 \| 0.0914 \| 0.1967 \| 0.4224 \| 0.2682 \| 0.4241 \| 0.4431 \| 0.1874 \| 0.3756 \| 0.6742 \| 0.5079 \| 0.691 \| 0.1679 \| 0.4215 \| 0.1627 \| 0.3857 \| 0.1174 \| 0.3569 \| 0.2527 \| 0.3604 \| \| 1.0149 \| 50.0 \| 5350 \| 1.3127 \| 0.2399 \| 0.4962 \| 0.2041 \| 0.096 \| 0.1854 \| 0.4276 \| 0.2638 \| 0.4178 \| 0.4325 \| 0.1657 \| 0.3617 \| 0.6608 \| 0.5148 \| 0.6797 \| 0.1434 \| 0.3734 \| 0.1616 \| 0.3746 \| 0.1342 \| 0.3831 \| 0.2454 \| 0.3516 \| \| 1.0223 \| 51.0 \| 5457 \| 1.2917 \| 0.2419 \| 0.4981 \| 0.2153 \| 0.0942 \| 0.2027 \| 0.4113 \| 0.2574 \| 0.4243 \| 0.4418 \| 0.1948 \| 0.3921 \| 0.6428 \| 0.5136 \| 0.6928 \| 0.1563 \| 0.4038 \| 0.1727 \| 0.3987 \| 0.1049 \| 0.36 \| 0.2618 \| 0.3538 \| \| 1.0072 \| 52.0 \| 5564 \| 1.3413 \| 0.2634 \| 0.5259 \| 0.2292 \| 0.0985 \| 0.2074 \| 0.4356 \| 0.2615 \| 0.415 \| 0.4305 \| 0.1818 \| 0.354 \| 0.6634 \| 0.5187 \| 0.6739 \| 0.2066 \| 0.4076 \| 0.1795 \| 0.3915 \| 0.1321 \| 0.3215 \| 0.2802 \| 0.3582 \| \| 0.9923 \| 53.0 \| 5671 \| 1.3195 \| 0.2393 \| 0.4806 \| 0.2104 \| 0.1019 \| 0.1819 \| 0.3959 \| 0.2597 \| 0.4239 \| 0.4406 \| 0.1933 \| 0.3776 \| 0.6387 \| 0.5188 \| 0.677 \| 0.1376 \| 0.4114 \| 0.1713 \| 0.4103 \| 0.091 \| 0.3308 \| 0.2775 \| 0.3738 \| \| 0.9981 \| 54.0 \| 5778 \| 1.3229 \| 0.2406 \| 0.5014 \| 0.2014 \| 0.1122 \| 0.1896 \| 0.404 \| 0.2441 \| 0.4128 \| 0.4303 \| 0.1999 \| 0.3707 \| 0.6241 \| 0.5172 \| 0.673 \| 0.1521 \| 0.3975 \| 0.1695 \| 0.4009 \| 0.1067 \| 0.32 \| 0.2574 \| 0.36 \| \| 0.9892 \| 55.0 \| 5885 \| 1.3100 \| 0.2483 \| 0.5044 \| 0.2104 \| 0.0779 \| 0.1985 \| 0.4205 \| 0.2606 \| 0.4207 \| 0.4357 \| 0.1744 \| 0.3783 \| 0.6592 \| 0.5088 \| 0.6608 \| 0.1744 \| 0.4051 \| 0.1689 \| 0.3915 \| 0.1261 \| 0.36 \| 0.2632 \| 0.3609 \| \| 0.9704 \| 56.0 \| 5992 \| 1.3102 \| 0.2508 \| 0.5103 \| 0.22 \| 0.0998 \| 0.1951 \| 0.4222 \| 0.259 \| 0.4147 \| 0.4341 \| 0.158 \| 0.3784 \| 0.6439 \| 0.5205 \| 0.6797 \| 0.1459 \| 0.4038 \| 0.1683 \| 0.4045 \| 0.1467 \| 0.3338 \| 0.2723 \| 0.3484 \| \| 0.9724 \| 57.0 \| 6099 \| 1.2838 \| 0.2591 \| 0.5087 \| 0.2339 \| 0.1102 \| 0.2132 \| 0.4197 \| 0.267 \| 0.4329 \| 0.4461 \| 0.2133 \| 0.3891 \| 0.6508 \| 0.5396 \| 0.6901 \| 0.1688 \| 0.4139 \| 0.1781 \| 0.3835 \| 0.1362 \| 0.3708 \| 0.2727 \| 0.3724 \| \| 0.9768 \| 58.0 \| 6206 \| 1.2982 \| 0.2591 \| 0.5133 \| 0.2325 \| 0.0919 \| 0.2165 \| 0.4129 \| 0.2634 \| 0.4314 \| 0.4431 \| 0.1627 \| 0.3999 \| 0.6589 \| 0.5289 \| 0.6923 \| 0.171 \| 0.4013 \| 0.1841 \| 0.3821 \| 0.1417 \| 0.3708 \| 0.2695 \| 0.3689 \| \| 0.974 \| 59.0 \| 6313 \| 1.2888 \| 0.2611 \| 0.5216 \| 0.239 \| 0.1126 \| 0.2094 \| 0.4504 \| 0.2659 \| 0.4367 \| 0.4506 \| 0.2099 \| 0.3957 \| 0.6611 \| 0.5364 \| 0.6833 \| 0.1824 \| 0.4304 \| 0.1768 \| 0.3884 \| 0.1455 \| 0.38 \| 0.2646 \| 0.3711 \| \| 0.9585 \| 60.0 \| 6420 \| 1.2801 \| 0.269 \| 0.5399 \| 0.2305 \| 0.103 \| 0.2209 \| 0.4622 \| 0.2636 \| 0.4453 \| 0.4548 \| 0.227 \| 0.3944 \| 0.6777 \| 0.5471 \| 0.6896 \| 0.1805 \| 0.4392 \| 0.1935 \| 0.3862 \| 0.1518 \| 0.3908 \| 0.272 \| 0.368 \| \| 0.9693 \| 61.0 \| 6527 \| 1.2816 \| 0.2685 \| 0.5368 \| 0.2347 \| 0.0904 \| 0.2268 \| 0.4546 \| 0.2661 \| 0.4332 \| 0.4445 \| 0.1645 \| 0.3918 \| 0.6802 \| 0.5394 \| 0.6811 \| 0.1894 \| 0.4481 \| 0.1913 \| 0.3754 \| 0.1505 \| 0.3585 \| 0.2718 \| 0.3596 \| \| 0.9653 \| 62.0 \| 6634 \| 1.2668 \| 0.2718 \| 0.5518 \| 0.2315 \| 0.1013 \| 0.2221 \| 0.4447 \| 0.2714 \| 0.4392 \| 0.4524 \| 0.2027 \| 0.3974 \| 0.6521 \| 0.5416 \| 0.6833 \| 0.1993 \| 0.4418 \| 0.193 \| 0.4018 \| 0.1515 \| 0.3646 \| 0.2733 \| 0.3707 \| \| 0.9367 \| 63.0 \| 6741 \| 1.2756 \| 0.2678 \| 0.5419 \| 0.2284 \| 0.0915 \| 0.2106 \| 0.4618 \| 0.2696 \| 0.4304 \| 0.4467 \| 0.2066 \| 0.3823 \| 0.6624 \| 0.5449 \| 0.6941 \| 0.1986 \| 0.4418 \| 0.1872 \| 0.4098 \| 0.1375 \| 0.32 \| 0.2706 \| 0.3676 \| \| 0.9397 \| 64.0 \| 6848 \| 1.2792 \| 0.2693 \| 0.543 \| 0.2349 \| 0.1105 \| 0.2191 \| 0.4305 \| 0.2671 \| 0.447 \| 0.4594 \| 0.2058 \| 0.4023 \| 0.6729 \| 0.532 \| 0.6856 \| 0.1938 \| 0.4544 \| 0.1966 \| 0.3942 \| 0.1431 \| 0.3892 \| 0.2811 \| 0.3738 \| \| 0.9362 \| 65.0 \| 6955 \| 1.2815 \| 0.2622 \| 0.5268 \| 0.2297 \| 0.0993 \| 0.2152 \| 0.4254 \| 0.2702 \| 0.4417 \| 0.4564 \| 0.1804 \| 0.404 \| 0.6691 \| 0.5354 \| 0.6815 \| 0.1833 \| 0.4608 \| 0.1828 \| 0.3763 \| 0.1314 \| 0.3831 \| 0.2783 \| 0.3804 \| \| 0.9256 \| 66.0 \| 7062 \| 1.2706 \| 0.2785 \| 0.5545 \| 0.2555 \| 0.11 \| 0.2213 \| 0.4632 \| 0.2807 \| 0.4514 \| 0.4665 \| 0.2383 \| 0.4052 \| 0.6819 \| 0.5466 \| 0.6977 \| 0.2198 \| 0.4532 \| 0.1932 \| 0.4076 \| 0.1533 \| 0.3938 \| 0.2795 \| 0.38 \| \| 0.9251 \| 67.0 \| 7169 \| 1.2693 \| 0.2779 \| 0.5672 \| 0.2466 \| 0.1024 \| 0.2213 \| 0.4665 \| 0.2746 \| 0.4364 \| 0.4467 \| 0.1908 \| 0.3882 \| 0.6738 \| 0.5463 \| 0.6896 \| 0.2235 \| 0.4329 \| 0.1956 \| 0.3853 \| 0.1504 \| 0.3508 \| 0.2735 \| 0.3747 \| \| 0.9284 \| 68.0 \| 7276 \| 1.2592 \| 0.2784 \| 0.5506 \| 0.2458 \| 0.0927 \| 0.2304 \| 0.483 \| 0.2735 \| 0.4356 \| 0.4443 \| 0.1732 \| 0.3946 \| 0.6872 \| 0.5438 \| 0.6896 \| 0.2034 \| 0.4038 \| 0.2033 \| 0.3951 \| 0.1668 \| 0.3585 \| 0.2749 \| 0.3747 \| \| 0.9217 \| 69.0 \| 7383 \| 1.2788 \| 0.2751 \| 0.5401 \| 0.2457 \| 0.0984 \| 0.223 \| 0.4747 \| 0.2762 \| 0.4435 \| 0.4547 \| 0.2076 \| 0.4048 \| 0.6831 \| 0.5437 \| 0.6896 \| 0.2062 \| 0.4392 \| 0.1936 \| 0.3888 \| 0.1552 \| 0.3738 \| 0.277 \| 0.3818 \| \| 0.8987 \| 70.0 \| 7490 \| 1.2390 \| 0.2757 \| 0.5535 \| 0.2487 \| 0.1123 \| 0.2263 \| 0.4587 \| 0.2759 \| 0.4482 \| 0.4603 \| 0.2193 \| 0.4165 \| 0.6799 \| 0.5415 \| 0.6901 \| 0.1939 \| 0.4532 \| 0.2053 \| 0.4116 \| 0.1652 \| 0.3646 \| 0.2727 \| 0.3822 \| \| 0.8797 \| 71.0 \| 7597 \| 1.2614 \| 0.2749 \| 0.5451 \| 0.2406 \| 0.1055 \| 0.2251 \| 0.4616 \| 0.275 \| 0.446 \| 0.459 \| 0.1965 \| 0.4088 \| 0.6842 \| 0.5438 \| 0.6928 \| 0.19 \| 0.4506 \| 0.2133 \| 0.4058 \| 0.1504 \| 0.3723 \| 0.2768 \| 0.3733 \| \| 0.8864 \| 72.0 \| 7704 \| 1.2601 \| 0.2813 \| 0.5445 \| 0.2524 \| 0.1029 \| 0.2261 \| 0.4549 \| 0.28 \| 0.4474 \| 0.4608 \| 0.2296 \| 0.4052 \| 0.6677 \| 0.5449 \| 0.6878 \| 0.2062 \| 0.4342 \| 0.2122 \| 0.4196 \| 0.1731 \| 0.3877 \| 0.2701 \| 0.3747 \| \| 0.8739 \| 73.0 \| 7811 \| 1.2542 \| 0.2809 \| 0.556 \| 0.2491 \| 0.1104 \| 0.2179 \| 0.4702 \| 0.2839 \| 0.4483 \| 0.4652 \| 0.2438 \| 0.4059 \| 0.6685 \| 0.5439 \| 0.6847 \| 0.1978 \| 0.4468 \| 0.2114 \| 0.429 \| 0.1767 \| 0.3892 \| 0.2746 \| 0.376 \| \| 0.8851 \| 74.0 \| 7918 \| 1.3047 \| 0.271 \| 0.5478 \| 0.2363 \| 0.1153 \| 0.2078 \| 0.4572 \| 0.2842 \| 0.4438 \| 0.4542 \| 0.2441 \| 0.3865 \| 0.665 \| 0.521 \| 0.6676 \| 0.1916 \| 0.4203 \| 0.214 \| 0.4286 \| 0.1578 \| 0.3892 \| 0.2704 \| 0.3653 \| \| 0.8902 \| 75.0 \| 8025 \| 1.2808 \| 0.2709 \| 0.5429 \| 0.2351 \| 0.101 \| 0.2174 \| 0.4628 \| 0.2806 \| 0.4448 \| 0.4583 \| 0.2163 \| 0.397 \| 0.6919 \| 0.5314 \| 0.677 \| 0.1856 \| 0.4342 \| 0.1961 \| 0.4036 \| 0.1625 \| 0.3969 \| 0.2791 \| 0.38 \| \| 0.8874 \| 76.0 \| 8132 \| 1.2806 \| 0.2812 \| 0.5664 \| 0.2465 \| 0.113 \| 0.2339 \| 0.4718 \| 0.2864 \| 0.4519 \| 0.4645 \| 0.2363 \| 0.4141 \| 0.688 \| 0.5339 \| 0.6869 \| 0.194 \| 0.4481 \| 0.208 \| 0.4228 \| 0.1807 \| 0.3738 \| 0.2897 \| 0.3907 \| \| 0.8809 \| 77.0 \| 8239 \| 1.2476 \| 0.2807 \| 0.5632 \| 0.2575 \| 0.1312 \| 0.2228 \| 0.4686 \| 0.2888 \| 0.4592 \| 0.4738 \| 0.2428 \| 0.4097 \| 0.7011 \| 0.5351 \| 0.6914 \| 0.1972 \| 0.4582 \| 0.2129 \| 0.4259 \| 0.1747 \| 0.4092 \| 0.2834 \| 0.384 \| \| 0.856 \| 78.0 \| 8346 \| 1.2580 \| 0.2878 \| 0.5675 \| 0.2528 \| 0.1216 \| 0.2298 \| 0.4649 \| 0.2918 \| 0.4565 \| 0.4712 \| 0.2357 \| 0.4122 \| 0.6921 \| 0.5297 \| 0.6923 \| 0.2236 \| 0.4456 \| 0.216 \| 0.4384 \| 0.1805 \| 0.3985 \| 0.2891 \| 0.3813 \| \| 0.8509 \| 79.0 \| 8453 \| 1.2741 \| 0.2811 \| 0.5657 \| 0.2445 \| 0.131 \| 0.2218 \| 0.4685 \| 0.2832 \| 0.4469 \| 0.4609 \| 0.2213 \| 0.3994 \| 0.6813 \| 0.5281 \| 0.6842 \| 0.2263 \| 0.4506 \| 0.205 \| 0.4179 \| 0.1643 \| 0.3708 \| 0.2817 \| 0.3809 \| \| 0.88 \| 80.0 \| 8560 \| 1.2556 \| 0.2818 \| 0.5628 \| 0.2442 \| 0.1218 \| 0.2313 \| 0.4529 \| 0.2848 \| 0.4525 \| 0.4659 \| 0.2211 \| 0.4178 \| 0.68 \| 0.536 \| 0.6878 \| 0.2158 \| 0.438 \| 0.2157 \| 0.4348 \| 0.1503 \| 0.3831 \| 0.2913 \| 0.3858 \| \| 0.8739 \| 81.0 \| 8667 \| 1.2754 \| 0.2797 \| 0.5595 \| 0.2432 \| 0.1169 \| 0.2255 \| 0.4812 \| 0.2791 \| 0.4481 \| 0.4632 \| 0.2257 \| 0.3972 \| 0.6992 \| 0.5369 \| 0.6901 \| 0.2077 \| 0.4544 \| 0.2024 \| 0.4076 \| 0.1652 \| 0.3846 \| 0.2864 \| 0.3791 \| \| 0.8604 \| 82.0 \| 8774 \| 1.2531 \| 0.2775 \| 0.5547 \| 0.2411 \| 0.121 \| 0.2204 \| 0.4861 \| 0.2823 \| 0.4461 \| 0.4577 \| 0.2244 \| 0.3945 \| 0.6931 \| 0.5363 \| 0.6824 \| 0.2038 \| 0.4215 \| 0.1948 \| 0.4085 \| 0.1749 \| 0.4062 \| 0.2777 \| 0.3698 \| \| 0.8504 \| 83.0 \| 8881 \| 1.2448 \| 0.2837 \| 0.5589 \| 0.2539 \| 0.1182 \| 0.2199 \| 0.4942 \| 0.2894 \| 0.4561 \| 0.4682 \| 0.2282 \| 0.4074 \| 0.6941 \| 0.5404 \| 0.6946 \| 0.2168 \| 0.4646 \| 0.2078 \| 0.4174 \| 0.1691 \| 0.38 \| 0.2844 \| 0.3844 \| \| 0.8394 \| 84.0 \| 8988 \| 1.2491 \| 0.2766 \| 0.5478 \| 0.2385 \| 0.1219 \| 0.2293 \| 0.4674 \| 0.2776 \| 0.4501 \| 0.4611 \| 0.2267 \| 0.4055 \| 0.6803 \| 0.544 \| 0.7023 \| 0.189 \| 0.4203 \| 0.2092 \| 0.4067 \| 0.164 \| 0.4031 \| 0.2769 \| 0.3733 \| \| 0.8257 \| 85.0 \| 9095 \| 1.2410 \| 0.2805 \| 0.5591 \| 0.2415 \| 0.1251 \| 0.2292 \| 0.471 \| 0.2783 \| 0.4592 \| 0.4689 \| 0.2249 \| 0.4214 \| 0.6866 \| 0.5408 \| 0.6973 \| 0.1995 \| 0.4608 \| 0.2056 \| 0.408 \| 0.1613 \| 0.3831 \| 0.295 \| 0.3951 \| \| 0.8416 \| 86.0 \| 9202 \| 1.2492 \| 0.2868 \| 0.5613 \| 0.2512 \| 0.1136 \| 0.2345 \| 0.4828 \| 0.2807 \| 0.455 \| 0.4644 \| 0.2239 \| 0.4123 \| 0.6914 \| 0.5519 \| 0.6905 \| 0.209 \| 0.4443 \| 0.2155 \| 0.4143 \| 0.1671 \| 0.3815 \| 0.2907 \| 0.3916 \| \| 0.8395 \| 87.0 \| 9309 \| 1.2354 \| 0.2863 \| 0.5545 \| 0.2498 \| 0.1311 \| 0.2408 \| 0.4847 \| 0.2865 \| 0.4603 \| 0.4709 \| 0.2264 \| 0.4195 \| 0.6952 \| 0.538 \| 0.6914 \| 0.2185 \| 0.4734 \| 0.2144 \| 0.4152 \| 0.1696 \| 0.3908 \| 0.2908 \| 0.3836 \| \| 0.831 \| 88.0 \| 9416 \| 1.2456 \| 0.2801 \| 0.5556 \| 0.2431 \| 0.122 \| 0.2307 \| 0.4571 \| 0.2787 \| 0.4571 \| 0.4697 \| 0.2377 \| 0.4118 \| 0.6943 \| 0.5398 \| 0.6946 \| 0.1915 \| 0.4418 \| 0.2088 \| 0.4223 \| 0.1688 \| 0.4062 \| 0.2916 \| 0.3836 \| \| 0.8135 \| 89.0 \| 9523 \| 1.2334 \| 0.2837 \| 0.5639 \| 0.2552 \| 0.1354 \| 0.2321 \| 0.4607 \| 0.2801 \| 0.4679 \| 0.4786 \| 0.2477 \| 0.4247 \| 0.7046 \| 0.5416 \| 0.6937 \| 0.2148 \| 0.4747 \| 0.2154 \| 0.4308 \| 0.156 \| 0.4077 \| 0.2908 \| 0.3862 \| \| 0.8169 \| 90.0 \| 9630 \| 1.2384 \| 0.2866 \| 0.5663 \| 0.2508 \| 0.1454 \| 0.237 \| 0.4681 \| 0.2799 \| 0.4634 \| 0.4749 \| 0.2483 \| 0.4152 \| 0.6961 \| 0.5472 \| 0.6928 \| 0.2252 \| 0.4709 \| 0.2082 \| 0.4174 \| 0.1521 \| 0.4 \| 0.3003 \| 0.3933 \| \| 0.8085 \| 91.0 \| 9737 \| 1.2410 \| 0.2913 \| 0.5751 \| 0.2568 \| 0.1375 \| 0.2386 \| 0.476 \| 0.2839 \| 0.4661 \| 0.4805 \| 0.2439 \| 0.4281 \| 0.6948 \| 0.5469 \| 0.6982 \| 0.2298 \| 0.4848 \| 0.2134 \| 0.4183 \| 0.1742 \| 0.4046 \| 0.2924 \| 0.3964 \| \| 0.8272 \| 92.0 \| 9844 \| 1.2486 \| 0.29 \| 0.5747 \| 0.26 \| 0.1216 \| 0.2434 \| 0.4781 \| 0.2828 \| 0.4639 \| 0.4757 \| 0.2383 \| 0.4208 \| 0.6864 \| 0.545 \| 0.6959 \| 0.219 \| 0.4722 \| 0.2112 \| 0.4098 \| 0.183 \| 0.4062 \| 0.2915 \| 0.3947 \| \| 0.7949 \| 93.0 \| 9951 \| 1.2430 \| 0.2786 \| 0.5565 \| 0.2355 \| 0.1252 \| 0.2239 \| 0.4703 \| 0.2809 \| 0.4591 \| 0.4724 \| 0.2344 \| 0.4165 \| 0.7 \| 0.5329 \| 0.6838 \| 0.2075 \| 0.4696 \| 0.2052 \| 0.4219 \| 0.162 \| 0.3954 \| 0.2852 \| 0.3911 \| \| 0.8009 \| 94.0 \| 10058 \| 1.2419 \| 0.2866 \| 0.5667 \| 0.2557 \| 0.1403 \| 0.2326 \| 0.4687 \| 0.2796 \| 0.4672 \| 0.4787 \| 0.24 \| 0.4214 \| 0.7029 \| 0.5401 \| 0.6865 \| 0.2134 \| 0.4848 \| 0.2098 \| 0.4192 \| 0.1778 \| 0.4046 \| 0.2917 \| 0.3982 \| \| 0.8088 \| 95.0 \| 10165 \| 1.2328 \| 0.2838 \| 0.57 \| 0.249 \| 0.1482 \| 0.2328 \| 0.4686 \| 0.2851 \| 0.4616 \| 0.4754 \| 0.2413 \| 0.4191 \| 0.6996 \| 0.5383 \| 0.6847 \| 0.2113 \| 0.4797 \| 0.2114 \| 0.4183 \| 0.1672 \| 0.3954 \| 0.2907 \| 0.3987 \| \| 0.8113 \| 96.0 \| 10272 \| 1.2335 \| 0.2852 \| 0.5734 \| 0.2426 \| 0.1313 \| 0.2336 \| 0.4788 \| 0.2848 \| 0.4614 \| 0.4742 \| 0.2416 \| 0.4169 \| 0.6924 \| 0.5401 \| 0.686 \| 0.2135 \| 0.481 \| 0.2115 \| 0.4121 \| 0.1693 \| 0.3954 \| 0.2914 \| 0.3964 \| \| 0.7898 \| 97.0 \| 10379 \| 1.2327 \| 0.2867 \| 0.5712 \| 0.2504 \| 0.1332 \| 0.2335 \| 0.4682 \| 0.288 \| 0.4648 \| 0.4785 \| 0.2391 \| 0.421 \| 0.7037 \| 0.5415 \| 0.6892 \| 0.2193 \| 0.4949 \| 0.2123 \| 0.4112 \| 0.1712 \| 0.4015 \| 0.2891 \| 0.3956 \| \| 0.8043 \| 98.0 \| 10486 \| 1.2280 \| 0.288 \| 0.5693 \| 0.2456 \| 0.1247 \| 0.2341 \| 0.4886 \| 0.29 \| 0.4667 \| 0.4785 \| 0.2389 \| 0.4194 \| 0.711 \| 0.5462 \| 0.6914 \| 0.2148 \| 0.4785 \| 0.2133 \| 0.4152 \| 0.1746 \| 0.4108 \| 0.2908 \| 0.3964 \| \| 0.8017 \| 99.0 \| 10593 \| 1.2349 \| 0.2863 \| 0.5661 \| 0.2508 \| 0.123 \| 0.2321 \| 0.4837 \| 0.2882 \| 0.4639 \| 0.4755 \| 0.2338 \| 0.4168 \| 0.7093 \| 0.5448 \| 0.691 \| 0.2127 \| 0.4722 \| 0.2136 \| 0.4103 \| 0.1744 \| 0.4123 \| 0.286 \| 0.3916 \| \| 0.7948 \| 100.0 \| 10700 \| 1.2371 \| 0.2873 \| 0.5678 \| 0.2501 \| 0.126 \| 0.2327 \| 0.4873 \| 0.2843 \| 0.4643 \| 0.4762 \| 0.2338 \| 0.4167 \| 0.7114 \| 0.5461 \| 0.6932 \| 0.2167 \| 0.4785 \| 0.2135 \| 0.4094 \| 0.173 \| 0.4092 \| 0.2871 \| 0.3907 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session2	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session2 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]
SIS-2024-spring/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
ChiJuiChen/Lab8_DETR_BOAT	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Lab8_DETR_BOAT This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.0+cu118 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
Wellyowo/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
uwwee/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
sunfu-chou/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
leowang707/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
cj94/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
wennnny/detr-resnet-50-finetuned-real-boat-dataset	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.2 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]
nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session6	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session6 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]
Dilipan/detr-finetuned-edzola-form-section	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8" ]
zajko194/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
schoonhovenra/20240502	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 20240502 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 400 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1	[ "wheel_front_left", "wheel_front_right", "wheel_rear_left", "wheel_rear_right", "head_light_left", "head_light_right", "rear_light_left", "rear_light_right", "third_brake_light", "mirror_left", "mirror_right", "brand_badge", "brand_text", "engine_type", "type_badge", "filler_cap", "tow_bar", "roof_rack", "exhaust", "panorama_roof", "antenna", "spare_wheel", "parking_sensor", "license_plate_holder", "fog_light_front_left", "fog_light_front_right", "model_text", "driving_assistance_grill", "driving_assistance_windshield", "license_plate", "car", "backseat", "driving wheel", "dashboard", "center console", "window front left", "window front right", "back mirror" ]
nsugianto/detr-resnet50_finetuned_mstbltransf_lsdocelementdetv1type5_session1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet50_finetuned_mstbltransf_lsdocelementdetv1type5_session1 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "leftcap_rightval", "topcap_bottomval", "paragraphtext", "tablenogroup", "tablewithgroup" ]
jweaths/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
mserrasa/yolo_finetuned_VinBigData	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # yolo_finetuned_VinBigData This model is a fine-tuned version of [mserrasa/yolo_finetuned_VinBigData](https://huggingface.co/mserrasa/yolo_finetuned_VinBigData) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1	[ "aortic enlargement", "cardiomegaly", "nodule/mass", "pleural thickening", "pulmonary fibrosis" ]
mserrasa/yolos_finetuned_VinBigData	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # yolos_finetuned_VinBigData This model is a fine-tuned version of [mserrasa/yolos_finetuned_VinBigData](https://huggingface.co/mserrasa/yolos_finetuned_VinBigData) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1	[ "aortic enlargement", "cardiomegaly", "nodule/mass", "pleural thickening", "pulmonary fibrosis" ]

vrglx333/detr-finetuned-personal-equipment

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14" ]

nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4

# detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session3](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session3) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]

oskarkuuse/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.0882 | 1.0 | 1250 | 1.3492 | | 1.6047 | 2.0 | 2500 | 1.2964 | | 1.5492 | 3.0 | 3750 | 1.2105 | | 1.3223 | 4.0 | 5000 | 1.1513 | | 1.1328 | 5.0 | 6250 | 1.1143 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session5

# detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session5 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session4) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]

maxencerch/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3326 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5582 | 0.32 | 100 | 1.7392 | | 1.5953 | 0.64 | 200 | 1.4379 | | 1.4199 | 0.96 | 300 | 1.3326 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

rjhugs/modelStructure_TT_V3

# modelStructure_TT_V3 This model is a fine-tuned version of [microsoft/table-transformer-structure-recognition-v1.1-all](https://huggingface.co/microsoft/table-transformer-structure-recognition-v1.1-all) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.2+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

[ "table", "table column header", "table column" ]

marie434/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9455 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.504 | 0.16 | 100 | 1.4472 | | 1.3067 | 0.32 | 200 | 1.2490 | | 1.1213 | 0.48 | 300 | 1.1124 | | 1.1397 | 0.64 | 400 | 1.0763 | | 1.0015 | 0.8 | 500 | 0.9798 | | 1.0052 | 0.96 | 600 | 0.9455 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "class_0", "class_1", "class_2", "class_3" ]

qubvel-hf/detr-resnet-50-finetuned-10k-cppe5

# detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 0.9865 - Map: 0.3578 - Map 50: 0.6781 - Map 75: 0.3105 - Map Small: 0.3578 - Map Medium: -1.0 - Map Large: -1.0 - Mar 1: 0.365 - Mar 10: 0.535 - Mar 100: 0.5483 - Mar Small: 0.5483 - Mar Medium: -1.0 - Mar Large: -1.0 - Map Coverall: 0.6584 - Mar 100 Coverall: 0.7772 - Map Face Shield: 0.3691 - Mar 100 Face Shield: 0.6063 - Map Gloves: 0.2477 - Mar 100 Gloves: 0.4266 - Map Goggles: 0.1766 - Mar 100 Goggles: 0.4655 - Map Mask: 0.3371 - Mar 100 Mask: 0.4661 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 3.7198 | 1.0 | 107 | 3.1869 | 0.0036 | 0.0144 | 0.0006 | 0.0036 | -1.0 | -1.0 | 0.017 | 0.0426 | 0.0647 | 0.0647 | -1.0 | -1.0 | 0.0169 | 0.1994 | 0.0 | 0.0 | 0.0001 | 0.0177 | 0.0 | 0.0 | 0.0008 | 0.1062 | | 3.0393 | 2.0 | 214 | 2.8978 | 0.007 | 0.021 | 0.003 | 0.007 | -1.0 | -1.0 | 0.0192 | 0.0796 | 0.1238 | 0.1238 | -1.0 | -1.0 | 0.0333 | 0.5216 | 0.0 | 0.0 | 0.0001 | 0.0292 | 0.0 | 0.0 | 0.0017 | 0.0684 | | 2.7812 | 3.0 | 321 | 2.5445 | 0.0138 | 0.0414 | 0.0085 | 0.0138 | -1.0 | -1.0 | 0.0307 | 0.0994 | 0.1258 | 0.1258 | -1.0 | -1.0 | 0.0655 | 0.4938 | 0.0 | 0.0 | 0.0002 | 0.0354 | 0.0 | 0.0 | 0.0033 | 0.1 | | 2.5992 | 4.0 | 428 | 2.3828 | 0.0232 | 0.0601 | 0.0155 | 0.0232 | -1.0 | -1.0 | 0.0423 | 0.1202 | 0.1518 | 0.1518 | -1.0 | -1.0 | 0.1021 | 0.5481 | 0.0 | 0.0 | 0.0006 | 0.0495 | 0.0059 | 0.0109 | 0.0072 | 0.1503 | | 2.3828 | 5.0 | 535 | 2.2672 | 0.0283 | 0.0703 | 0.0179 | 0.0283 | -1.0 | -1.0 | 0.0521 | 0.1283 | 0.1737 | 0.1737 | -1.0 | -1.0 | 0.1344 | 0.5846 | 0.0 | 0.0 | 0.001 | 0.0833 | 0.0 | 0.0 | 0.0063 | 0.2006 | | 2.2633 | 6.0 | 642 | 2.0618 | 0.0479 | 0.0996 | 0.0416 | 0.0479 | -1.0 | -1.0 | 0.0782 | 0.1679 | 0.2035 | 0.2035 | -1.0 | -1.0 | 0.2099 | 0.6333 | 0.003 | 0.0159 | 0.0018 | 0.1187 | 0.0052 | 0.0218 | 0.0195 | 0.2277 | | 2.1837 | 7.0 | 749 | 2.1100 | 0.0455 | 0.1159 | 0.0255 | 0.0455 | -1.0 | -1.0 | 0.0747 | 0.1582 | 0.1894 | 0.1894 | -1.0 | -1.0 | 0.2068 | 0.6185 | 0.0085 | 0.0556 | 0.001 | 0.0734 | 0.0002 | 0.0018 | 0.0113 | 0.1977 | | 2.0689 | 8.0 | 856 | 2.0000 | 0.054 | 0.1389 | 0.0301 | 0.054 | -1.0 | -1.0 | 0.0954 | 0.1846 | 0.2159 | 0.2159 | -1.0 | -1.0 | 0.2155 | 0.5537 | 0.0314 | 0.1397 | 0.0049 | 0.1406 | 0.0002 | 0.0018 | 0.0181 | 0.2435 | | 2.0417 | 9.0 | 963 | 1.8702 | 0.0697 | 0.1631 | 0.0501 | 0.0697 | -1.0 | -1.0 | 0.1074 | 0.2173 | 0.257 | 0.257 | -1.0 | -1.0 | 0.2826 | 0.6086 | 0.0279 | 0.181 | 0.0046 | 0.1734 | 0.0102 | 0.0418 | 0.0234 | 0.2802 | | 1.9972 | 10.0 | 1070 | 1.8563 | 0.0742 | 0.1568 | 0.0541 | 0.0742 | -1.0 | -1.0 | 0.1196 | 0.2416 | 0.2786 | 0.2786 | -1.0 | -1.0 | 0.2933 | 0.6086 | 0.0233 | 0.1921 | 0.0053 | 0.1672 | 0.0239 | 0.0891 | 0.025 | 0.3362 | | 1.8931 | 11.0 | 1177 | 1.6778 | 0.1054 | 0.2248 | 0.0898 | 0.1054 | -1.0 | -1.0 | 0.1456 | 0.2764 | 0.3033 | 0.3033 | -1.0 | -1.0 | 0.3955 | 0.671 | 0.0498 | 0.2603 | 0.0108 | 0.2188 | 0.0149 | 0.0382 | 0.056 | 0.3282 | | 1.8269 | 12.0 | 1284 | 1.6905 | 0.1111 | 0.2399 | 0.0942 | 0.1111 | -1.0 | -1.0 | 0.1543 | 0.2949 | 0.3257 | 0.3257 | -1.0 | -1.0 | 0.4113 | 0.679 | 0.069 | 0.319 | 0.0087 | 0.2021 | 0.015 | 0.0909 | 0.0514 | 0.3373 | | 1.8036 | 13.0 | 1391 | 1.6406 | 0.1149 | 0.2407 | 0.097 | 0.1149 | -1.0 | -1.0 | 0.1636 | 0.3108 | 0.3372 | 0.3372 | -1.0 | -1.0 | 0.4255 | 0.6759 | 0.0771 | 0.3381 | 0.0109 | 0.2182 | 0.0137 | 0.1309 | 0.047 | 0.3226 | | 1.7463 | 14.0 | 1498 | 1.7169 | 0.1106 | 0.2421 | 0.0875 | 0.1106 | -1.0 | -1.0 | 0.1776 | 0.3205 | 0.3511 | 0.3511 | -1.0 | -1.0 | 0.3996 | 0.7 | 0.0404 | 0.2476 | 0.0117 | 0.2458 | 0.0257 | 0.2036 | 0.0757 | 0.3582 | | 1.763 | 15.0 | 1605 | 1.5961 | 0.1245 | 0.2577 | 0.1018 | 0.1245 | -1.0 | -1.0 | 0.1817 | 0.3384 | 0.3677 | 0.3677 | -1.0 | -1.0 | 0.4575 | 0.6679 | 0.0775 | 0.3698 | 0.0107 | 0.2505 | 0.0318 | 0.1964 | 0.0447 | 0.3537 | | 1.6467 | 16.0 | 1712 | 1.5365 | 0.1376 | 0.3073 | 0.1062 | 0.1376 | -1.0 | -1.0 | 0.2164 | 0.38 | 0.408 | 0.408 | -1.0 | -1.0 | 0.455 | 0.6852 | 0.0739 | 0.3873 | 0.0215 | 0.2719 | 0.0442 | 0.2891 | 0.0934 | 0.4068 | | 1.6222 | 17.0 | 1819 | 1.5990 | 0.1295 | 0.2696 | 0.1026 | 0.1295 | -1.0 | -1.0 | 0.1802 | 0.3409 | 0.3693 | 0.3693 | -1.0 | -1.0 | 0.4577 | 0.6654 | 0.0786 | 0.3619 | 0.0297 | 0.2958 | 0.0211 | 0.2218 | 0.0603 | 0.3017 | | 1.6239 | 18.0 | 1926 | 1.4164 | 0.159 | 0.3543 | 0.1262 | 0.159 | -1.0 | -1.0 | 0.235 | 0.3929 | 0.4138 | 0.4138 | -1.0 | -1.0 | 0.4753 | 0.7204 | 0.0921 | 0.3968 | 0.039 | 0.2922 | 0.0323 | 0.2636 | 0.1565 | 0.396 | | 1.5448 | 19.0 | 2033 | 1.4689 | 0.1628 | 0.3725 | 0.1314 | 0.1628 | -1.0 | -1.0 | 0.205 | 0.3811 | 0.4064 | 0.4064 | -1.0 | -1.0 | 0.4794 | 0.6895 | 0.1038 | 0.419 | 0.0398 | 0.2828 | 0.0333 | 0.28 | 0.1578 | 0.3605 | | 1.5026 | 20.0 | 2140 | 1.4093 | 0.1798 | 0.397 | 0.1369 | 0.1798 | -1.0 | -1.0 | 0.2336 | 0.4125 | 0.4349 | 0.4349 | -1.0 | -1.0 | 0.4851 | 0.6858 | 0.1494 | 0.4508 | 0.0341 | 0.2859 | 0.0434 | 0.3382 | 0.1869 | 0.4136 | | 1.4797 | 21.0 | 2247 | 1.4605 | 0.1652 | 0.3605 | 0.1254 | 0.1652 | -1.0 | -1.0 | 0.2295 | 0.3823 | 0.4041 | 0.4041 | -1.0 | -1.0 | 0.4978 | 0.6957 | 0.0968 | 0.3825 | 0.0529 | 0.2797 | 0.0263 | 0.3236 | 0.1522 | 0.339 | | 1.4298 | 22.0 | 2354 | 1.4231 | 0.163 | 0.3558 | 0.115 | 0.163 | -1.0 | -1.0 | 0.2256 | 0.3851 | 0.4108 | 0.4108 | -1.0 | -1.0 | 0.4902 | 0.7093 | 0.1033 | 0.4159 | 0.0515 | 0.313 | 0.0261 | 0.3109 | 0.1437 | 0.3051 | | 1.4157 | 23.0 | 2461 | 1.3665 | 0.1914 | 0.4048 | 0.1533 | 0.1914 | -1.0 | -1.0 | 0.2478 | 0.4232 | 0.447 | 0.447 | -1.0 | -1.0 | 0.491 | 0.6975 | 0.1599 | 0.4683 | 0.0502 | 0.3021 | 0.0603 | 0.3618 | 0.1956 | 0.4051 | | 1.4438 | 24.0 | 2568 | 1.2908 | 0.2103 | 0.433 | 0.168 | 0.2103 | -1.0 | -1.0 | 0.2643 | 0.4512 | 0.4761 | 0.4761 | -1.0 | -1.0 | 0.5368 | 0.7136 | 0.1493 | 0.4873 | 0.0789 | 0.3609 | 0.043 | 0.3891 | 0.2433 | 0.4294 | | 1.4044 | 25.0 | 2675 | 1.4752 | 0.1709 | 0.3749 | 0.1388 | 0.1709 | -1.0 | -1.0 | 0.2187 | 0.3926 | 0.4191 | 0.4191 | -1.0 | -1.0 | 0.4862 | 0.7167 | 0.09 | 0.3905 | 0.0762 | 0.299 | 0.0393 | 0.3527 | 0.1627 | 0.3367 | | 1.3703 | 26.0 | 2782 | 1.3047 | 0.2162 | 0.4568 | 0.1714 | 0.2162 | -1.0 | -1.0 | 0.2661 | 0.4344 | 0.4548 | 0.4548 | -1.0 | -1.0 | 0.5342 | 0.7272 | 0.166 | 0.4508 | 0.0971 | 0.3281 | 0.0424 | 0.3527 | 0.2414 | 0.4153 | | 1.3292 | 27.0 | 2889 | 1.2674 | 0.22 | 0.4681 | 0.1702 | 0.22 | -1.0 | -1.0 | 0.2743 | 0.4286 | 0.4473 | 0.4473 | -1.0 | -1.0 | 0.5438 | 0.7265 | 0.2128 | 0.4429 | 0.1171 | 0.3443 | 0.0387 | 0.3455 | 0.1878 | 0.3774 | | 1.359 | 28.0 | 2996 | 1.3156 | 0.2007 | 0.4272 | 0.1536 | 0.2007 | -1.0 | -1.0 | 0.2715 | 0.4384 | 0.4555 | 0.4555 | -1.0 | -1.0 | 0.5306 | 0.7111 | 0.163 | 0.5016 | 0.0896 | 0.3135 | 0.0307 | 0.38 | 0.1898 | 0.3712 | | 1.3471 | 29.0 | 3103 | 1.2646 | 0.2161 | 0.455 | 0.172 | 0.2161 | -1.0 | -1.0 | 0.277 | 0.4492 | 0.4728 | 0.4728 | -1.0 | -1.0 | 0.5301 | 0.7216 | 0.1708 | 0.519 | 0.1216 | 0.3271 | 0.0391 | 0.4145 | 0.2189 | 0.3819 | | 1.308 | 30.0 | 3210 | 1.3017 | 0.2107 | 0.4465 | 0.1718 | 0.2107 | -1.0 | -1.0 | 0.2556 | 0.4141 | 0.4387 | 0.4387 | -1.0 | -1.0 | 0.5321 | 0.7136 | 0.1531 | 0.454 | 0.1037 | 0.3203 | 0.0334 | 0.3218 | 0.2313 | 0.3836 | | 1.3023 | 31.0 | 3317 | 1.2809 | 0.2174 | 0.462 | 0.1714 | 0.2174 | -1.0 | -1.0 | 0.2646 | 0.4242 | 0.4473 | 0.4473 | -1.0 | -1.0 | 0.5484 | 0.7259 | 0.1686 | 0.427 | 0.1163 | 0.3536 | 0.0506 | 0.3564 | 0.2029 | 0.3734 | | 1.2561 | 32.0 | 3424 | 1.2679 | 0.2082 | 0.4557 | 0.1579 | 0.2082 | -1.0 | -1.0 | 0.263 | 0.4283 | 0.4511 | 0.4511 | -1.0 | -1.0 | 0.541 | 0.7284 | 0.1448 | 0.4556 | 0.1308 | 0.3682 | 0.0411 | 0.3436 | 0.1835 | 0.3599 | | 1.268 | 33.0 | 3531 | 1.2632 | 0.2304 | 0.4841 | 0.1882 | 0.2304 | -1.0 | -1.0 | 0.2852 | 0.4469 | 0.4629 | 0.4629 | -1.0 | -1.0 | 0.5535 | 0.7327 | 0.2042 | 0.5286 | 0.1027 | 0.2917 | 0.0472 | 0.38 | 0.2441 | 0.3814 | | 1.2337 | 34.0 | 3638 | 1.2157 | 0.2339 | 0.497 | 0.1854 | 0.2339 | -1.0 | -1.0 | 0.2845 | 0.4495 | 0.4756 | 0.4756 | -1.0 | -1.0 | 0.5704 | 0.7414 | 0.187 | 0.5095 | 0.1339 | 0.3448 | 0.0414 | 0.3982 | 0.2367 | 0.3842 | | 1.2192 | 35.0 | 3745 | 1.2109 | 0.2505 | 0.5078 | 0.2013 | 0.2505 | -1.0 | -1.0 | 0.288 | 0.4617 | 0.4795 | 0.4795 | -1.0 | -1.0 | 0.5599 | 0.7259 | 0.2431 | 0.5016 | 0.1564 | 0.3521 | 0.0496 | 0.4164 | 0.2437 | 0.4017 | | 1.2119 | 36.0 | 3852 | 1.1897 | 0.2584 | 0.5229 | 0.2198 | 0.2584 | -1.0 | -1.0 | 0.2986 | 0.4575 | 0.4744 | 0.4744 | -1.0 | -1.0 | 0.5742 | 0.7383 | 0.2667 | 0.5159 | 0.1389 | 0.35 | 0.0448 | 0.3473 | 0.2676 | 0.4203 | | 1.1686 | 37.0 | 3959 | 1.1441 | 0.2581 | 0.5164 | 0.2072 | 0.2581 | -1.0 | -1.0 | 0.3002 | 0.4712 | 0.4918 | 0.4918 | -1.0 | -1.0 | 0.5904 | 0.7642 | 0.2406 | 0.5238 | 0.1535 | 0.3708 | 0.0404 | 0.3945 | 0.2654 | 0.4056 | | 1.1664 | 38.0 | 4066 | 1.1554 | 0.2618 | 0.546 | 0.204 | 0.2618 | -1.0 | -1.0 | 0.3078 | 0.4822 | 0.5069 | 0.5069 | -1.0 | -1.0 | 0.5674 | 0.734 | 0.2486 | 0.519 | 0.1922 | 0.3927 | 0.0409 | 0.4509 | 0.26 | 0.4379 | | 1.1746 | 39.0 | 4173 | 1.1873 | 0.2514 | 0.5269 | 0.1995 | 0.2514 | -1.0 | -1.0 | 0.292 | 0.4628 | 0.4842 | 0.4842 | -1.0 | -1.0 | 0.5527 | 0.7235 | 0.2427 | 0.5127 | 0.1591 | 0.3448 | 0.0468 | 0.4218 | 0.2557 | 0.4181 | | 1.1559 | 40.0 | 4280 | 1.1594 | 0.2652 | 0.5517 | 0.2014 | 0.2652 | -1.0 | -1.0 | 0.2955 | 0.4743 | 0.4885 | 0.4885 | -1.0 | -1.0 | 0.5685 | 0.7377 | 0.2624 | 0.5238 | 0.1852 | 0.3755 | 0.0505 | 0.3873 | 0.2595 | 0.4181 | | 1.1668 | 41.0 | 4387 | 1.1562 | 0.2479 | 0.5294 | 0.1853 | 0.2479 | -1.0 | -1.0 | 0.2967 | 0.4772 | 0.4994 | 0.4994 | -1.0 | -1.0 | 0.5742 | 0.7272 | 0.217 | 0.5413 | 0.1492 | 0.3453 | 0.058 | 0.4709 | 0.2411 | 0.4124 | | 1.1084 | 42.0 | 4494 | 1.1302 | 0.2634 | 0.5352 | 0.2094 | 0.2634 | -1.0 | -1.0 | 0.3135 | 0.4783 | 0.4945 | 0.4945 | -1.0 | -1.0 | 0.5847 | 0.7333 | 0.2335 | 0.5397 | 0.1653 | 0.3583 | 0.0484 | 0.4127 | 0.2851 | 0.4282 | | 1.1118 | 43.0 | 4601 | 1.1270 | 0.2754 | 0.5506 | 0.2408 | 0.2754 | -1.0 | -1.0 | 0.3103 | 0.4789 | 0.4973 | 0.4973 | -1.0 | -1.0 | 0.5867 | 0.7364 | 0.2662 | 0.5476 | 0.1587 | 0.3682 | 0.0599 | 0.3855 | 0.3057 | 0.4486 | | 1.1096 | 44.0 | 4708 | 1.1772 | 0.2611 | 0.5565 | 0.2002 | 0.2611 | -1.0 | -1.0 | 0.2873 | 0.4606 | 0.4755 | 0.4755 | -1.0 | -1.0 | 0.5739 | 0.7228 | 0.247 | 0.5143 | 0.1649 | 0.3516 | 0.081 | 0.4073 | 0.2389 | 0.3814 | | 1.0741 | 45.0 | 4815 | 1.0871 | 0.2857 | 0.5718 | 0.2186 | 0.2857 | -1.0 | -1.0 | 0.3237 | 0.4934 | 0.5104 | 0.5104 | -1.0 | -1.0 | 0.5927 | 0.75 | 0.2775 | 0.5698 | 0.1899 | 0.3786 | 0.0781 | 0.4145 | 0.2904 | 0.439 | | 1.0774 | 46.0 | 4922 | 1.1049 | 0.2684 | 0.5461 | 0.2197 | 0.2684 | -1.0 | -1.0 | 0.3112 | 0.4956 | 0.5115 | 0.5115 | -1.0 | -1.0 | 0.5979 | 0.7574 | 0.2552 | 0.5794 | 0.143 | 0.3677 | 0.062 | 0.4291 | 0.284 | 0.4237 | | 1.0517 | 47.0 | 5029 | 1.1005 | 0.2806 | 0.5821 | 0.2184 | 0.2806 | -1.0 | -1.0 | 0.3112 | 0.4872 | 0.5048 | 0.5048 | -1.0 | -1.0 | 0.5861 | 0.7543 | 0.2862 | 0.5619 | 0.1749 | 0.4016 | 0.0737 | 0.3945 | 0.282 | 0.4119 | | 1.0717 | 48.0 | 5136 | 1.0878 | 0.2956 | 0.5902 | 0.2492 | 0.2956 | -1.0 | -1.0 | 0.3146 | 0.4925 | 0.5089 | 0.5089 | -1.0 | -1.0 | 0.6128 | 0.7506 | 0.2813 | 0.5381 | 0.1947 | 0.4036 | 0.0849 | 0.4091 | 0.3041 | 0.4429 | | 1.0578 | 49.0 | 5243 | 1.0932 | 0.2816 | 0.5671 | 0.2246 | 0.2816 | -1.0 | -1.0 | 0.3137 | 0.479 | 0.4943 | 0.4943 | -1.0 | -1.0 | 0.6069 | 0.7537 | 0.2616 | 0.4873 | 0.1947 | 0.4026 | 0.0613 | 0.3964 | 0.2835 | 0.4316 | | 1.0367 | 50.0 | 5350 | 1.0784 | 0.2956 | 0.5813 | 0.2416 | 0.2956 | -1.0 | -1.0 | 0.3308 | 0.4864 | 0.4979 | 0.4979 | -1.0 | -1.0 | 0.6193 | 0.7642 | 0.2809 | 0.5143 | 0.1841 | 0.3854 | 0.0885 | 0.3891 | 0.3053 | 0.4367 | | 1.0239 | 51.0 | 5457 | 1.0702 | 0.2893 | 0.5689 | 0.2387 | 0.2893 | -1.0 | -1.0 | 0.315 | 0.4944 | 0.5097 | 0.5097 | -1.0 | -1.0 | 0.5978 | 0.763 | 0.2656 | 0.5095 | 0.1887 | 0.3938 | 0.0817 | 0.4309 | 0.3125 | 0.4514 | | 1.0127 | 52.0 | 5564 | 1.0771 | 0.2833 | 0.5713 | 0.243 | 0.2833 | -1.0 | -1.0 | 0.3184 | 0.4856 | 0.5075 | 0.5075 | -1.0 | -1.0 | 0.6007 | 0.7605 | 0.2649 | 0.5175 | 0.1843 | 0.3932 | 0.0766 | 0.4364 | 0.2902 | 0.4299 | | 0.9948 | 53.0 | 5671 | 1.1039 | 0.2821 | 0.5628 | 0.2607 | 0.2821 | -1.0 | -1.0 | 0.3143 | 0.4954 | 0.5132 | 0.5132 | -1.0 | -1.0 | 0.6002 | 0.7599 | 0.2846 | 0.5587 | 0.1729 | 0.3693 | 0.0646 | 0.4582 | 0.2884 | 0.4198 | | 1.0026 | 54.0 | 5778 | 1.1125 | 0.2811 | 0.5569 | 0.2461 | 0.2811 | -1.0 | -1.0 | 0.3166 | 0.4846 | 0.5019 | 0.5019 | -1.0 | -1.0 | 0.6118 | 0.7691 | 0.2617 | 0.5286 | 0.1762 | 0.376 | 0.0625 | 0.3964 | 0.2932 | 0.4395 | | 1.0037 | 55.0 | 5885 | 1.1172 | 0.2819 | 0.5757 | 0.2532 | 0.2819 | -1.0 | -1.0 | 0.3089 | 0.4653 | 0.4811 | 0.4811 | -1.0 | -1.0 | 0.5869 | 0.7302 | 0.2946 | 0.5032 | 0.1914 | 0.388 | 0.0565 | 0.3745 | 0.2802 | 0.4096 | | 0.9892 | 56.0 | 5992 | 1.0649 | 0.311 | 0.6027 | 0.2673 | 0.311 | -1.0 | -1.0 | 0.3226 | 0.4954 | 0.515 | 0.515 | -1.0 | -1.0 | 0.629 | 0.7679 | 0.2972 | 0.5556 | 0.198 | 0.3901 | 0.1233 | 0.4182 | 0.3075 | 0.4435 | | 0.98 | 57.0 | 6099 | 1.0413 | 0.3137 | 0.6244 | 0.2912 | 0.3137 | -1.0 | -1.0 | 0.3293 | 0.5021 | 0.5171 | 0.5171 | -1.0 | -1.0 | 0.6288 | 0.7599 | 0.3193 | 0.5698 | 0.2062 | 0.399 | 0.1101 | 0.4236 | 0.3043 | 0.4333 | | 0.9682 | 58.0 | 6206 | 1.0301 | 0.3128 | 0.6003 | 0.2801 | 0.3128 | -1.0 | -1.0 | 0.3278 | 0.4963 | 0.517 | 0.517 | -1.0 | -1.0 | 0.632 | 0.7704 | 0.3038 | 0.5238 | 0.2093 | 0.4203 | 0.1204 | 0.4164 | 0.2984 | 0.4542 | | 0.9533 | 59.0 | 6313 | 1.0755 | 0.2928 | 0.5954 | 0.25 | 0.2928 | -1.0 | -1.0 | 0.3177 | 0.4937 | 0.5084 | 0.5084 | -1.0 | -1.0 | 0.6102 | 0.7401 | 0.2906 | 0.5635 | 0.1942 | 0.399 | 0.1002 | 0.4218 | 0.2688 | 0.4175 | | 0.9589 | 60.0 | 6420 | 1.0364 | 0.3213 | 0.6182 | 0.2717 | 0.3213 | -1.0 | -1.0 | 0.3383 | 0.5121 | 0.5273 | 0.5273 | -1.0 | -1.0 | 0.622 | 0.7617 | 0.3318 | 0.5683 | 0.2273 | 0.4193 | 0.1243 | 0.4236 | 0.301 | 0.4638 | | 0.9398 | 61.0 | 6527 | 1.0653 | 0.3089 | 0.6099 | 0.2673 | 0.3089 | -1.0 | -1.0 | 0.3325 | 0.4946 | 0.5046 | 0.5046 | -1.0 | -1.0 | 0.6166 | 0.7556 | 0.3144 | 0.5286 | 0.177 | 0.376 | 0.1451 | 0.4127 | 0.2915 | 0.4503 | | 0.924 | 62.0 | 6634 | 1.0409 | 0.319 | 0.6335 | 0.2641 | 0.319 | -1.0 | -1.0 | 0.3437 | 0.5085 | 0.5234 | 0.5234 | -1.0 | -1.0 | 0.6223 | 0.7537 | 0.3263 | 0.5476 | 0.21 | 0.4021 | 0.1269 | 0.4582 | 0.3093 | 0.4554 | | 0.924 | 63.0 | 6741 | 1.0517 | 0.3212 | 0.6485 | 0.2727 | 0.3212 | -1.0 | -1.0 | 0.3382 | 0.5109 | 0.5232 | 0.5232 | -1.0 | -1.0 | 0.6392 | 0.7704 | 0.3195 | 0.5857 | 0.1967 | 0.399 | 0.1268 | 0.4218 | 0.3241 | 0.439 | | 0.9458 | 64.0 | 6848 | 1.0314 | 0.3213 | 0.6357 | 0.28 | 0.3213 | -1.0 | -1.0 | 0.3414 | 0.5152 | 0.5278 | 0.5278 | -1.0 | -1.0 | 0.6272 | 0.7648 | 0.3137 | 0.573 | 0.227 | 0.4078 | 0.1257 | 0.4545 | 0.3129 | 0.439 | | 0.9136 | 65.0 | 6955 | 1.0237 | 0.3262 | 0.6377 | 0.284 | 0.3262 | -1.0 | -1.0 | 0.3489 | 0.5157 | 0.5299 | 0.5299 | -1.0 | -1.0 | 0.6277 | 0.766 | 0.3294 | 0.5587 | 0.2223 | 0.3958 | 0.13 | 0.4655 | 0.3216 | 0.4633 | | 0.8969 | 66.0 | 7062 | 1.0190 | 0.3188 | 0.6478 | 0.2833 | 0.3188 | -1.0 | -1.0 | 0.3456 | 0.5153 | 0.528 | 0.528 | -1.0 | -1.0 | 0.6254 | 0.7704 | 0.3338 | 0.5762 | 0.187 | 0.3859 | 0.1258 | 0.4582 | 0.322 | 0.4492 | | 0.8917 | 67.0 | 7169 | 0.9900 | 0.326 | 0.6304 | 0.281 | 0.326 | -1.0 | -1.0 | 0.354 | 0.5289 | 0.5382 | 0.5382 | -1.0 | -1.0 | 0.6478 | 0.7759 | 0.3118 | 0.5556 | 0.2245 | 0.4115 | 0.1149 | 0.4691 | 0.3312 | 0.4791 | | 0.8724 | 68.0 | 7276 | 1.0181 | 0.3141 | 0.6119 | 0.2757 | 0.3141 | -1.0 | -1.0 | 0.3398 | 0.517 | 0.5303 | 0.5303 | -1.0 | -1.0 | 0.642 | 0.7716 | 0.3118 | 0.5778 | 0.2091 | 0.4177 | 0.096 | 0.4255 | 0.3116 | 0.4588 | | 0.8881 | 69.0 | 7383 | 1.0231 | 0.3119 | 0.63 | 0.2566 | 0.3119 | -1.0 | -1.0 | 0.3438 | 0.507 | 0.5148 | 0.5148 | -1.0 | -1.0 | 0.6278 | 0.7556 | 0.3062 | 0.5476 | 0.2077 | 0.4052 | 0.0951 | 0.4073 | 0.3228 | 0.4582 | | 0.8604 | 70.0 | 7490 | 1.0245 | 0.3266 | 0.6436 | 0.2621 | 0.3266 | -1.0 | -1.0 | 0.3448 | 0.5179 | 0.5273 | 0.5273 | -1.0 | -1.0 | 0.6251 | 0.7623 | 0.3301 | 0.5889 | 0.2223 | 0.4115 | 0.1406 | 0.4255 | 0.315 | 0.4486 | | 0.86 | 71.0 | 7597 | 1.0249 | 0.3226 | 0.6426 | 0.2709 | 0.3226 | -1.0 | -1.0 | 0.3442 | 0.5201 | 0.5325 | 0.5325 | -1.0 | -1.0 | 0.6353 | 0.7716 | 0.3171 | 0.6032 | 0.2233 | 0.4177 | 0.1289 | 0.4236 | 0.3082 | 0.4463 | | 0.8452 | 72.0 | 7704 | 1.0383 | 0.3225 | 0.6364 | 0.2629 | 0.3225 | -1.0 | -1.0 | 0.344 | 0.5124 | 0.5223 | 0.5223 | -1.0 | -1.0 | 0.6375 | 0.7728 | 0.322 | 0.5794 | 0.2082 | 0.399 | 0.1443 | 0.4291 | 0.3003 | 0.4311 | | 0.8444 | 73.0 | 7811 | 1.0018 | 0.3312 | 0.6454 | 0.2914 | 0.3312 | -1.0 | -1.0 | 0.3558 | 0.5223 | 0.535 | 0.535 | -1.0 | -1.0 | 0.6395 | 0.771 | 0.3471 | 0.5841 | 0.2158 | 0.4182 | 0.1433 | 0.4345 | 0.3105 | 0.4672 | | 0.8325 | 74.0 | 7918 | 1.0412 | 0.3296 | 0.6574 | 0.272 | 0.3296 | -1.0 | -1.0 | 0.3404 | 0.5203 | 0.5312 | 0.5312 | -1.0 | -1.0 | 0.6449 | 0.7722 | 0.3422 | 0.6032 | 0.2302 | 0.4125 | 0.1352 | 0.4509 | 0.2956 | 0.4169 | | 0.8438 | 75.0 | 8025 | 1.0015 | 0.34 | 0.6482 | 0.3027 | 0.34 | -1.0 | -1.0 | 0.3487 | 0.5211 | 0.5333 | 0.5333 | -1.0 | -1.0 | 0.6449 | 0.7691 | 0.3555 | 0.5968 | 0.2324 | 0.4172 | 0.1427 | 0.4382 | 0.3248 | 0.4452 | | 0.8197 | 76.0 | 8132 | 1.0196 | 0.3426 | 0.6544 | 0.2931 | 0.3426 | -1.0 | -1.0 | 0.356 | 0.5234 | 0.5347 | 0.5347 | -1.0 | -1.0 | 0.6443 | 0.7722 | 0.35 | 0.5794 | 0.2465 | 0.4172 | 0.1434 | 0.4455 | 0.3288 | 0.4593 | | 0.8241 | 77.0 | 8239 | 1.0215 | 0.339 | 0.6531 | 0.3012 | 0.339 | -1.0 | -1.0 | 0.3523 | 0.5162 | 0.5279 | 0.5279 | -1.0 | -1.0 | 0.6411 | 0.771 | 0.3486 | 0.5921 | 0.2324 | 0.4073 | 0.1375 | 0.4091 | 0.3357 | 0.4599 | | 0.8019 | 78.0 | 8346 | 1.0356 | 0.3265 | 0.6279 | 0.2799 | 0.3265 | -1.0 | -1.0 | 0.3503 | 0.5169 | 0.5264 | 0.5264 | -1.0 | -1.0 | 0.639 | 0.771 | 0.3313 | 0.5762 | 0.2315 | 0.413 | 0.1144 | 0.4127 | 0.3162 | 0.4593 | | 0.8113 | 79.0 | 8453 | 1.0087 | 0.3377 | 0.6556 | 0.3059 | 0.3377 | -1.0 | -1.0 | 0.3487 | 0.5269 | 0.5369 | 0.5369 | -1.0 | -1.0 | 0.6396 | 0.7765 | 0.3392 | 0.5873 | 0.2336 | 0.4068 | 0.1513 | 0.4564 | 0.3249 | 0.4576 | | 0.8043 | 80.0 | 8560 | 1.0225 | 0.3351 | 0.6526 | 0.3035 | 0.3351 | -1.0 | -1.0 | 0.3485 | 0.5177 | 0.5285 | 0.5285 | -1.0 | -1.0 | 0.6529 | 0.7827 | 0.331 | 0.5778 | 0.2224 | 0.399 | 0.1421 | 0.4255 | 0.3273 | 0.4576 | | 0.7909 | 81.0 | 8667 | 1.0009 | 0.345 | 0.6563 | 0.3086 | 0.345 | -1.0 | -1.0 | 0.3512 | 0.522 | 0.5359 | 0.5359 | -1.0 | -1.0 | 0.6649 | 0.7877 | 0.3589 | 0.5714 | 0.2366 | 0.4219 | 0.1374 | 0.4418 | 0.3272 | 0.4565 | | 0.8034 | 82.0 | 8774 | 1.0006 | 0.333 | 0.6455 | 0.295 | 0.333 | -1.0 | -1.0 | 0.3529 | 0.5257 | 0.5369 | 0.5369 | -1.0 | -1.0 | 0.6387 | 0.7741 | 0.3352 | 0.5873 | 0.2342 | 0.4208 | 0.1362 | 0.4491 | 0.3206 | 0.4531 | | 0.7744 | 83.0 | 8881 | 0.9946 | 0.341 | 0.6513 | 0.3182 | 0.341 | -1.0 | -1.0 | 0.3615 | 0.5273 | 0.5381 | 0.5381 | -1.0 | -1.0 | 0.6425 | 0.7778 | 0.3636 | 0.5873 | 0.2404 | 0.4198 | 0.1246 | 0.4418 | 0.3341 | 0.4638 | | 0.7769 | 84.0 | 8988 | 0.9994 | 0.3406 | 0.6523 | 0.3009 | 0.3406 | -1.0 | -1.0 | 0.3637 | 0.5304 | 0.5384 | 0.5384 | -1.0 | -1.0 | 0.6478 | 0.7728 | 0.3472 | 0.5968 | 0.2355 | 0.4198 | 0.1444 | 0.4382 | 0.3281 | 0.4644 | | 0.7679 | 85.0 | 9095 | 1.0170 | 0.3397 | 0.6549 | 0.2888 | 0.3397 | -1.0 | -1.0 | 0.3624 | 0.5301 | 0.5407 | 0.5407 | -1.0 | -1.0 | 0.6485 | 0.7685 | 0.3431 | 0.6079 | 0.2294 | 0.4167 | 0.1607 | 0.46 | 0.317 | 0.4503 | | 0.772 | 86.0 | 9202 | 0.9953 | 0.3515 | 0.6722 | 0.3099 | 0.3515 | -1.0 | -1.0 | 0.3674 | 0.5331 | 0.543 | 0.543 | -1.0 | -1.0 | 0.6599 | 0.7778 | 0.3692 | 0.6032 | 0.24 | 0.4187 | 0.1523 | 0.46 | 0.3361 | 0.4554 | | 0.783 | 87.0 | 9309 | 1.0003 | 0.3401 | 0.6545 | 0.2943 | 0.3401 | -1.0 | -1.0 | 0.3603 | 0.5309 | 0.5457 | 0.5457 | -1.0 | -1.0 | 0.651 | 0.7759 | 0.3521 | 0.6222 | 0.2296 | 0.4161 | 0.1433 | 0.46 | 0.3246 | 0.4542 | | 0.7508 | 88.0 | 9416 | 0.9849 | 0.3517 | 0.6734 | 0.3217 | 0.3517 | -1.0 | -1.0 | 0.362 | 0.5333 | 0.5447 | 0.5447 | -1.0 | -1.0 | 0.6572 | 0.7747 | 0.369 | 0.5984 | 0.2439 | 0.4318 | 0.1503 | 0.4527 | 0.338 | 0.4661 | | 0.7558 | 89.0 | 9523 | 0.9861 | 0.3517 | 0.6749 | 0.3173 | 0.3517 | -1.0 | -1.0 | 0.3628 | 0.5329 | 0.5408 | 0.5408 | -1.0 | -1.0 | 0.6469 | 0.7728 | 0.3661 | 0.5889 | 0.2323 | 0.4104 | 0.1747 | 0.4709 | 0.3385 | 0.461 | | 0.7398 | 90.0 | 9630 | 0.9966 | 0.3565 | 0.6784 | 0.315 | 0.3565 | -1.0 | -1.0 | 0.3621 | 0.5367 | 0.5484 | 0.5484 | -1.0 | -1.0 | 0.6524 | 0.7796 | 0.3655 | 0.6127 | 0.2374 | 0.4151 | 0.1769 | 0.4655 | 0.3503 | 0.4689 | | 0.7532 | 91.0 | 9737 | 0.9883 | 0.354 | 0.6709 | 0.3179 | 0.354 | -1.0 | -1.0 | 0.3623 | 0.5395 | 0.5475 | 0.5475 | -1.0 | -1.0 | 0.6551 | 0.779 | 0.3669 | 0.6175 | 0.2432 | 0.4187 | 0.1545 | 0.4564 | 0.3502 | 0.4661 | | 0.7417 | 92.0 | 9844 | 0.9932 | 0.3557 | 0.6777 | 0.3131 | 0.3557 | -1.0 | -1.0 | 0.3629 | 0.5361 | 0.5465 | 0.5465 | -1.0 | -1.0 | 0.6566 | 0.7765 | 0.3713 | 0.6143 | 0.2425 | 0.4224 | 0.163 | 0.4491 | 0.3451 | 0.4701 | | 0.7554 | 93.0 | 9951 | 0.9921 | 0.3598 | 0.6841 | 0.3212 | 0.3598 | -1.0 | -1.0 | 0.3643 | 0.5375 | 0.5466 | 0.5466 | -1.0 | -1.0 | 0.6591 | 0.7821 | 0.3696 | 0.5952 | 0.2482 | 0.4214 | 0.175 | 0.4691 | 0.3469 | 0.465 | | 0.7339 | 94.0 | 10058 | 1.0049 | 0.3587 | 0.6838 | 0.3184 | 0.3587 | -1.0 | -1.0 | 0.3668 | 0.5347 | 0.5447 | 0.5447 | -1.0 | -1.0 | 0.658 | 0.7753 | 0.3767 | 0.6048 | 0.2434 | 0.4177 | 0.1744 | 0.46 | 0.3411 | 0.4655 | | 0.7184 | 95.0 | 10165 | 0.9969 | 0.3547 | 0.6739 | 0.3214 | 0.3547 | -1.0 | -1.0 | 0.3633 | 0.5353 | 0.5449 | 0.5449 | -1.0 | -1.0 | 0.6521 | 0.7722 | 0.369 | 0.6079 | 0.2402 | 0.413 | 0.1707 | 0.4673 | 0.3412 | 0.4638 | | 0.7448 | 96.0 | 10272 | 0.9925 | 0.3556 | 0.6708 | 0.3061 | 0.3556 | -1.0 | -1.0 | 0.365 | 0.5327 | 0.544 | 0.544 | -1.0 | -1.0 | 0.6517 | 0.7765 | 0.3707 | 0.6016 | 0.2354 | 0.4208 | 0.1781 | 0.4527 | 0.3421 | 0.4684 | | 0.7264 | 97.0 | 10379 | 0.9914 | 0.3576 | 0.6793 | 0.315 | 0.3576 | -1.0 | -1.0 | 0.3657 | 0.5332 | 0.5439 | 0.5439 | -1.0 | -1.0 | 0.6517 | 0.7753 | 0.378 | 0.6 | 0.2464 | 0.426 | 0.1707 | 0.4545 | 0.3414 | 0.4638 | | 0.7455 | 98.0 | 10486 | 0.9829 | 0.3586 | 0.6794 | 0.3132 | 0.3586 | -1.0 | -1.0 | 0.367 | 0.5353 | 0.5473 | 0.5473 | -1.0 | -1.0 | 0.6586 | 0.7772 | 0.3781 | 0.6079 | 0.2468 | 0.4266 | 0.1742 | 0.4582 | 0.3355 | 0.4667 | | 0.7105 | 99.0 | 10593 | 0.9862 | 0.3577 | 0.6795 | 0.3089 | 0.3577 | -1.0 | -1.0 | 0.3649 | 0.5352 | 0.5476 | 0.5476 | -1.0 | -1.0 | 0.6591 | 0.7778 | 0.3712 | 0.6032 | 0.2481 | 0.4271 | 0.1727 | 0.4655 | 0.3376 | 0.4644 | | 0.7089 | 100.0 | 10700 | 0.9865 | 0.3578 | 0.6781 | 0.3105 | 0.3578 | -1.0 | -1.0 | 0.365 | 0.535 | 0.5483 | 0.5483 | -1.0 | -1.0 | 0.6584 | 0.7772 | 0.3691 | 0.6063 | 0.2477 | 0.4266 | 0.1766 | 0.4655 | 0.3371 | 0.4661 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

mali17361/detr-finetuned-table-v1

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]

ChingizA/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7117 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.0345 | 0.08 | 200 | 1.8447 | | 1.5511 | 0.16 | 400 | 1.4217 | | 1.444 | 0.24 | 600 | 1.3814 | | 1.3746 | 0.32 | 800 | 1.3241 | | 1.2361 | 0.4 | 1000 | 1.2589 | | 1.3506 | 0.48 | 1200 | 1.2441 | | 1.2833 | 0.56 | 1400 | 1.2052 | | 1.1051 | 0.64 | 1600 | 1.0607 | | 1.1091 | 0.72 | 1800 | 1.0610 | | 1.0295 | 0.8 | 2000 | 1.0241 | | 1.1376 | 0.88 | 2200 | 1.0846 | | 1.1172 | 0.96 | 2400 | 1.1095 | | 1.0186 | 1.04 | 2600 | 0.9978 | | 1.0775 | 1.12 | 2800 | 1.0225 | | 0.9973 | 1.2 | 3000 | 0.9934 | | 1.006 | 1.28 | 3200 | 0.9886 | | 0.9814 | 1.36 | 3400 | 0.9256 | | 1.0253 | 1.44 | 3600 | 0.9209 | | 0.9932 | 1.52 | 3800 | 0.9159 | | 0.9307 | 1.6 | 4000 | 0.9058 | | 0.9103 | 1.68 | 4200 | 0.9049 | | 0.9034 | 1.76 | 4400 | 0.8643 | | 0.9544 | 1.84 | 4600 | 0.9114 | | 0.889 | 1.92 | 4800 | 0.8880 | | 0.8888 | 2.0 | 5000 | 0.8515 | | 0.8877 | 2.08 | 5200 | 0.8707 | | 0.8799 | 2.16 | 5400 | 0.8458 | | 0.8398 | 2.24 | 5600 | 0.8292 | | 0.8181 | 2.32 | 5800 | 0.8226 | | 0.8876 | 2.4 | 6000 | 0.8021 | | 0.8893 | 2.48 | 6200 | 0.8173 | | 0.8497 | 2.56 | 6400 | 0.7870 | | 0.8369 | 2.64 | 6600 | 0.7719 | | 0.8213 | 2.72 | 6800 | 0.7877 | | 0.8044 | 2.8 | 7000 | 0.7763 | | 0.8087 | 2.88 | 7200 | 0.7702 | | 0.7616 | 2.96 | 7400 | 0.7570 | | 0.7901 | 3.04 | 7600 | 0.7451 | | 0.8454 | 3.12 | 7800 | 0.7560 | | 0.7428 | 3.2 | 8000 | 0.7455 | | 0.822 | 3.28 | 8200 | 0.7390 | | 0.8293 | 3.36 | 8400 | 0.7324 | | 0.7196 | 3.44 | 8600 | 0.7270 | | 0.7508 | 3.52 | 8800 | 0.7357 | | 0.783 | 3.6 | 9000 | 0.7293 | | 0.7094 | 3.68 | 9200 | 0.7276 | | 0.7811 | 3.76 | 9400 | 0.7178 | | 0.7765 | 3.84 | 9600 | 0.7129 | | 0.7542 | 3.92 | 9800 | 0.7165 | | 0.756 | 4.0 | 10000 | 0.7117 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

mali17361/detr-finetuned-balloon-v1

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]

Ldicet/detr-jan

# detr-jan This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "zs", "znr", "zst" ]

sekhharr/detr_finetuned_v10_last_checkpoint

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]

qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5

# microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Map: 0.3131 - Map 50: 0.6268 - Map 75: 0.2698 - Map Small: 0.2089 - Map Medium: 0.2237 - Map Large: 0.5358 - Mar 1: 0.3352 - Mar 10: 0.4586 - Mar 100: 0.4682 - Mar Small: 0.2773 - Mar Medium: 0.3748 - Mar Large: 0.6417 - Map Coverall: 0.5362 - Mar 100 Coverall: 0.6844 - Map Face Shield: 0.3199 - Mar 100 Face Shield: 0.4635 - Map Gloves: 0.1628 - Mar 100 Gloves: 0.3176 - Map Goggles: 0.2427 - Mar 100 Goggles: 0.4475 - Map Mask: 0.3038 - Mar 100 Mask: 0.4278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 13.6734 | 1.0 | 107 | 3.0782 | 0.0022 | 0.009 | 0.0004 | 0.0002 | 0.0002 | 0.0027 | 0.0054 | 0.0291 | 0.0632 | 0.0028 | 0.0108 | 0.0955 | 0.0105 | 0.2595 | 0.0 | 0.0 | 0.0 | 0.0126 | 0.0 | 0.0 | 0.0005 | 0.0438 | | 2.9712 | 2.0 | 214 | 2.7340 | 0.0089 | 0.0267 | 0.0034 | 0.0036 | 0.0017 | 0.0114 | 0.0186 | 0.0749 | 0.1162 | 0.0082 | 0.0358 | 0.1266 | 0.0374 | 0.4457 | 0.0 | 0.0 | 0.0004 | 0.0101 | 0.0 | 0.0 | 0.0069 | 0.1253 | | 2.6599 | 3.0 | 321 | 2.6057 | 0.0122 | 0.0432 | 0.0034 | 0.0011 | 0.003 | 0.0138 | 0.0281 | 0.1003 | 0.1355 | 0.0324 | 0.0542 | 0.1526 | 0.0538 | 0.4428 | 0.0004 | 0.0288 | 0.0003 | 0.0322 | 0.0 | 0.0049 | 0.0064 | 0.1686 | | 2.53 | 4.0 | 428 | 2.3681 | 0.0187 | 0.0542 | 0.0099 | 0.0061 | 0.0057 | 0.0372 | 0.0494 | 0.1502 | 0.1831 | 0.0272 | 0.1134 | 0.1631 | 0.0737 | 0.5191 | 0.0032 | 0.1 | 0.0012 | 0.0487 | 0.0058 | 0.0656 | 0.0097 | 0.182 | | 2.3495 | 5.0 | 535 | 2.2947 | 0.0282 | 0.0843 | 0.0163 | 0.0163 | 0.0175 | 0.0387 | 0.0796 | 0.194 | 0.2282 | 0.0407 | 0.1538 | 0.2516 | 0.0928 | 0.5798 | 0.015 | 0.1577 | 0.0009 | 0.0563 | 0.0131 | 0.123 | 0.0193 | 0.2242 | | 2.2478 | 6.0 | 642 | 2.2016 | 0.0346 | 0.0914 | 0.0186 | 0.0079 | 0.0116 | 0.0668 | 0.0938 | 0.2406 | 0.2729 | 0.0362 | 0.1578 | 0.4043 | 0.1208 | 0.6121 | 0.0178 | 0.2577 | 0.0021 | 0.0925 | 0.0152 | 0.2033 | 0.017 | 0.199 | | 2.1515 | 7.0 | 749 | 2.1026 | 0.0424 | 0.1121 | 0.0226 | 0.0162 | 0.0197 | 0.0747 | 0.1058 | 0.2613 | 0.2973 | 0.0996 | 0.2269 | 0.3484 | 0.1382 | 0.5769 | 0.0236 | 0.3 | 0.0052 | 0.1216 | 0.0125 | 0.2246 | 0.0327 | 0.2634 | | 2.0571 | 8.0 | 856 | 2.1115 | 0.0485 | 0.1183 | 0.0346 | 0.0078 | 0.0287 | 0.0922 | 0.1332 | 0.2587 | 0.2995 | 0.0573 | 0.1973 | 0.424 | 0.1446 | 0.6318 | 0.0442 | 0.2808 | 0.0027 | 0.1211 | 0.0182 | 0.1836 | 0.0327 | 0.2804 | | 1.9981 | 9.0 | 963 | 2.0429 | 0.0433 | 0.1161 | 0.0242 | 0.0239 | 0.0216 | 0.0831 | 0.1163 | 0.2543 | 0.2985 | 0.1208 | 0.1892 | 0.4359 | 0.1403 | 0.6358 | 0.0251 | 0.2365 | 0.0059 | 0.1673 | 0.0187 | 0.1754 | 0.0265 | 0.2773 | | 1.9338 | 10.0 | 1070 | 1.9025 | 0.079 | 0.1694 | 0.061 | 0.0175 | 0.0481 | 0.1245 | 0.1621 | 0.313 | 0.3524 | 0.1106 | 0.2565 | 0.5211 | 0.2324 | 0.6809 | 0.0753 | 0.3269 | 0.0094 | 0.198 | 0.0112 | 0.2328 | 0.0667 | 0.3232 | | 1.8745 | 11.0 | 1177 | 1.8462 | 0.0938 | 0.2008 | 0.0772 | 0.0191 | 0.0666 | 0.1644 | 0.1632 | 0.3078 | 0.3485 | 0.1358 | 0.2725 | 0.483 | 0.3093 | 0.6618 | 0.0471 | 0.3442 | 0.0104 | 0.2005 | 0.0122 | 0.2033 | 0.0899 | 0.3325 | | 1.7669 | 12.0 | 1284 | 1.7660 | 0.12 | 0.271 | 0.0939 | 0.0298 | 0.0714 | 0.2035 | 0.1928 | 0.3391 | 0.366 | 0.1539 | 0.2852 | 0.5395 | 0.368 | 0.6566 | 0.0741 | 0.3327 | 0.0236 | 0.2442 | 0.0363 | 0.2459 | 0.0981 | 0.3505 | | 1.7122 | 13.0 | 1391 | 1.7355 | 0.1376 | 0.2863 | 0.1139 | 0.027 | 0.0899 | 0.2379 | 0.2151 | 0.3567 | 0.3878 | 0.1285 | 0.2998 | 0.5798 | 0.3958 | 0.637 | 0.0969 | 0.3712 | 0.0389 | 0.2548 | 0.0271 | 0.3115 | 0.1294 | 0.3644 | | 1.6336 | 14.0 | 1498 | 1.6695 | 0.1609 | 0.3374 | 0.1317 | 0.0196 | 0.107 | 0.2757 | 0.2211 | 0.3673 | 0.3964 | 0.1449 | 0.3237 | 0.5801 | 0.4353 | 0.6595 | 0.146 | 0.3923 | 0.034 | 0.2482 | 0.0437 | 0.3361 | 0.1455 | 0.3459 | | 1.5696 | 15.0 | 1605 | 1.6275 | 0.1601 | 0.3509 | 0.127 | 0.0231 | 0.102 | 0.2989 | 0.2181 | 0.3813 | 0.4083 | 0.1688 | 0.3313 | 0.5781 | 0.4323 | 0.6549 | 0.1061 | 0.4038 | 0.0618 | 0.3005 | 0.029 | 0.3246 | 0.1714 | 0.3577 | | 1.5149 | 16.0 | 1712 | 1.5810 | 0.1862 | 0.3984 | 0.1463 | 0.0622 | 0.1143 | 0.3439 | 0.2268 | 0.3819 | 0.4019 | 0.1881 | 0.3158 | 0.6013 | 0.4779 | 0.6624 | 0.1141 | 0.4 | 0.0694 | 0.2668 | 0.0704 | 0.3262 | 0.1991 | 0.3541 | | 1.4732 | 17.0 | 1819 | 1.5458 | 0.1905 | 0.4135 | 0.1472 | 0.0514 | 0.1196 | 0.3237 | 0.2289 | 0.3868 | 0.4171 | 0.167 | 0.3378 | 0.5905 | 0.4746 | 0.6538 | 0.1547 | 0.4519 | 0.0932 | 0.3055 | 0.0488 | 0.3279 | 0.1812 | 0.3464 | | 1.4327 | 18.0 | 1926 | 1.5617 | 0.1887 | 0.4038 | 0.142 | 0.0323 | 0.1178 | 0.3574 | 0.2395 | 0.3855 | 0.4114 | 0.1902 | 0.3236 | 0.589 | 0.4801 | 0.6595 | 0.1518 | 0.4462 | 0.0866 | 0.2829 | 0.0389 | 0.3197 | 0.186 | 0.349 | | 1.3925 | 19.0 | 2033 | 1.5032 | 0.2162 | 0.4531 | 0.1701 | 0.0303 | 0.1292 | 0.3854 | 0.2572 | 0.4077 | 0.4328 | 0.1759 | 0.3377 | 0.6233 | 0.5043 | 0.6734 | 0.169 | 0.4442 | 0.1341 | 0.3221 | 0.0725 | 0.3754 | 0.201 | 0.349 | | 1.3582 | 20.0 | 2140 | 1.4728 | 0.2077 | 0.4357 | 0.1744 | 0.0412 | 0.1414 | 0.3759 | 0.2516 | 0.4017 | 0.4251 | 0.1842 | 0.3402 | 0.6106 | 0.4879 | 0.6607 | 0.1537 | 0.4404 | 0.1159 | 0.2955 | 0.0633 | 0.359 | 0.2178 | 0.3701 | | 1.329 | 21.0 | 2247 | 1.4826 | 0.2062 | 0.4509 | 0.1642 | 0.0325 | 0.1256 | 0.4185 | 0.2559 | 0.3974 | 0.4214 | 0.1371 | 0.3201 | 0.619 | 0.4879 | 0.6584 | 0.146 | 0.4212 | 0.1136 | 0.2698 | 0.0884 | 0.4164 | 0.195 | 0.3412 | | 1.3458 | 22.0 | 2354 | 1.4653 | 0.2244 | 0.4636 | 0.1859 | 0.074 | 0.1501 | 0.4031 | 0.272 | 0.4102 | 0.4276 | 0.1735 | 0.3346 | 0.6213 | 0.5139 | 0.6711 | 0.1848 | 0.4712 | 0.1083 | 0.2769 | 0.0732 | 0.323 | 0.2419 | 0.3959 | | 1.3028 | 23.0 | 2461 | 1.4139 | 0.2289 | 0.4906 | 0.1886 | 0.0572 | 0.1524 | 0.4043 | 0.2721 | 0.4191 | 0.4353 | 0.1771 | 0.3517 | 0.5964 | 0.4964 | 0.6653 | 0.1865 | 0.4481 | 0.1575 | 0.3211 | 0.0746 | 0.3738 | 0.2293 | 0.368 | | 1.2704 | 24.0 | 2568 | 1.4173 | 0.2371 | 0.4973 | 0.199 | 0.0605 | 0.1634 | 0.419 | 0.2752 | 0.4172 | 0.4401 | 0.1776 | 0.3515 | 0.6027 | 0.5033 | 0.6636 | 0.2403 | 0.4596 | 0.1149 | 0.3005 | 0.1009 | 0.4066 | 0.2261 | 0.3701 | | 1.2379 | 25.0 | 2675 | 1.3933 | 0.2608 | 0.5303 | 0.2271 | 0.0541 | 0.188 | 0.4582 | 0.2849 | 0.4189 | 0.4396 | 0.158 | 0.3455 | 0.6343 | 0.5376 | 0.6913 | 0.2509 | 0.4635 | 0.1194 | 0.3065 | 0.1534 | 0.3623 | 0.2428 | 0.3742 | | 1.2257 | 26.0 | 2782 | 1.4209 | 0.2467 | 0.5049 | 0.2078 | 0.0557 | 0.1592 | 0.4446 | 0.2708 | 0.4135 | 0.4292 | 0.1526 | 0.3298 | 0.6245 | 0.5239 | 0.6636 | 0.2282 | 0.4288 | 0.134 | 0.294 | 0.1079 | 0.3902 | 0.2394 | 0.3696 | | 1.1706 | 27.0 | 2889 | 1.3593 | 0.2631 | 0.5273 | 0.2353 | 0.0955 | 0.1891 | 0.4521 | 0.2912 | 0.4196 | 0.4422 | 0.1554 | 0.3475 | 0.6437 | 0.5374 | 0.685 | 0.261 | 0.4692 | 0.1276 | 0.2925 | 0.1269 | 0.3803 | 0.2624 | 0.384 | | 1.1512 | 28.0 | 2996 | 1.3320 | 0.27 | 0.5371 | 0.2171 | 0.0727 | 0.192 | 0.456 | 0.2981 | 0.4398 | 0.4609 | 0.1954 | 0.3663 | 0.6427 | 0.5406 | 0.6971 | 0.2264 | 0.5 | 0.1629 | 0.3131 | 0.1617 | 0.4066 | 0.2584 | 0.3876 | | 1.1511 | 29.0 | 3103 | 1.3592 | 0.2641 | 0.526 | 0.2346 | 0.0526 | 0.1701 | 0.498 | 0.2977 | 0.4249 | 0.4395 | 0.1836 | 0.3263 | 0.6367 | 0.5376 | 0.6717 | 0.2129 | 0.4154 | 0.1414 | 0.291 | 0.1589 | 0.418 | 0.2697 | 0.4015 | | 1.1312 | 30.0 | 3210 | 1.3863 | 0.2582 | 0.5328 | 0.2115 | 0.0667 | 0.1747 | 0.4443 | 0.283 | 0.4188 | 0.4418 | 0.2191 | 0.332 | 0.6478 | 0.5086 | 0.6526 | 0.2387 | 0.4808 | 0.1249 | 0.295 | 0.1609 | 0.3787 | 0.2579 | 0.4021 | | 1.1207 | 31.0 | 3317 | 1.3294 | 0.2813 | 0.5581 | 0.252 | 0.1121 | 0.1988 | 0.4749 | 0.296 | 0.4362 | 0.4568 | 0.2423 | 0.3532 | 0.6468 | 0.5314 | 0.6775 | 0.2837 | 0.4788 | 0.1526 | 0.3131 | 0.1613 | 0.4033 | 0.2777 | 0.4113 | | 1.0856 | 32.0 | 3424 | 1.3426 | 0.274 | 0.565 | 0.2282 | 0.1612 | 0.2023 | 0.4789 | 0.2989 | 0.4375 | 0.4541 | 0.2492 | 0.3497 | 0.6661 | 0.5195 | 0.6844 | 0.2952 | 0.4731 | 0.149 | 0.298 | 0.1551 | 0.4393 | 0.2516 | 0.3758 | | 1.0909 | 33.0 | 3531 | 1.3174 | 0.2868 | 0.5799 | 0.2445 | 0.0956 | 0.2097 | 0.4865 | 0.3119 | 0.4466 | 0.4652 | 0.2553 | 0.3672 | 0.653 | 0.536 | 0.704 | 0.3096 | 0.4788 | 0.1576 | 0.3211 | 0.1758 | 0.441 | 0.2552 | 0.3809 | | 1.0726 | 34.0 | 3638 | 1.3767 | 0.2751 | 0.561 | 0.2242 | 0.0993 | 0.1951 | 0.4811 | 0.2831 | 0.4241 | 0.452 | 0.2247 | 0.3529 | 0.6578 | 0.5375 | 0.6919 | 0.2638 | 0.4692 | 0.1562 | 0.3075 | 0.1456 | 0.3885 | 0.2724 | 0.4026 | | 1.0799 | 35.0 | 3745 | 1.3316 | 0.2691 | 0.5569 | 0.2126 | 0.1108 | 0.1876 | 0.4522 | 0.2913 | 0.4243 | 0.4443 | 0.2217 | 0.3366 | 0.6247 | 0.5257 | 0.6879 | 0.2661 | 0.4712 | 0.1575 | 0.3055 | 0.1659 | 0.3869 | 0.2301 | 0.3701 | | 1.0402 | 36.0 | 3852 | 1.3501 | 0.2735 | 0.5757 | 0.207 | 0.1217 | 0.2057 | 0.4564 | 0.284 | 0.4377 | 0.4568 | 0.2358 | 0.3814 | 0.6465 | 0.5171 | 0.685 | 0.2325 | 0.4481 | 0.1429 | 0.3035 | 0.2158 | 0.4656 | 0.2594 | 0.382 | | 1.0531 | 37.0 | 3959 | 1.3324 | 0.2701 | 0.5336 | 0.2233 | 0.0834 | 0.1907 | 0.4826 | 0.2852 | 0.4439 | 0.4644 | 0.2142 | 0.3754 | 0.6658 | 0.5318 | 0.7052 | 0.2218 | 0.4596 | 0.1431 | 0.2995 | 0.1909 | 0.4639 | 0.2632 | 0.3938 | | 1.028 | 38.0 | 4066 | 1.3593 | 0.2637 | 0.5412 | 0.2147 | 0.0638 | 0.1752 | 0.4642 | 0.2853 | 0.4231 | 0.4438 | 0.1997 | 0.3274 | 0.6417 | 0.5256 | 0.6705 | 0.2526 | 0.4327 | 0.1312 | 0.2854 | 0.1766 | 0.4344 | 0.2324 | 0.3959 | | 1.0211 | 39.0 | 4173 | 1.3359 | 0.2728 | 0.5553 | 0.2294 | 0.1086 | 0.1991 | 0.4663 | 0.3018 | 0.4335 | 0.4525 | 0.2277 | 0.3625 | 0.6463 | 0.5408 | 0.6821 | 0.2664 | 0.4635 | 0.1253 | 0.2995 | 0.1883 | 0.4377 | 0.2433 | 0.3799 | | 0.9986 | 40.0 | 4280 | 1.2900 | 0.2921 | 0.5961 | 0.2449 | 0.1617 | 0.2199 | 0.4851 | 0.3028 | 0.4506 | 0.4659 | 0.2422 | 0.3836 | 0.6408 | 0.546 | 0.696 | 0.2649 | 0.4538 | 0.1729 | 0.3111 | 0.2041 | 0.4574 | 0.2725 | 0.4113 | | 0.9951 | 41.0 | 4387 | 1.3541 | 0.2695 | 0.5709 | 0.2055 | 0.1418 | 0.1857 | 0.4392 | 0.2921 | 0.4326 | 0.4456 | 0.2248 | 0.3602 | 0.602 | 0.5355 | 0.6855 | 0.2612 | 0.4365 | 0.137 | 0.2879 | 0.1907 | 0.4344 | 0.2233 | 0.3835 | | 0.9838 | 42.0 | 4494 | 1.2978 | 0.2868 | 0.5888 | 0.2326 | 0.1262 | 0.209 | 0.5085 | 0.3099 | 0.4478 | 0.463 | 0.2469 | 0.3642 | 0.6518 | 0.5266 | 0.6867 | 0.2904 | 0.4692 | 0.1673 | 0.3291 | 0.2 | 0.4295 | 0.2497 | 0.4005 | | 0.9637 | 43.0 | 4601 | 1.2950 | 0.285 | 0.5769 | 0.2351 | 0.1588 | 0.2034 | 0.4954 | 0.3066 | 0.4479 | 0.463 | 0.2504 | 0.3566 | 0.658 | 0.5326 | 0.689 | 0.2793 | 0.4615 | 0.1678 | 0.3171 | 0.1861 | 0.4262 | 0.2593 | 0.4211 | | 0.9408 | 44.0 | 4708 | 1.3063 | 0.2795 | 0.5601 | 0.2375 | 0.1689 | 0.1905 | 0.5034 | 0.3126 | 0.4407 | 0.4552 | 0.2524 | 0.3763 | 0.6449 | 0.5461 | 0.6884 | 0.2866 | 0.4635 | 0.1291 | 0.2935 | 0.1614 | 0.4262 | 0.2741 | 0.4046 | | 0.9457 | 45.0 | 4815 | 1.2866 | 0.2851 | 0.5794 | 0.2471 | 0.1712 | 0.2011 | 0.5017 | 0.3168 | 0.448 | 0.4642 | 0.2719 | 0.3629 | 0.6464 | 0.544 | 0.6896 | 0.2772 | 0.4692 | 0.1691 | 0.3176 | 0.1561 | 0.4328 | 0.279 | 0.4119 | | 0.9476 | 46.0 | 4922 | 1.3047 | 0.2737 | 0.5779 | 0.2219 | 0.1539 | 0.1932 | 0.4666 | 0.3059 | 0.4356 | 0.449 | 0.2126 | 0.3626 | 0.6348 | 0.5344 | 0.6896 | 0.2537 | 0.4615 | 0.1547 | 0.3055 | 0.1925 | 0.4393 | 0.233 | 0.349 | | 0.9206 | 47.0 | 5029 | 1.2955 | 0.283 | 0.579 | 0.2365 | 0.1437 | 0.1993 | 0.5024 | 0.3048 | 0.4388 | 0.4501 | 0.2412 | 0.3643 | 0.6283 | 0.5501 | 0.6913 | 0.2723 | 0.4096 | 0.1598 | 0.302 | 0.1965 | 0.4689 | 0.2364 | 0.3789 | | 0.9431 | 48.0 | 5136 | 1.2809 | 0.2955 | 0.6039 | 0.2482 | 0.0914 | 0.2308 | 0.5222 | 0.3096 | 0.4442 | 0.4548 | 0.2012 | 0.3786 | 0.6271 | 0.5476 | 0.6855 | 0.3006 | 0.4154 | 0.1713 | 0.3246 | 0.2015 | 0.441 | 0.2567 | 0.4072 | | 0.8965 | 49.0 | 5243 | 1.2968 | 0.2935 | 0.6015 | 0.2566 | 0.1579 | 0.2276 | 0.4831 | 0.3112 | 0.4451 | 0.4623 | 0.2279 | 0.3816 | 0.6389 | 0.5336 | 0.6844 | 0.2907 | 0.4558 | 0.1725 | 0.3317 | 0.1938 | 0.4344 | 0.2771 | 0.4052 | | 0.8636 | 50.0 | 5350 | 1.2918 | 0.2933 | 0.611 | 0.2363 | 0.1728 | 0.2087 | 0.5035 | 0.3236 | 0.4496 | 0.468 | 0.2535 | 0.3745 | 0.6468 | 0.5353 | 0.6786 | 0.2819 | 0.4769 | 0.1849 | 0.3432 | 0.2064 | 0.4475 | 0.2583 | 0.3938 | | 0.8769 | 51.0 | 5457 | 1.2744 | 0.2959 | 0.6161 | 0.2408 | 0.1599 | 0.2291 | 0.4925 | 0.3212 | 0.4461 | 0.4567 | 0.2249 | 0.3782 | 0.6405 | 0.5475 | 0.6942 | 0.289 | 0.4365 | 0.1629 | 0.3191 | 0.2061 | 0.4361 | 0.2737 | 0.3974 | | 0.8591 | 52.0 | 5564 | 1.3174 | 0.2949 | 0.6031 | 0.254 | 0.1589 | 0.2241 | 0.5068 | 0.3164 | 0.4543 | 0.4668 | 0.2307 | 0.3872 | 0.6637 | 0.5382 | 0.6884 | 0.2941 | 0.4596 | 0.1565 | 0.3106 | 0.2212 | 0.4689 | 0.2645 | 0.4067 | | 0.8347 | 53.0 | 5671 | 1.2626 | 0.3071 | 0.6126 | 0.2618 | 0.1107 | 0.228 | 0.5266 | 0.3237 | 0.4523 | 0.4672 | 0.2187 | 0.3786 | 0.6472 | 0.5428 | 0.6809 | 0.3195 | 0.4673 | 0.1497 | 0.3156 | 0.2318 | 0.4377 | 0.2917 | 0.4345 | | 0.8474 | 54.0 | 5778 | 1.2730 | 0.3094 | 0.6174 | 0.2591 | 0.1729 | 0.2387 | 0.5153 | 0.3268 | 0.4591 | 0.4714 | 0.2482 | 0.3975 | 0.645 | 0.5352 | 0.6925 | 0.3235 | 0.4615 | 0.1627 | 0.3151 | 0.2273 | 0.459 | 0.2983 | 0.4289 | | 0.8326 | 55.0 | 5885 | 1.3201 | 0.2977 | 0.6056 | 0.259 | 0.1635 | 0.2296 | 0.4971 | 0.3159 | 0.4505 | 0.4619 | 0.2297 | 0.3789 | 0.6458 | 0.5208 | 0.6769 | 0.3094 | 0.4462 | 0.1661 | 0.3075 | 0.1961 | 0.4508 | 0.2963 | 0.4284 | | 0.8263 | 56.0 | 5992 | 1.2886 | 0.3029 | 0.6093 | 0.2592 | 0.1656 | 0.2243 | 0.5254 | 0.3256 | 0.4529 | 0.471 | 0.2464 | 0.3875 | 0.6623 | 0.5289 | 0.6838 | 0.3448 | 0.4808 | 0.1479 | 0.3095 | 0.2026 | 0.4574 | 0.2906 | 0.4237 | | 0.8222 | 57.0 | 6099 | 1.2601 | 0.3086 | 0.6085 | 0.2789 | 0.1746 | 0.2259 | 0.5201 | 0.3328 | 0.4596 | 0.4718 | 0.2474 | 0.3766 | 0.6718 | 0.5455 | 0.6931 | 0.3148 | 0.4769 | 0.1575 | 0.3181 | 0.2356 | 0.4525 | 0.2895 | 0.4186 | | 0.8328 | 58.0 | 6206 | 1.2603 | 0.3137 | 0.6183 | 0.2922 | 0.1647 | 0.2253 | 0.5418 | 0.3307 | 0.4605 | 0.4712 | 0.243 | 0.3802 | 0.6654 | 0.5519 | 0.6936 | 0.3174 | 0.4615 | 0.1559 | 0.3276 | 0.2486 | 0.4492 | 0.2948 | 0.4242 | | 0.8005 | 59.0 | 6313 | 1.2536 | 0.3103 | 0.6141 | 0.266 | 0.173 | 0.2226 | 0.5386 | 0.3322 | 0.4597 | 0.4703 | 0.2512 | 0.3798 | 0.6546 | 0.5517 | 0.6867 | 0.3166 | 0.4673 | 0.1487 | 0.309 | 0.2489 | 0.4623 | 0.2857 | 0.4263 | | 0.7786 | 60.0 | 6420 | 1.2642 | 0.309 | 0.6275 | 0.2553 | 0.1445 | 0.2286 | 0.5254 | 0.3205 | 0.4492 | 0.4578 | 0.223 | 0.3713 | 0.6501 | 0.5394 | 0.6786 | 0.3146 | 0.4346 | 0.1573 | 0.3126 | 0.2571 | 0.4623 | 0.2764 | 0.401 | | 0.8077 | 61.0 | 6527 | 1.2750 | 0.2959 | 0.6106 | 0.236 | 0.1838 | 0.2134 | 0.5175 | 0.3227 | 0.4463 | 0.4581 | 0.2573 | 0.3555 | 0.662 | 0.5316 | 0.6884 | 0.3034 | 0.4308 | 0.1394 | 0.302 | 0.2304 | 0.459 | 0.2748 | 0.4103 | | 0.7867 | 62.0 | 6634 | 1.2521 | 0.3135 | 0.6255 | 0.2768 | 0.1781 | 0.2267 | 0.5261 | 0.3347 | 0.4584 | 0.4696 | 0.2595 | 0.3671 | 0.6675 | 0.5386 | 0.6925 | 0.3145 | 0.4635 | 0.1656 | 0.3211 | 0.258 | 0.4639 | 0.2908 | 0.4072 | | 0.772 | 63.0 | 6741 | 1.2546 | 0.3072 | 0.6313 | 0.2524 | 0.1802 | 0.2297 | 0.5233 | 0.3295 | 0.4587 | 0.4696 | 0.2715 | 0.3667 | 0.6625 | 0.5381 | 0.6913 | 0.277 | 0.4346 | 0.1792 | 0.3327 | 0.2497 | 0.4787 | 0.2918 | 0.4108 | | 0.7736 | 64.0 | 6848 | 1.2673 | 0.3021 | 0.614 | 0.244 | 0.1813 | 0.2327 | 0.4996 | 0.3305 | 0.4561 | 0.4622 | 0.2509 | 0.3738 | 0.6488 | 0.5308 | 0.6803 | 0.3057 | 0.4423 | 0.1597 | 0.306 | 0.2247 | 0.459 | 0.2895 | 0.4232 | | 0.7561 | 65.0 | 6955 | 1.3063 | 0.2936 | 0.6089 | 0.2321 | 0.1759 | 0.2158 | 0.5179 | 0.3244 | 0.4481 | 0.4604 | 0.2518 | 0.3706 | 0.6391 | 0.519 | 0.6769 | 0.3054 | 0.4558 | 0.1472 | 0.308 | 0.2309 | 0.459 | 0.2653 | 0.4021 | | 0.7316 | 66.0 | 7062 | 1.2575 | 0.315 | 0.6168 | 0.2804 | 0.1995 | 0.2251 | 0.5303 | 0.3363 | 0.464 | 0.4719 | 0.2563 | 0.3883 | 0.6547 | 0.5418 | 0.6913 | 0.3304 | 0.4577 | 0.1497 | 0.3141 | 0.2533 | 0.4738 | 0.3001 | 0.4227 | | 0.746 | 67.0 | 7169 | 1.2713 | 0.3118 | 0.6229 | 0.2625 | 0.1695 | 0.2338 | 0.5371 | 0.3323 | 0.4597 | 0.4707 | 0.2419 | 0.3916 | 0.6594 | 0.5428 | 0.6896 | 0.3193 | 0.4538 | 0.1503 | 0.3166 | 0.2676 | 0.4787 | 0.2791 | 0.4149 | | 0.7539 | 68.0 | 7276 | 1.2705 | 0.3161 | 0.6353 | 0.2591 | 0.1718 | 0.2332 | 0.5451 | 0.3343 | 0.4568 | 0.4702 | 0.2554 | 0.3826 | 0.6569 | 0.5358 | 0.6803 | 0.3318 | 0.4577 | 0.1494 | 0.3146 | 0.2715 | 0.4918 | 0.292 | 0.4067 | | 0.7219 | 69.0 | 7383 | 1.2644 | 0.3175 | 0.6321 | 0.2758 | 0.1924 | 0.2341 | 0.5379 | 0.3404 | 0.4653 | 0.4744 | 0.2457 | 0.3925 | 0.6648 | 0.5442 | 0.6988 | 0.3151 | 0.4712 | 0.1662 | 0.3176 | 0.2727 | 0.4672 | 0.2895 | 0.417 | | 0.7202 | 70.0 | 7490 | 1.2718 | 0.3124 | 0.6233 | 0.2622 | 0.1758 | 0.2246 | 0.5305 | 0.334 | 0.4534 | 0.4602 | 0.2437 | 0.3718 | 0.6419 | 0.547 | 0.6954 | 0.3204 | 0.4327 | 0.16 | 0.3251 | 0.2487 | 0.4361 | 0.2858 | 0.4119 | | 0.7019 | 71.0 | 7597 | 1.2819 | 0.3073 | 0.6104 | 0.2737 | 0.1777 | 0.2295 | 0.5232 | 0.3287 | 0.4558 | 0.4679 | 0.2492 | 0.3812 | 0.6526 | 0.548 | 0.6913 | 0.3097 | 0.4269 | 0.1571 | 0.3176 | 0.2321 | 0.4951 | 0.2894 | 0.4088 | | 0.6951 | 72.0 | 7704 | 1.2853 | 0.3099 | 0.6174 | 0.2613 | 0.1872 | 0.2375 | 0.5177 | 0.3358 | 0.463 | 0.4711 | 0.2495 | 0.3905 | 0.6495 | 0.5493 | 0.6954 | 0.3007 | 0.4462 | 0.1597 | 0.3312 | 0.2477 | 0.4689 | 0.2921 | 0.4139 | | 0.6821 | 73.0 | 7811 | 1.2949 | 0.304 | 0.6157 | 0.2543 | 0.1795 | 0.2194 | 0.5134 | 0.3303 | 0.4579 | 0.4682 | 0.2485 | 0.3667 | 0.6627 | 0.5392 | 0.6884 | 0.3043 | 0.4596 | 0.1457 | 0.3106 | 0.2493 | 0.4803 | 0.2817 | 0.4021 | | 0.6943 | 74.0 | 7918 | 1.2790 | 0.3101 | 0.6194 | 0.2567 | 0.1863 | 0.2366 | 0.52 | 0.3337 | 0.4559 | 0.4647 | 0.2422 | 0.3708 | 0.6517 | 0.5416 | 0.6844 | 0.3093 | 0.4442 | 0.1441 | 0.3005 | 0.2547 | 0.4754 | 0.3007 | 0.4191 | | 0.6786 | 75.0 | 8025 | 1.2823 | 0.3121 | 0.6285 | 0.2576 | 0.1497 | 0.2309 | 0.5135 | 0.3348 | 0.4575 | 0.4686 | 0.2583 | 0.3764 | 0.6485 | 0.5449 | 0.6838 | 0.3174 | 0.4635 | 0.1517 | 0.3111 | 0.2466 | 0.4639 | 0.2997 | 0.4206 | | 0.6606 | 76.0 | 8132 | 1.2780 | 0.3191 | 0.628 | 0.2754 | 0.1958 | 0.2363 | 0.5414 | 0.3399 | 0.4597 | 0.4708 | 0.2592 | 0.3744 | 0.6633 | 0.5547 | 0.7 | 0.316 | 0.4442 | 0.1575 | 0.3111 | 0.2601 | 0.4672 | 0.3071 | 0.4314 | | 0.6585 | 77.0 | 8239 | 1.2696 | 0.3162 | 0.6344 | 0.2645 | 0.1962 | 0.2277 | 0.5346 | 0.3365 | 0.4586 | 0.4682 | 0.2466 | 0.3861 | 0.6505 | 0.544 | 0.6931 | 0.3221 | 0.4462 | 0.1604 | 0.3231 | 0.243 | 0.4541 | 0.3117 | 0.4247 | | 0.6592 | 78.0 | 8346 | 1.2684 | 0.3196 | 0.6245 | 0.2718 | 0.2043 | 0.2306 | 0.5384 | 0.3429 | 0.4657 | 0.4755 | 0.2704 | 0.3934 | 0.6572 | 0.5458 | 0.6902 | 0.3302 | 0.4577 | 0.1748 | 0.3377 | 0.2455 | 0.4672 | 0.3014 | 0.4247 | | 0.6585 | 79.0 | 8453 | 1.2727 | 0.3178 | 0.6234 | 0.2755 | 0.2032 | 0.2309 | 0.5422 | 0.337 | 0.4627 | 0.4718 | 0.2685 | 0.3828 | 0.6604 | 0.5462 | 0.6879 | 0.334 | 0.4538 | 0.1643 | 0.3337 | 0.2447 | 0.4738 | 0.2997 | 0.4098 | | 0.6463 | 80.0 | 8560 | 1.2868 | 0.3123 | 0.6186 | 0.2752 | 0.1921 | 0.2364 | 0.5315 | 0.3377 | 0.4535 | 0.465 | 0.2578 | 0.3804 | 0.6535 | 0.5375 | 0.6879 | 0.333 | 0.4519 | 0.1566 | 0.3201 | 0.2372 | 0.4525 | 0.2973 | 0.4129 | | 0.6458 | 81.0 | 8667 | 1.2962 | 0.3167 | 0.6227 | 0.277 | 0.1767 | 0.2339 | 0.5406 | 0.3376 | 0.4549 | 0.4652 | 0.244 | 0.3766 | 0.6626 | 0.5454 | 0.6948 | 0.3445 | 0.4692 | 0.1444 | 0.3146 | 0.2609 | 0.441 | 0.2885 | 0.4062 | | 0.6207 | 82.0 | 8774 | 1.2957 | 0.3139 | 0.6258 | 0.2759 | 0.202 | 0.2299 | 0.5281 | 0.3389 | 0.4568 | 0.47 | 0.2643 | 0.3793 | 0.6634 | 0.5374 | 0.6896 | 0.3243 | 0.4692 | 0.151 | 0.3246 | 0.258 | 0.4443 | 0.2987 | 0.4222 | | 0.6356 | 83.0 | 8881 | 1.3102 | 0.31 | 0.618 | 0.2765 | 0.1833 | 0.229 | 0.5134 | 0.3317 | 0.4583 | 0.4686 | 0.2588 | 0.394 | 0.642 | 0.5361 | 0.689 | 0.3412 | 0.4769 | 0.1453 | 0.3241 | 0.241 | 0.4443 | 0.2865 | 0.4088 | | 0.6286 | 84.0 | 8988 | 1.2985 | 0.3066 | 0.6197 | 0.2678 | 0.1879 | 0.2187 | 0.524 | 0.3336 | 0.4478 | 0.4549 | 0.2533 | 0.3669 | 0.6431 | 0.5354 | 0.6844 | 0.3222 | 0.4423 | 0.1468 | 0.306 | 0.2344 | 0.4344 | 0.294 | 0.4072 | | 0.6269 | 85.0 | 9095 | 1.2769 | 0.3174 | 0.6259 | 0.2708 | 0.2034 | 0.2266 | 0.5397 | 0.3425 | 0.4593 | 0.4669 | 0.2736 | 0.3741 | 0.6577 | 0.5429 | 0.6919 | 0.3334 | 0.4596 | 0.1592 | 0.3211 | 0.2454 | 0.441 | 0.3061 | 0.4211 | | 0.5998 | 86.0 | 9202 | 1.2841 | 0.317 | 0.616 | 0.2823 | 0.1881 | 0.2347 | 0.538 | 0.3419 | 0.4606 | 0.4711 | 0.2701 | 0.3843 | 0.6488 | 0.5414 | 0.6896 | 0.3243 | 0.4692 | 0.1586 | 0.3256 | 0.2489 | 0.4443 | 0.3116 | 0.4268 | | 0.6054 | 87.0 | 9309 | 1.2951 | 0.3163 | 0.6223 | 0.2731 | 0.1937 | 0.2287 | 0.541 | 0.3368 | 0.4578 | 0.4648 | 0.2732 | 0.3682 | 0.6534 | 0.5436 | 0.6832 | 0.3263 | 0.4577 | 0.1617 | 0.3196 | 0.251 | 0.4377 | 0.2988 | 0.4258 | | 0.6138 | 88.0 | 9416 | 1.2934 | 0.3144 | 0.6109 | 0.2739 | 0.1932 | 0.2315 | 0.5285 | 0.3451 | 0.4584 | 0.4683 | 0.264 | 0.3767 | 0.6479 | 0.5384 | 0.6844 | 0.3286 | 0.4712 | 0.1513 | 0.3131 | 0.2518 | 0.4492 | 0.3019 | 0.4237 | | 0.5887 | 89.0 | 9523 | 1.2940 | 0.3171 | 0.6147 | 0.279 | 0.2023 | 0.2306 | 0.5411 | 0.3377 | 0.4613 | 0.4698 | 0.2773 | 0.3725 | 0.6505 | 0.5397 | 0.689 | 0.336 | 0.4673 | 0.161 | 0.3161 | 0.248 | 0.4541 | 0.3008 | 0.4227 | | 0.6026 | 90.0 | 9630 | 1.3081 | 0.3106 | 0.6154 | 0.2657 | 0.2063 | 0.2143 | 0.5398 | 0.3342 | 0.4543 | 0.4616 | 0.2846 | 0.3601 | 0.6397 | 0.5287 | 0.6769 | 0.3076 | 0.4442 | 0.1675 | 0.3146 | 0.2498 | 0.4426 | 0.2996 | 0.4299 | | 0.6116 | 91.0 | 9737 | 1.3193 | 0.3046 | 0.5974 | 0.2664 | 0.215 | 0.2096 | 0.5344 | 0.3349 | 0.4514 | 0.4594 | 0.2806 | 0.3671 | 0.6362 | 0.5371 | 0.6844 | 0.2908 | 0.4327 | 0.1537 | 0.3121 | 0.2481 | 0.4475 | 0.2931 | 0.4201 | | 0.5874 | 92.0 | 9844 | 1.3148 | 0.307 | 0.5997 | 0.2706 | 0.207 | 0.2168 | 0.5303 | 0.3364 | 0.457 | 0.4653 | 0.2759 | 0.38 | 0.6418 | 0.5305 | 0.6827 | 0.3038 | 0.4519 | 0.1554 | 0.3176 | 0.2488 | 0.4541 | 0.2965 | 0.4201 | | 0.5765 | 93.0 | 9951 | 1.3114 | 0.31 | 0.6063 | 0.2757 | 0.2056 | 0.2172 | 0.5388 | 0.3349 | 0.4549 | 0.4625 | 0.275 | 0.369 | 0.6406 | 0.5308 | 0.6827 | 0.3106 | 0.4538 | 0.1567 | 0.309 | 0.2532 | 0.4459 | 0.2988 | 0.4211 | | 0.5799 | 94.0 | 10058 | 1.3074 | 0.3101 | 0.6134 | 0.2681 | 0.2042 | 0.2135 | 0.5519 | 0.3365 | 0.454 | 0.4634 | 0.272 | 0.3674 | 0.6508 | 0.53 | 0.6821 | 0.3105 | 0.4596 | 0.1525 | 0.3161 | 0.2582 | 0.4377 | 0.2993 | 0.4216 | | 0.5678 | 95.0 | 10165 | 1.3073 | 0.3102 | 0.6184 | 0.2637 | 0.2103 | 0.2233 | 0.537 | 0.3327 | 0.4527 | 0.461 | 0.2775 | 0.3668 | 0.6309 | 0.537 | 0.6884 | 0.3217 | 0.4404 | 0.1558 | 0.3106 | 0.2354 | 0.4459 | 0.3011 | 0.4196 | | 0.5776 | 96.0 | 10272 | 1.3083 | 0.3146 | 0.6177 | 0.2751 | 0.2083 | 0.2193 | 0.5465 | 0.3364 | 0.4548 | 0.4627 | 0.2731 | 0.3681 | 0.6399 | 0.5359 | 0.6861 | 0.3306 | 0.4538 | 0.1566 | 0.3176 | 0.2484 | 0.4361 | 0.3012 | 0.4201 | | 0.5768 | 97.0 | 10379 | 1.3027 | 0.3134 | 0.6204 | 0.2681 | 0.2056 | 0.2203 | 0.5453 | 0.3365 | 0.4545 | 0.4622 | 0.2774 | 0.3652 | 0.6417 | 0.5329 | 0.6832 | 0.3282 | 0.4538 | 0.1591 | 0.3181 | 0.2445 | 0.4328 | 0.3021 | 0.4232 | | 0.5708 | 98.0 | 10486 | 1.3045 | 0.3094 | 0.6223 | 0.2705 | 0.2077 | 0.2196 | 0.5411 | 0.3325 | 0.4544 | 0.4623 | 0.2754 | 0.3668 | 0.641 | 0.5342 | 0.6838 | 0.3201 | 0.4519 | 0.1588 | 0.3181 | 0.2347 | 0.4361 | 0.299 | 0.4216 | | 0.5779 | 99.0 | 10593 | 1.3050 | 0.3104 | 0.6231 | 0.2616 | 0.2049 | 0.2159 | 0.5424 | 0.3324 | 0.455 | 0.4645 | 0.274 | 0.3682 | 0.6457 | 0.536 | 0.6838 | 0.3162 | 0.4615 | 0.1593 | 0.3176 | 0.2376 | 0.4377 | 0.303 | 0.4216 | | 0.5571 | 100.0 | 10700 | 1.3007 | 0.3131 | 0.6268 | 0.2698 | 0.2089 | 0.2237 | 0.5358 | 0.3352 | 0.4586 | 0.4682 | 0.2773 | 0.3748 | 0.6417 | 0.5362 | 0.6844 | 0.3199 | 0.4635 | 0.1628 | 0.3176 | 0.2427 | 0.4475 | 0.3038 | 0.4278 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5

# sensetime-deformable-detr-finetuned-10k-cppe5 This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.1028 - Map: 0.2808 - Map 50: 0.5257 - Map 75: 0.2589 - Map Small: 0.2879 - Map Medium: 0.1953 - Map Large: 0.4546 - Mar 1: 0.3004 - Mar 10: 0.5007 - Mar 100: 0.534 - Mar Small: 0.4339 - Mar Medium: 0.4079 - Mar Large: 0.7629 - Map Coverall: 0.5566 - Mar 100 Coverall: 0.7331 - Map Face Shield: 0.2761 - Mar 100 Face Shield: 0.549 - Map Gloves: 0.1484 - Mar 100 Gloves: 0.3943 - Map Goggles: 0.1271 - Mar 100 Goggles: 0.5085 - Map Mask: 0.2956 - Mar 100 Mask: 0.4853 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 9.1039 | 0.9953 | 106 | 2.6450 | 0.0073 | 0.0229 | 0.0025 | 0.0 | 0.0001 | 0.0088 | 0.0123 | 0.0311 | 0.0381 | 0.0009 | 0.0248 | 0.0329 | 0.0358 | 0.1186 | 0.0001 | 0.0204 | 0.0 | 0.0066 | 0.0002 | 0.0034 | 0.0002 | 0.0417 | | 2.3921 | 2.0 | 213 | 2.4742 | 0.0067 | 0.0207 | 0.0017 | 0.0009 | 0.0009 | 0.008 | 0.0157 | 0.0582 | 0.0923 | 0.0038 | 0.0276 | 0.1516 | 0.0296 | 0.3349 | 0.0011 | 0.0327 | 0.0 | 0.0199 | 0.0016 | 0.0203 | 0.0013 | 0.0536 | | 2.28 | 2.9953 | 319 | 2.5260 | 0.0064 | 0.0198 | 0.0024 | 0.0342 | 0.0005 | 0.0063 | 0.0124 | 0.0547 | 0.0777 | 0.1079 | 0.0291 | 0.0848 | 0.0294 | 0.2849 | 0.0009 | 0.0082 | 0.0 | 0.0057 | 0.0 | 0.0 | 0.0019 | 0.0896 | | 2.1883 | 4.0 | 426 | 2.2302 | 0.0077 | 0.0193 | 0.006 | 0.0013 | 0.001 | 0.0126 | 0.0259 | 0.0653 | 0.1098 | 0.0109 | 0.035 | 0.1264 | 0.0323 | 0.4203 | 0.0 | 0.0 | 0.0 | 0.01 | 0.0023 | 0.0119 | 0.0036 | 0.1066 | | 2.1263 | 4.9953 | 532 | 2.1512 | 0.0105 | 0.0306 | 0.0069 | 0.0021 | 0.0015 | 0.0105 | 0.0323 | 0.0757 | 0.1144 | 0.0199 | 0.0328 | 0.1168 | 0.0467 | 0.45 | 0.0001 | 0.002 | 0.0001 | 0.0175 | 0.0 | 0.0 | 0.0058 | 0.1024 | | 2.0232 | 6.0 | 639 | 2.1045 | 0.0133 | 0.0323 | 0.0103 | 0.0049 | 0.0023 | 0.0143 | 0.0357 | 0.0882 | 0.1265 | 0.0147 | 0.044 | 0.1458 | 0.0575 | 0.4773 | 0.0 | 0.0 | 0.0002 | 0.0147 | 0.0 | 0.0 | 0.0086 | 0.1403 | | 2.0103 | 6.9953 | 745 | 2.1646 | 0.0219 | 0.0466 | 0.0178 | 0.0049 | 0.0015 | 0.0232 | 0.0438 | 0.1018 | 0.1274 | 0.0085 | 0.0471 | 0.1658 | 0.1033 | 0.4698 | 0.0013 | 0.0184 | 0.0001 | 0.0251 | 0.0003 | 0.0136 | 0.0046 | 0.1104 | | 1.9723 | 8.0 | 852 | 1.9693 | 0.021 | 0.052 | 0.0165 | 0.0048 | 0.0036 | 0.0274 | 0.039 | 0.113 | 0.1558 | 0.0241 | 0.053 | 0.2211 | 0.0881 | 0.5407 | 0.0029 | 0.0286 | 0.0001 | 0.0265 | 0.0007 | 0.0119 | 0.013 | 0.1716 | | 1.9425 | 8.9953 | 958 | 1.9505 | 0.0199 | 0.0505 | 0.0148 | 0.003 | 0.0051 | 0.0195 | 0.0387 | 0.1041 | 0.1534 | 0.0233 | 0.0595 | 0.1477 | 0.0826 | 0.564 | 0.0037 | 0.0122 | 0.0001 | 0.0156 | 0.0003 | 0.0068 | 0.0126 | 0.1682 | | 1.8248 | 10.0 | 1065 | 1.9128 | 0.0237 | 0.0603 | 0.0166 | 0.0064 | 0.0079 | 0.0335 | 0.0558 | 0.137 | 0.1739 | 0.0569 | 0.089 | 0.1711 | 0.0872 | 0.5314 | 0.015 | 0.0939 | 0.0002 | 0.019 | 0.0018 | 0.0492 | 0.0143 | 0.1763 | | 1.7923 | 10.9953 | 1171 | 1.8334 | 0.0281 | 0.0628 | 0.0223 | 0.0069 | 0.0081 | 0.0302 | 0.0546 | 0.1543 | 0.204 | 0.0577 | 0.1129 | 0.2174 | 0.1088 | 0.5523 | 0.0103 | 0.0857 | 0.0002 | 0.0265 | 0.0048 | 0.1424 | 0.0163 | 0.2133 | | 1.7922 | 12.0 | 1278 | 1.8679 | 0.0405 | 0.0964 | 0.0281 | 0.0165 | 0.0126 | 0.0444 | 0.0596 | 0.1629 | 0.2031 | 0.0387 | 0.1127 | 0.2102 | 0.1462 | 0.5709 | 0.0299 | 0.1408 | 0.0002 | 0.0213 | 0.0099 | 0.0966 | 0.0163 | 0.1858 | | 1.7797 | 12.9953 | 1384 | 1.7715 | 0.036 | 0.0739 | 0.0337 | 0.0066 | 0.0089 | 0.042 | 0.0647 | 0.1644 | 0.2102 | 0.0472 | 0.1088 | 0.2326 | 0.1429 | 0.5924 | 0.014 | 0.1082 | 0.0006 | 0.0469 | 0.0016 | 0.0797 | 0.0207 | 0.2237 | | 1.6925 | 14.0 | 1491 | 1.7633 | 0.0427 | 0.0897 | 0.044 | 0.0073 | 0.0069 | 0.0564 | 0.061 | 0.163 | 0.2152 | 0.0631 | 0.1133 | 0.2339 | 0.1819 | 0.5988 | 0.0063 | 0.0429 | 0.0007 | 0.0493 | 0.0069 | 0.1695 | 0.0175 | 0.2156 | | 1.6927 | 14.9953 | 1597 | 1.7455 | 0.0429 | 0.0973 | 0.0311 | 0.0081 | 0.0139 | 0.0464 | 0.0718 | 0.196 | 0.243 | 0.059 | 0.1603 | 0.2494 | 0.1594 | 0.5959 | 0.0283 | 0.1592 | 0.0008 | 0.0559 | 0.0069 | 0.1627 | 0.0191 | 0.2412 | | 1.6409 | 16.0 | 1704 | 1.6922 | 0.0414 | 0.1001 | 0.0295 | 0.0186 | 0.0152 | 0.0447 | 0.0612 | 0.203 | 0.2566 | 0.0805 | 0.1605 | 0.3192 | 0.1449 | 0.5965 | 0.0235 | 0.151 | 0.0012 | 0.073 | 0.0108 | 0.2254 | 0.0264 | 0.237 | | 1.6535 | 16.9953 | 1810 | 1.6837 | 0.0508 | 0.0973 | 0.0468 | 0.011 | 0.021 | 0.0457 | 0.088 | 0.2063 | 0.2565 | 0.0518 | 0.1584 | 0.2943 | 0.1717 | 0.6256 | 0.0426 | 0.1694 | 0.0007 | 0.0507 | 0.0081 | 0.1712 | 0.0307 | 0.2654 | | 1.5853 | 18.0 | 1917 | 1.6565 | 0.0424 | 0.0842 | 0.0379 | 0.0103 | 0.0177 | 0.0497 | 0.0954 | 0.2317 | 0.2809 | 0.0502 | 0.1839 | 0.3685 | 0.1457 | 0.639 | 0.0169 | 0.1673 | 0.002 | 0.0986 | 0.0155 | 0.2339 | 0.0318 | 0.2659 | | 1.5743 | 18.9953 | 2023 | 1.6498 | 0.0409 | 0.0877 | 0.0367 | 0.0989 | 0.0149 | 0.0487 | 0.1 | 0.2277 | 0.2841 | 0.2421 | 0.1812 | 0.3256 | 0.1417 | 0.6535 | 0.0225 | 0.2286 | 0.0025 | 0.1052 | 0.0073 | 0.1407 | 0.0306 | 0.2924 | | 1.5241 | 20.0 | 2130 | 1.6974 | 0.0496 | 0.1014 | 0.0484 | 0.0401 | 0.0168 | 0.0579 | 0.099 | 0.2179 | 0.2668 | 0.0939 | 0.1701 | 0.3058 | 0.179 | 0.632 | 0.0278 | 0.2531 | 0.003 | 0.0825 | 0.0096 | 0.1525 | 0.0283 | 0.2137 | | 1.5431 | 20.9953 | 2236 | 1.5908 | 0.0584 | 0.1168 | 0.0529 | 0.098 | 0.0376 | 0.0581 | 0.1182 | 0.2721 | 0.3191 | 0.15 | 0.2129 | 0.3986 | 0.1837 | 0.6558 | 0.0505 | 0.3224 | 0.0039 | 0.1005 | 0.0151 | 0.2508 | 0.0388 | 0.2659 | | 1.5106 | 22.0 | 2343 | 1.6095 | 0.0593 | 0.1366 | 0.0445 | 0.0125 | 0.0284 | 0.0639 | 0.1034 | 0.2407 | 0.2845 | 0.063 | 0.1819 | 0.3299 | 0.1892 | 0.6238 | 0.0521 | 0.2327 | 0.006 | 0.1118 | 0.0147 | 0.1864 | 0.0345 | 0.2678 | | 1.5372 | 22.9953 | 2449 | 1.5951 | 0.0636 | 0.1316 | 0.0576 | 0.0282 | 0.0416 | 0.0537 | 0.1163 | 0.266 | 0.312 | 0.0853 | 0.2239 | 0.3533 | 0.1781 | 0.6052 | 0.0692 | 0.3286 | 0.008 | 0.1393 | 0.0153 | 0.1983 | 0.0475 | 0.2886 | | 1.4386 | 24.0 | 2556 | 1.5187 | 0.0622 | 0.123 | 0.0587 | 0.0185 | 0.0405 | 0.0661 | 0.1229 | 0.2786 | 0.3385 | 0.0898 | 0.2326 | 0.4092 | 0.1881 | 0.6599 | 0.0659 | 0.351 | 0.0061 | 0.1332 | 0.0147 | 0.2407 | 0.0362 | 0.3076 | | 1.4267 | 24.9953 | 2662 | 1.5143 | 0.0664 | 0.1359 | 0.0569 | 0.0405 | 0.0475 | 0.0664 | 0.1284 | 0.2935 | 0.3396 | 0.0994 | 0.2523 | 0.3919 | 0.1845 | 0.6512 | 0.0576 | 0.3204 | 0.0088 | 0.1393 | 0.0233 | 0.2508 | 0.0576 | 0.3365 | | 1.397 | 26.0 | 2769 | 1.5084 | 0.0689 | 0.1464 | 0.0617 | 0.0567 | 0.047 | 0.0686 | 0.1508 | 0.3158 | 0.363 | 0.1653 | 0.2526 | 0.453 | 0.1731 | 0.6605 | 0.0708 | 0.3796 | 0.009 | 0.1569 | 0.0347 | 0.2932 | 0.057 | 0.3246 | | 1.3846 | 26.9953 | 2875 | 1.4379 | 0.077 | 0.1554 | 0.0671 | 0.052 | 0.0457 | 0.0747 | 0.1576 | 0.3191 | 0.3686 | 0.2459 | 0.2615 | 0.4191 | 0.2061 | 0.6762 | 0.0678 | 0.3939 | 0.0154 | 0.19 | 0.027 | 0.2136 | 0.069 | 0.3692 | | 1.3763 | 28.0 | 2982 | 1.4605 | 0.0727 | 0.1483 | 0.0675 | 0.0761 | 0.037 | 0.0817 | 0.1428 | 0.3136 | 0.3706 | 0.1434 | 0.2537 | 0.4855 | 0.2099 | 0.6663 | 0.0653 | 0.398 | 0.0116 | 0.1801 | 0.0247 | 0.2678 | 0.052 | 0.3408 | | 1.3449 | 28.9953 | 3088 | 1.4449 | 0.0808 | 0.1637 | 0.0757 | 0.1455 | 0.0567 | 0.0952 | 0.1746 | 0.3383 | 0.3933 | 0.3418 | 0.2831 | 0.5146 | 0.2091 | 0.6779 | 0.0717 | 0.4449 | 0.0119 | 0.1635 | 0.032 | 0.3085 | 0.0794 | 0.3716 | | 1.3355 | 30.0 | 3195 | 1.4498 | 0.0727 | 0.1544 | 0.0677 | 0.0271 | 0.0509 | 0.0832 | 0.1562 | 0.3189 | 0.3589 | 0.2176 | 0.2815 | 0.4364 | 0.2056 | 0.6715 | 0.0554 | 0.3245 | 0.0152 | 0.1773 | 0.0149 | 0.2458 | 0.0723 | 0.3754 | | 1.3218 | 30.9953 | 3301 | 1.4156 | 0.0784 | 0.1641 | 0.0656 | 0.0608 | 0.0528 | 0.0978 | 0.1575 | 0.3344 | 0.3867 | 0.1655 | 0.2825 | 0.5141 | 0.2088 | 0.686 | 0.0624 | 0.398 | 0.0211 | 0.21 | 0.0238 | 0.2661 | 0.0761 | 0.3735 | | 1.2773 | 32.0 | 3408 | 1.3722 | 0.0823 | 0.1682 | 0.0764 | 0.0641 | 0.056 | 0.1128 | 0.1602 | 0.3656 | 0.4143 | 0.1975 | 0.308 | 0.5599 | 0.2192 | 0.6913 | 0.0704 | 0.449 | 0.0199 | 0.2213 | 0.0216 | 0.3153 | 0.0803 | 0.3948 | | 1.2839 | 32.9953 | 3514 | 1.3795 | 0.09 | 0.186 | 0.0785 | 0.0733 | 0.0697 | 0.14 | 0.1867 | 0.3742 | 0.416 | 0.2753 | 0.2984 | 0.5739 | 0.2294 | 0.693 | 0.0819 | 0.4673 | 0.0212 | 0.2218 | 0.0293 | 0.3119 | 0.0883 | 0.3858 | | 1.2384 | 34.0 | 3621 | 1.3960 | 0.1002 | 0.2061 | 0.0858 | 0.0783 | 0.0594 | 0.1223 | 0.175 | 0.36 | 0.4115 | 0.2457 | 0.2905 | 0.5741 | 0.2479 | 0.6895 | 0.0775 | 0.402 | 0.0246 | 0.2479 | 0.0371 | 0.3136 | 0.114 | 0.4047 | | 1.2495 | 34.9953 | 3727 | 1.3261 | 0.1007 | 0.2102 | 0.087 | 0.0847 | 0.0659 | 0.1473 | 0.1918 | 0.3789 | 0.4263 | 0.2146 | 0.3201 | 0.5737 | 0.246 | 0.7035 | 0.0753 | 0.4204 | 0.0362 | 0.2588 | 0.0315 | 0.3593 | 0.1145 | 0.3896 | | 1.2032 | 36.0 | 3834 | 1.3012 | 0.1024 | 0.2052 | 0.0886 | 0.105 | 0.0723 | 0.157 | 0.1899 | 0.394 | 0.4376 | 0.2237 | 0.3284 | 0.5945 | 0.2543 | 0.6907 | 0.0731 | 0.4673 | 0.0369 | 0.272 | 0.0294 | 0.3542 | 0.1184 | 0.4038 | | 1.2024 | 36.9953 | 3940 | 1.2989 | 0.107 | 0.2271 | 0.0844 | 0.2679 | 0.0987 | 0.1728 | 0.1961 | 0.4048 | 0.4467 | 0.3934 | 0.348 | 0.5898 | 0.2451 | 0.7035 | 0.0819 | 0.449 | 0.0419 | 0.2773 | 0.0373 | 0.3814 | 0.1288 | 0.4223 | | 1.1714 | 38.0 | 4047 | 1.3187 | 0.1186 | 0.2361 | 0.1052 | 0.0464 | 0.0813 | 0.16 | 0.1932 | 0.3883 | 0.4304 | 0.2583 | 0.321 | 0.6007 | 0.3029 | 0.7058 | 0.0951 | 0.4143 | 0.0472 | 0.2739 | 0.0372 | 0.3373 | 0.1108 | 0.4209 | | 1.1702 | 38.9953 | 4153 | 1.3017 | 0.1158 | 0.2338 | 0.1025 | 0.1431 | 0.0923 | 0.1729 | 0.1998 | 0.4178 | 0.4544 | 0.2428 | 0.3371 | 0.6742 | 0.2624 | 0.7134 | 0.0981 | 0.4694 | 0.0458 | 0.2981 | 0.0427 | 0.3559 | 0.1299 | 0.4351 | | 1.1745 | 40.0 | 4260 | 1.2802 | 0.1167 | 0.2351 | 0.1035 | 0.0578 | 0.095 | 0.1591 | 0.2099 | 0.4126 | 0.4571 | 0.3426 | 0.3457 | 0.6024 | 0.2627 | 0.7174 | 0.1144 | 0.4939 | 0.0403 | 0.29 | 0.0334 | 0.3407 | 0.1328 | 0.4436 | | 1.1502 | 40.9953 | 4366 | 1.2797 | 0.1283 | 0.2538 | 0.1168 | 0.082 | 0.0831 | 0.1653 | 0.2048 | 0.4136 | 0.4552 | 0.322 | 0.3349 | 0.6206 | 0.301 | 0.7169 | 0.1078 | 0.449 | 0.0513 | 0.3085 | 0.043 | 0.3627 | 0.1382 | 0.4389 | | 1.1149 | 42.0 | 4473 | 1.2880 | 0.134 | 0.2744 | 0.1197 | 0.1218 | 0.1095 | 0.2147 | 0.2133 | 0.4295 | 0.4717 | 0.2168 | 0.3639 | 0.7173 | 0.3046 | 0.7238 | 0.1305 | 0.5286 | 0.0479 | 0.3005 | 0.0425 | 0.3746 | 0.1445 | 0.4313 | | 1.1224 | 42.9953 | 4579 | 1.2321 | 0.1477 | 0.2839 | 0.1314 | 0.1361 | 0.1143 | 0.2275 | 0.22 | 0.4406 | 0.4751 | 0.3523 | 0.3623 | 0.6821 | 0.3288 | 0.7326 | 0.1463 | 0.4918 | 0.0582 | 0.3242 | 0.0434 | 0.3915 | 0.1616 | 0.4355 | | 1.0958 | 44.0 | 4686 | 1.2293 | 0.1416 | 0.2833 | 0.1279 | 0.1761 | 0.1067 | 0.2186 | 0.2211 | 0.4368 | 0.4815 | 0.3122 | 0.3702 | 0.6796 | 0.3046 | 0.732 | 0.1434 | 0.4959 | 0.054 | 0.3171 | 0.0355 | 0.3915 | 0.1702 | 0.4711 | | 1.081 | 44.9953 | 4792 | 1.2400 | 0.1403 | 0.2807 | 0.1243 | 0.1979 | 0.1054 | 0.24 | 0.2378 | 0.4331 | 0.4761 | 0.3035 | 0.3484 | 0.7158 | 0.297 | 0.7291 | 0.1405 | 0.4959 | 0.0624 | 0.328 | 0.0345 | 0.3814 | 0.167 | 0.446 | | 1.0659 | 46.0 | 4899 | 1.2180 | 0.1518 | 0.2984 | 0.1404 | 0.0926 | 0.1198 | 0.2352 | 0.2261 | 0.4455 | 0.4907 | 0.3604 | 0.3798 | 0.6992 | 0.3282 | 0.7262 | 0.1433 | 0.5102 | 0.0668 | 0.3502 | 0.0452 | 0.4085 | 0.1757 | 0.4583 | | 1.0741 | 46.9953 | 5005 | 1.2355 | 0.1501 | 0.3066 | 0.1381 | 0.2278 | 0.1125 | 0.2314 | 0.2259 | 0.4356 | 0.4874 | 0.3453 | 0.3694 | 0.6911 | 0.3114 | 0.7134 | 0.1447 | 0.5367 | 0.0688 | 0.336 | 0.0354 | 0.3763 | 0.1904 | 0.4744 | | 1.074 | 48.0 | 5112 | 1.2082 | 0.1495 | 0.2917 | 0.1343 | 0.0927 | 0.1293 | 0.2343 | 0.2247 | 0.4535 | 0.4956 | 0.346 | 0.396 | 0.712 | 0.3327 | 0.732 | 0.1477 | 0.5122 | 0.056 | 0.3498 | 0.0308 | 0.4237 | 0.1805 | 0.4602 | | 1.067 | 48.9953 | 5218 | 1.2123 | 0.165 | 0.3104 | 0.1467 | 0.0957 | 0.1188 | 0.2524 | 0.2522 | 0.4578 | 0.5 | 0.3661 | 0.3737 | 0.7116 | 0.3553 | 0.7291 | 0.1624 | 0.5286 | 0.0706 | 0.3417 | 0.0463 | 0.422 | 0.1902 | 0.4787 | | 1.0404 | 50.0 | 5325 | 1.2088 | 0.1569 | 0.3099 | 0.1328 | 0.1295 | 0.1105 | 0.2661 | 0.235 | 0.4425 | 0.4879 | 0.3104 | 0.3708 | 0.7181 | 0.3615 | 0.7273 | 0.1395 | 0.4796 | 0.0763 | 0.3597 | 0.0414 | 0.439 | 0.1658 | 0.4336 | | 1.052 | 50.9953 | 5431 | 1.1966 | 0.1676 | 0.3345 | 0.1453 | 0.1543 | 0.1219 | 0.2477 | 0.2439 | 0.4492 | 0.493 | 0.2986 | 0.387 | 0.72 | 0.3652 | 0.725 | 0.1442 | 0.502 | 0.0701 | 0.3578 | 0.0622 | 0.4119 | 0.1963 | 0.4682 | | 1.0249 | 52.0 | 5538 | 1.2046 | 0.1601 | 0.324 | 0.1455 | 0.0884 | 0.1159 | 0.2423 | 0.2383 | 0.4475 | 0.4907 | 0.3318 | 0.3694 | 0.7025 | 0.3368 | 0.7186 | 0.1664 | 0.4959 | 0.072 | 0.3758 | 0.0313 | 0.3915 | 0.1941 | 0.4716 | | 1.0286 | 52.9953 | 5644 | 1.2160 | 0.1732 | 0.3482 | 0.1486 | 0.0809 | 0.124 | 0.2539 | 0.2465 | 0.4497 | 0.4899 | 0.2724 | 0.3755 | 0.6934 | 0.3827 | 0.7192 | 0.1516 | 0.5061 | 0.0716 | 0.337 | 0.0494 | 0.4322 | 0.2107 | 0.455 | | 1.0085 | 54.0 | 5751 | 1.2242 | 0.169 | 0.3425 | 0.1446 | 0.0889 | 0.1154 | 0.2541 | 0.2335 | 0.4485 | 0.4852 | 0.3407 | 0.3658 | 0.6959 | 0.3834 | 0.718 | 0.1546 | 0.4959 | 0.0797 | 0.3365 | 0.0465 | 0.4373 | 0.1805 | 0.4384 | | 1.0105 | 54.9953 | 5857 | 1.1761 | 0.1885 | 0.3678 | 0.1675 | 0.1149 | 0.1313 | 0.2775 | 0.2653 | 0.4731 | 0.5102 | 0.3635 | 0.3872 | 0.724 | 0.4022 | 0.736 | 0.1799 | 0.5429 | 0.0971 | 0.3739 | 0.0536 | 0.4288 | 0.2098 | 0.4692 | | 0.9725 | 56.0 | 5964 | 1.2053 | 0.1867 | 0.3609 | 0.1665 | 0.2528 | 0.1335 | 0.2908 | 0.2491 | 0.4626 | 0.5064 | 0.3851 | 0.3879 | 0.7348 | 0.4134 | 0.7372 | 0.1765 | 0.5367 | 0.0808 | 0.3398 | 0.0615 | 0.4644 | 0.2013 | 0.454 | | 0.9825 | 56.9953 | 6070 | 1.1839 | 0.1857 | 0.361 | 0.1718 | 0.1396 | 0.1366 | 0.2957 | 0.2667 | 0.4622 | 0.5012 | 0.3591 | 0.3848 | 0.715 | 0.4079 | 0.7395 | 0.2112 | 0.5245 | 0.0823 | 0.3664 | 0.0412 | 0.4237 | 0.1858 | 0.4517 | | 0.9768 | 58.0 | 6177 | 1.1782 | 0.1909 | 0.3696 | 0.1677 | 0.1695 | 0.1316 | 0.3046 | 0.2637 | 0.4752 | 0.5143 | 0.3641 | 0.3965 | 0.7246 | 0.4094 | 0.7285 | 0.1889 | 0.5367 | 0.0866 | 0.3758 | 0.0526 | 0.4559 | 0.2171 | 0.4744 | | 0.9646 | 58.9953 | 6283 | 1.2234 | 0.1752 | 0.3522 | 0.1485 | 0.0982 | 0.1131 | 0.2967 | 0.2342 | 0.4388 | 0.4825 | 0.3028 | 0.3743 | 0.6903 | 0.4095 | 0.711 | 0.1621 | 0.4939 | 0.0666 | 0.3308 | 0.0441 | 0.4322 | 0.1937 | 0.4445 | | 0.9433 | 60.0 | 6390 | 1.1778 | 0.2039 | 0.3913 | 0.1795 | 0.1085 | 0.1387 | 0.3213 | 0.2627 | 0.4721 | 0.5035 | 0.3112 | 0.3827 | 0.7032 | 0.4448 | 0.7448 | 0.2013 | 0.5245 | 0.0995 | 0.3678 | 0.0554 | 0.422 | 0.2184 | 0.4583 | | 0.9539 | 60.9953 | 6496 | 1.1710 | 0.2142 | 0.3998 | 0.1909 | 0.0873 | 0.1439 | 0.3343 | 0.258 | 0.4688 | 0.5159 | 0.3227 | 0.3985 | 0.71 | 0.4632 | 0.7448 | 0.2376 | 0.5286 | 0.0976 | 0.3645 | 0.0512 | 0.4661 | 0.2214 | 0.4758 | | 0.9323 | 62.0 | 6603 | 1.1525 | 0.2241 | 0.4084 | 0.2017 | 0.1758 | 0.1621 | 0.3357 | 0.2827 | 0.4755 | 0.5187 | 0.3303 | 0.3996 | 0.7289 | 0.4692 | 0.7413 | 0.2387 | 0.5367 | 0.1032 | 0.3635 | 0.0586 | 0.4542 | 0.2508 | 0.4976 | | 0.9312 | 62.9953 | 6709 | 1.1400 | 0.2201 | 0.4184 | 0.1825 | 0.2621 | 0.1752 | 0.3242 | 0.2887 | 0.4916 | 0.531 | 0.4282 | 0.4101 | 0.7258 | 0.4556 | 0.7413 | 0.2209 | 0.5469 | 0.0957 | 0.3716 | 0.0663 | 0.4898 | 0.2622 | 0.5052 | | 0.9131 | 64.0 | 6816 | 1.1468 | 0.2239 | 0.4165 | 0.2042 | 0.2835 | 0.1687 | 0.3288 | 0.2852 | 0.4906 | 0.5291 | 0.4339 | 0.4064 | 0.7331 | 0.4363 | 0.7302 | 0.2399 | 0.5612 | 0.1086 | 0.3754 | 0.0774 | 0.4847 | 0.2575 | 0.4938 | | 0.924 | 64.9953 | 6922 | 1.1554 | 0.2301 | 0.4351 | 0.2124 | 0.239 | 0.1666 | 0.357 | 0.2763 | 0.4864 | 0.5214 | 0.3761 | 0.398 | 0.7455 | 0.4637 | 0.725 | 0.2383 | 0.5327 | 0.1101 | 0.3545 | 0.09 | 0.5017 | 0.2487 | 0.4934 | | 0.9114 | 66.0 | 7029 | 1.1486 | 0.2231 | 0.4351 | 0.2016 | 0.2949 | 0.1585 | 0.3553 | 0.2745 | 0.4848 | 0.5247 | 0.426 | 0.4126 | 0.7391 | 0.4658 | 0.7366 | 0.2095 | 0.5347 | 0.1104 | 0.3768 | 0.0835 | 0.4932 | 0.2465 | 0.482 | | 0.9218 | 66.9953 | 7135 | 1.1245 | 0.2343 | 0.4329 | 0.2067 | 0.2009 | 0.1752 | 0.3804 | 0.2929 | 0.4957 | 0.5349 | 0.3876 | 0.4202 | 0.7486 | 0.4825 | 0.7494 | 0.2458 | 0.549 | 0.1214 | 0.3915 | 0.0671 | 0.4831 | 0.2549 | 0.5014 | | 0.8886 | 68.0 | 7242 | 1.1260 | 0.236 | 0.4351 | 0.2162 | 0.2133 | 0.1619 | 0.3728 | 0.2844 | 0.4972 | 0.5283 | 0.3972 | 0.3923 | 0.7581 | 0.4756 | 0.7273 | 0.2387 | 0.549 | 0.1187 | 0.391 | 0.0872 | 0.4678 | 0.2601 | 0.5062 | | 0.898 | 68.9953 | 7348 | 1.1272 | 0.2419 | 0.4601 | 0.2178 | 0.2485 | 0.1736 | 0.3688 | 0.2919 | 0.4958 | 0.5333 | 0.3847 | 0.41 | 0.7582 | 0.4948 | 0.7314 | 0.2347 | 0.5388 | 0.1286 | 0.3938 | 0.0871 | 0.5 | 0.264 | 0.5024 | | 0.88 | 70.0 | 7455 | 1.1621 | 0.2398 | 0.4532 | 0.2123 | 0.2639 | 0.1676 | 0.3743 | 0.2785 | 0.4865 | 0.5151 | 0.3999 | 0.3942 | 0.7292 | 0.4905 | 0.7215 | 0.256 | 0.5408 | 0.1072 | 0.3526 | 0.0876 | 0.4746 | 0.2574 | 0.4858 | | 0.8868 | 70.9953 | 7561 | 1.1089 | 0.2574 | 0.4713 | 0.2332 | 0.2675 | 0.1874 | 0.392 | 0.2957 | 0.5025 | 0.5356 | 0.4084 | 0.4319 | 0.7417 | 0.5181 | 0.7413 | 0.2715 | 0.5571 | 0.13 | 0.3844 | 0.1029 | 0.5017 | 0.2642 | 0.4934 | | 0.876 | 72.0 | 7668 | 1.1296 | 0.2469 | 0.4705 | 0.2256 | 0.2927 | 0.1699 | 0.3973 | 0.2881 | 0.4892 | 0.5235 | 0.4206 | 0.4026 | 0.7447 | 0.5116 | 0.7314 | 0.2526 | 0.5653 | 0.1131 | 0.3739 | 0.1002 | 0.4542 | 0.257 | 0.4924 | | 0.8786 | 72.9953 | 7774 | 1.1189 | 0.2503 | 0.4775 | 0.2262 | 0.2579 | 0.1729 | 0.4036 | 0.2811 | 0.4968 | 0.5319 | 0.4364 | 0.4133 | 0.7461 | 0.5128 | 0.7308 | 0.2551 | 0.549 | 0.1207 | 0.3915 | 0.0923 | 0.4915 | 0.2707 | 0.4967 | | 0.8646 | 74.0 | 7881 | 1.1545 | 0.2503 | 0.4782 | 0.2289 | 0.2576 | 0.1648 | 0.4083 | 0.2792 | 0.4817 | 0.5187 | 0.4232 | 0.3924 | 0.7514 | 0.5224 | 0.7198 | 0.2514 | 0.5224 | 0.1166 | 0.3635 | 0.1066 | 0.5051 | 0.2547 | 0.4825 | | 0.8624 | 74.9953 | 7987 | 1.1148 | 0.2612 | 0.4931 | 0.2276 | 0.2794 | 0.1776 | 0.4119 | 0.2917 | 0.4974 | 0.5361 | 0.4242 | 0.4151 | 0.7662 | 0.5376 | 0.7401 | 0.2444 | 0.5633 | 0.1307 | 0.3848 | 0.1186 | 0.5119 | 0.275 | 0.4806 | | 0.838 | 76.0 | 8094 | 1.0995 | 0.2687 | 0.493 | 0.2477 | 0.2754 | 0.1831 | 0.4188 | 0.303 | 0.497 | 0.5302 | 0.3972 | 0.406 | 0.7602 | 0.5425 | 0.7349 | 0.2761 | 0.5367 | 0.1306 | 0.3991 | 0.1082 | 0.4915 | 0.2861 | 0.4886 | | 0.8483 | 76.9953 | 8200 | 1.1110 | 0.2698 | 0.4914 | 0.2511 | 0.1734 | 0.1845 | 0.4311 | 0.2965 | 0.4996 | 0.532 | 0.405 | 0.4092 | 0.7608 | 0.5616 | 0.7477 | 0.2642 | 0.5367 | 0.135 | 0.3924 | 0.1092 | 0.4966 | 0.2791 | 0.4867 | | 0.8413 | 78.0 | 8307 | 1.1064 | 0.2644 | 0.4912 | 0.2444 | 0.1873 | 0.1831 | 0.4319 | 0.2896 | 0.4983 | 0.5336 | 0.3939 | 0.4117 | 0.7562 | 0.5319 | 0.7343 | 0.2637 | 0.5265 | 0.1325 | 0.4095 | 0.1126 | 0.5 | 0.2812 | 0.4976 | | 0.8424 | 78.9953 | 8413 | 1.1112 | 0.2682 | 0.496 | 0.2498 | 0.1394 | 0.184 | 0.4279 | 0.2966 | 0.4985 | 0.5272 | 0.358 | 0.4079 | 0.7394 | 0.552 | 0.7395 | 0.2704 | 0.5429 | 0.1313 | 0.3815 | 0.1094 | 0.5 | 0.2779 | 0.472 | | 0.8277 | 80.0 | 8520 | 1.1451 | 0.2504 | 0.4758 | 0.2206 | 0.2244 | 0.1643 | 0.4229 | 0.2734 | 0.4878 | 0.517 | 0.3697 | 0.3882 | 0.7498 | 0.5308 | 0.7308 | 0.2379 | 0.5347 | 0.1187 | 0.373 | 0.0919 | 0.4678 | 0.2725 | 0.4787 | | 0.8269 | 80.9953 | 8626 | 1.1064 | 0.2697 | 0.5029 | 0.2497 | 0.2324 | 0.1932 | 0.4296 | 0.2832 | 0.5024 | 0.5379 | 0.3857 | 0.4293 | 0.7615 | 0.5475 | 0.7424 | 0.2713 | 0.5673 | 0.1481 | 0.3953 | 0.1 | 0.4898 | 0.2818 | 0.4948 | | 0.8159 | 82.0 | 8733 | 1.1055 | 0.2668 | 0.5022 | 0.2361 | 0.3082 | 0.1789 | 0.4284 | 0.2955 | 0.4975 | 0.5315 | 0.4478 | 0.4038 | 0.7377 | 0.5396 | 0.7267 | 0.2607 | 0.5429 | 0.1491 | 0.4024 | 0.1042 | 0.5051 | 0.2805 | 0.4806 | | 0.8195 | 82.9953 | 8839 | 1.1321 | 0.2674 | 0.505 | 0.2418 | 0.3007 | 0.1856 | 0.4311 | 0.289 | 0.4912 | 0.528 | 0.4278 | 0.4039 | 0.7507 | 0.5368 | 0.7267 | 0.2728 | 0.5633 | 0.1336 | 0.3863 | 0.1169 | 0.4898 | 0.2772 | 0.4739 | | 0.8138 | 84.0 | 8946 | 1.1110 | 0.2731 | 0.5061 | 0.2498 | 0.2881 | 0.192 | 0.44 | 0.2935 | 0.503 | 0.5382 | 0.4484 | 0.4141 | 0.7625 | 0.5537 | 0.7407 | 0.272 | 0.5796 | 0.1368 | 0.3863 | 0.1223 | 0.5 | 0.2808 | 0.4844 | | 0.8113 | 84.9953 | 9052 | 1.1105 | 0.2722 | 0.5054 | 0.255 | 0.2724 | 0.1862 | 0.4388 | 0.2906 | 0.4969 | 0.536 | 0.4272 | 0.412 | 0.7586 | 0.5428 | 0.7314 | 0.2666 | 0.5592 | 0.1484 | 0.4033 | 0.125 | 0.5051 | 0.2784 | 0.481 | | 0.808 | 86.0 | 9159 | 1.1130 | 0.2733 | 0.5056 | 0.253 | 0.2557 | 0.1873 | 0.4403 | 0.2926 | 0.502 | 0.5359 | 0.4107 | 0.4037 | 0.7613 | 0.5414 | 0.7384 | 0.2814 | 0.5612 | 0.1421 | 0.3948 | 0.1221 | 0.5085 | 0.2798 | 0.4768 | | 0.8052 | 86.9953 | 9265 | 1.1015 | 0.2786 | 0.5087 | 0.2612 | 0.2996 | 0.192 | 0.4411 | 0.2995 | 0.5082 | 0.5401 | 0.4446 | 0.4152 | 0.7531 | 0.555 | 0.7326 | 0.2882 | 0.5653 | 0.1422 | 0.4076 | 0.1207 | 0.5119 | 0.2867 | 0.4834 | | 0.7967 | 88.0 | 9372 | 1.1301 | 0.2689 | 0.5019 | 0.2442 | 0.3262 | 0.1843 | 0.4353 | 0.2939 | 0.4921 | 0.5253 | 0.452 | 0.3952 | 0.7619 | 0.5423 | 0.7279 | 0.2769 | 0.5612 | 0.1295 | 0.3668 | 0.1215 | 0.4983 | 0.2745 | 0.472 | | 0.7837 | 88.9953 | 9478 | 1.1018 | 0.276 | 0.5116 | 0.2489 | 0.2803 | 0.1919 | 0.4484 | 0.304 | 0.5015 | 0.5301 | 0.401 | 0.4065 | 0.7623 | 0.5529 | 0.7308 | 0.2728 | 0.549 | 0.153 | 0.4014 | 0.1207 | 0.4898 | 0.2806 | 0.4796 | | 0.7865 | 90.0 | 9585 | 1.1292 | 0.2712 | 0.5082 | 0.2462 | 0.2705 | 0.1855 | 0.4462 | 0.2898 | 0.494 | 0.524 | 0.3992 | 0.3985 | 0.7521 | 0.5493 | 0.7267 | 0.2821 | 0.5429 | 0.127 | 0.3673 | 0.1233 | 0.5085 | 0.2742 | 0.4744 | | 0.7882 | 90.9953 | 9691 | 1.1164 | 0.2766 | 0.5191 | 0.2477 | 0.3024 | 0.1952 | 0.4425 | 0.296 | 0.5001 | 0.529 | 0.4275 | 0.4097 | 0.751 | 0.5528 | 0.7308 | 0.2731 | 0.5347 | 0.1424 | 0.3848 | 0.1298 | 0.5119 | 0.2849 | 0.4829 | | 0.7887 | 92.0 | 9798 | 1.1070 | 0.2776 | 0.5169 | 0.2512 | 0.2553 | 0.1923 | 0.4504 | 0.2999 | 0.5033 | 0.5333 | 0.4005 | 0.4078 | 0.7665 | 0.5547 | 0.7343 | 0.2719 | 0.5449 | 0.1517 | 0.3962 | 0.1237 | 0.5068 | 0.2861 | 0.4844 | | 0.7905 | 92.9953 | 9904 | 1.0951 | 0.2753 | 0.5169 | 0.2526 | 0.2849 | 0.1916 | 0.4366 | 0.2928 | 0.504 | 0.54 | 0.4511 | 0.411 | 0.7577 | 0.5602 | 0.7337 | 0.2639 | 0.549 | 0.1435 | 0.4019 | 0.1204 | 0.5339 | 0.2884 | 0.4815 | | 0.7743 | 94.0 | 10011 | 1.0933 | 0.2819 | 0.521 | 0.2597 | 0.2878 | 0.1949 | 0.4462 | 0.2988 | 0.5049 | 0.541 | 0.4443 | 0.4101 | 0.772 | 0.572 | 0.7378 | 0.2717 | 0.551 | 0.1474 | 0.4043 | 0.1252 | 0.5254 | 0.2933 | 0.4867 | | 0.7762 | 94.9953 | 10117 | 1.1082 | 0.2785 | 0.5219 | 0.2596 | 0.2847 | 0.1917 | 0.4507 | 0.2935 | 0.4993 | 0.5339 | 0.4327 | 0.4051 | 0.7669 | 0.5695 | 0.7355 | 0.2691 | 0.5531 | 0.1402 | 0.3834 | 0.1218 | 0.5169 | 0.2921 | 0.4806 | | 0.7618 | 96.0 | 10224 | 1.1062 | 0.2774 | 0.5204 | 0.2555 | 0.287 | 0.1876 | 0.4483 | 0.2982 | 0.5005 | 0.532 | 0.436 | 0.4003 | 0.7614 | 0.5632 | 0.7326 | 0.2667 | 0.5408 | 0.1435 | 0.3938 | 0.1251 | 0.5153 | 0.2886 | 0.4777 | | 0.783 | 96.9953 | 10330 | 1.1082 | 0.279 | 0.523 | 0.2572 | 0.284 | 0.1949 | 0.4486 | 0.302 | 0.4993 | 0.5334 | 0.4331 | 0.4071 | 0.7629 | 0.5593 | 0.7308 | 0.2744 | 0.5531 | 0.1447 | 0.3915 | 0.1238 | 0.5085 | 0.293 | 0.4834 | | 0.7681 | 98.0 | 10437 | 1.0984 | 0.2819 | 0.5263 | 0.257 | 0.288 | 0.1948 | 0.4534 | 0.304 | 0.503 | 0.5368 | 0.4351 | 0.413 | 0.7653 | 0.5622 | 0.7326 | 0.277 | 0.549 | 0.1479 | 0.3957 | 0.129 | 0.5169 | 0.2933 | 0.4896 | | 0.7742 | 98.9953 | 10543 | 1.1026 | 0.2806 | 0.5263 | 0.2569 | 0.2889 | 0.1948 | 0.4543 | 0.3014 | 0.5012 | 0.5341 | 0.4352 | 0.4077 | 0.7639 | 0.5568 | 0.732 | 0.2749 | 0.549 | 0.1484 | 0.3948 | 0.1271 | 0.5102 | 0.2957 | 0.4848 | | 0.7654 | 99.5305 | 10600 | 1.1028 | 0.2808 | 0.5257 | 0.2589 | 0.2879 | 0.1953 | 0.4546 | 0.3004 | 0.5007 | 0.534 | 0.4339 | 0.4079 | 0.7629 | 0.5566 | 0.7331 | 0.2761 | 0.549 | 0.1484 | 0.3943 | 0.1271 | 0.5085 | 0.2956 | 0.4853 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

sekhharr/detr_finetuned_v11_last_checkpoint

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]

sekhharr/detr_finetuned_v11_bestlast_checkpoint

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5" ]

Yaroslava270602/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6906 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.6507 | 0.04 | 100 | 2.1677 | | 1.8892 | 0.08 | 200 | 1.6329 | | 1.638 | 0.12 | 300 | 1.5517 | | 1.4457 | 0.16 | 400 | 1.4176 | | 1.3489 | 0.2 | 500 | 1.2559 | | 1.277 | 0.24 | 600 | 1.2726 | | 1.2948 | 0.28 | 700 | 1.2169 | | 1.1878 | 0.32 | 800 | 1.1680 | | 1.1781 | 0.36 | 900 | 1.1291 | | 1.1747 | 0.4 | 1000 | 1.1146 | | 1.1966 | 0.44 | 1100 | 1.1399 | | 1.1641 | 0.48 | 1200 | 1.0844 | | 1.128 | 0.52 | 1300 | 1.1119 | | 1.1191 | 0.56 | 1400 | 1.0528 | | 1.1435 | 0.6 | 1500 | 1.0689 | | 1.1657 | 0.64 | 1600 | 1.1484 | | 1.1727 | 0.68 | 1700 | 1.0764 | | 1.1085 | 0.72 | 1800 | 1.0391 | | 1.0579 | 0.76 | 1900 | 1.0012 | | 1.0935 | 0.8 | 2000 | 1.0160 | | 1.054 | 0.84 | 2100 | 0.9796 | | 1.0486 | 0.88 | 2200 | 0.9508 | | 1.0472 | 0.92 | 2300 | 0.9869 | | 1.032 | 0.96 | 2400 | 0.9655 | | 1.0313 | 1.0 | 2500 | 0.9484 | | 1.0096 | 1.04 | 2600 | 0.9676 | | 1.0279 | 1.08 | 2700 | 1.0771 | | 1.027 | 1.12 | 2800 | 0.9912 | | 1.0415 | 1.16 | 2900 | 0.9882 | | 1.003 | 1.2 | 3000 | 0.9579 | | 1.0084 | 1.24 | 3100 | 0.9288 | | 0.9353 | 1.28 | 3200 | 0.9278 | | 0.9514 | 1.32 | 3300 | 0.8915 | | 0.9452 | 1.36 | 3400 | 0.8904 | | 0.9312 | 1.4 | 3500 | 0.8925 | | 0.9256 | 1.44 | 3600 | 0.8729 | | 0.8861 | 1.48 | 3700 | 0.8655 | | 0.9043 | 1.52 | 3800 | 0.8977 | | 0.8935 | 1.56 | 3900 | 0.8679 | | 0.8974 | 1.6 | 4000 | 0.8908 | | 0.9342 | 1.64 | 4100 | 0.8742 | | 0.889 | 1.68 | 4200 | 0.8534 | | 0.8998 | 1.72 | 4300 | 0.8409 | | 0.8727 | 1.76 | 4400 | 0.8333 | | 0.8728 | 1.8 | 4500 | 0.8386 | | 0.8525 | 1.84 | 4600 | 0.8152 | | 0.8709 | 1.88 | 4700 | 0.8146 | | 0.8694 | 1.92 | 4800 | 0.8245 | | 0.8663 | 1.96 | 4900 | 0.8216 | | 0.8442 | 2.0 | 5000 | 0.8019 | | 0.8256 | 2.04 | 5100 | 0.8022 | | 0.8385 | 2.08 | 5200 | 0.7938 | | 0.7995 | 2.12 | 5300 | 0.7958 | | 0.8217 | 2.16 | 5400 | 0.7962 | | 0.8432 | 2.2 | 5500 | 0.7772 | | 0.8228 | 2.24 | 5600 | 0.7857 | | 0.8283 | 2.28 | 5700 | 0.7982 | | 0.772 | 2.32 | 5800 | 0.7969 | | 0.8019 | 2.36 | 5900 | 0.7902 | | 0.7805 | 2.4 | 6000 | 0.7782 | | 0.802 | 2.44 | 6100 | 0.7681 | | 0.8483 | 2.48 | 6200 | 0.7722 | | 0.802 | 2.52 | 6300 | 0.7673 | | 0.8064 | 2.56 | 6400 | 0.7603 | | 0.7638 | 2.6 | 6500 | 0.7475 | | 0.7727 | 2.64 | 6600 | 0.7515 | | 0.801 | 2.68 | 6700 | 0.7523 | | 0.8022 | 2.72 | 6800 | 0.7519 | | 0.8074 | 2.76 | 6900 | 0.7555 | | 0.7951 | 2.8 | 7000 | 0.7450 | | 0.8125 | 2.84 | 7100 | 0.7476 | | 0.8085 | 2.88 | 7200 | 0.7505 | | 0.7959 | 2.92 | 7300 | 0.7432 | | 0.7668 | 2.96 | 7400 | 0.7454 | | 0.7666 | 3.0 | 7500 | 0.7419 | | 0.7422 | 3.04 | 7600 | 0.7284 | | 0.7713 | 3.08 | 7700 | 0.7418 | | 0.7296 | 3.12 | 7800 | 0.7274 | | 0.7468 | 3.16 | 7900 | 0.7224 | | 0.7767 | 3.2 | 8000 | 0.7268 | | 0.7526 | 3.24 | 8100 | 0.7210 | | 0.7328 | 3.28 | 8200 | 0.7139 | | 0.7626 | 3.32 | 8300 | 0.7142 | | 0.7515 | 3.36 | 8400 | 0.7102 | | 0.7141 | 3.4 | 8500 | 0.7100 | | 0.7068 | 3.44 | 8600 | 0.7097 | | 0.7274 | 3.48 | 8700 | 0.7018 | | 0.7458 | 3.52 | 8800 | 0.7041 | | 0.7205 | 3.56 | 8900 | 0.7065 | | 0.7643 | 3.6 | 9000 | 0.6985 | | 0.6968 | 3.64 | 9100 | 0.6983 | | 0.7111 | 3.68 | 9200 | 0.6982 | | 0.7229 | 3.72 | 9300 | 0.6920 | | 0.7466 | 3.76 | 9400 | 0.6959 | | 0.7126 | 3.8 | 9500 | 0.6925 | | 0.739 | 3.84 | 9600 | 0.6869 | | 0.7449 | 3.88 | 9700 | 0.6939 | | 0.7139 | 3.92 | 9800 | 0.6893 | | 0.7216 | 3.96 | 9900 | 0.6895 | | 0.6942 | 4.0 | 10000 | 0.6906 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5

# jozhang97-deta-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.7663 - Map: 0.2022 - Map 50: 0.4588 - Map 75: 0.1509 - Map Small: 0.0948 - Map Medium: 0.1223 - Map Large: 0.2882 - Mar 1: 0.2396 - Mar 10: 0.405 - Mar 100: 0.4238 - Mar Small: 0.2134 - Mar Medium: 0.3177 - Mar Large: 0.5501 - Map Coverall: 0.5051 - Mar 100 Coverall: 0.6628 - Map Face Shield: 0.1207 - Mar 100 Face Shield: 0.3371 - Map Gloves: 0.0983 - Mar 100 Gloves: 0.3115 - Map Goggles: 0.1325 - Mar 100 Goggles: 0.431 - Map Mask: 0.1545 - Mar 100 Mask: 0.3768 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 12.2099 | 0.9953 | 106 | 3.7120 | 0.0067 | 0.0314 | 0.0003 | 0.0 | 0.0006 | 0.0068 | 0.0122 | 0.0353 | 0.0421 | 0.0 | 0.0067 | 0.0451 | 0.0332 | 0.2049 | 0.0001 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.4288 | 2.0 | 213 | 3.8665 | 0.0117 | 0.0403 | 0.0038 | 0.0 | 0.0004 | 0.0118 | 0.0199 | 0.0448 | 0.0505 | 0.0 | 0.0015 | 0.0592 | 0.0577 | 0.2348 | 0.001 | 0.0143 | 0.0 | 0.0021 | 0.0 | 0.0 | 0.0 | 0.0011 | | 3.5031 | 2.9953 | 319 | 3.5816 | 0.014 | 0.0448 | 0.0075 | 0.0 | 0.0003 | 0.0143 | 0.0236 | 0.0424 | 0.0486 | 0.0 | 0.0015 | 0.0659 | 0.0693 | 0.2238 | 0.0001 | 0.0071 | 0.0 | 0.0031 | 0.0004 | 0.0024 | 0.0001 | 0.0068 | | 3.3269 | 4.0 | 426 | 3.1994 | 0.02 | 0.0594 | 0.0065 | 0.0 | 0.0002 | 0.0203 | 0.0242 | 0.0567 | 0.066 | 0.0004 | 0.0027 | 0.1003 | 0.0991 | 0.2768 | 0.0002 | 0.0271 | 0.0 | 0.0078 | 0.0 | 0.0095 | 0.0004 | 0.0085 | | 3.1804 | 4.9953 | 532 | 3.0309 | 0.0158 | 0.0494 | 0.0066 | 0.0 | 0.0017 | 0.0151 | 0.0262 | 0.0535 | 0.064 | 0.0004 | 0.0092 | 0.1022 | 0.0713 | 0.2451 | 0.001 | 0.0343 | 0.0001 | 0.0104 | 0.0 | 0.0 | 0.0068 | 0.0299 | | 2.9763 | 6.0 | 639 | 2.7981 | 0.0281 | 0.0939 | 0.0086 | 0.0019 | 0.0023 | 0.03 | 0.0354 | 0.0831 | 0.098 | 0.0147 | 0.0284 | 0.1149 | 0.1311 | 0.3433 | 0.0006 | 0.05 | 0.0 | 0.0052 | 0.0001 | 0.0024 | 0.009 | 0.0893 | | 2.7978 | 6.9953 | 745 | 2.6720 | 0.0344 | 0.0999 | 0.0131 | 0.0017 | 0.0036 | 0.0338 | 0.0498 | 0.109 | 0.1197 | 0.0263 | 0.0547 | 0.1193 | 0.1616 | 0.372 | 0.005 | 0.0957 | 0.0 | 0.0177 | 0.0004 | 0.0333 | 0.0047 | 0.0797 | | 2.6679 | 8.0 | 852 | 2.6906 | 0.0363 | 0.0999 | 0.0137 | 0.0146 | 0.0065 | 0.0366 | 0.0438 | 0.0947 | 0.104 | 0.0321 | 0.0332 | 0.1264 | 0.1681 | 0.3415 | 0.0083 | 0.0886 | 0.0004 | 0.0167 | 0.0002 | 0.0119 | 0.0047 | 0.0616 | | 2.6637 | 8.9953 | 958 | 2.6518 | 0.0319 | 0.1009 | 0.012 | 0.0032 | 0.005 | 0.0309 | 0.0462 | 0.1171 | 0.133 | 0.0089 | 0.0649 | 0.1368 | 0.1457 | 0.4006 | 0.0048 | 0.0943 | 0.0001 | 0.0167 | 0.0004 | 0.0381 | 0.0086 | 0.1153 | | 2.6412 | 10.0 | 1065 | 2.5417 | 0.0443 | 0.12 | 0.0164 | 0.0548 | 0.0015 | 0.0465 | 0.0567 | 0.1178 | 0.1316 | 0.0591 | 0.0452 | 0.171 | 0.2142 | 0.4183 | 0.0031 | 0.0929 | 0.0002 | 0.0281 | 0.0006 | 0.0381 | 0.0032 | 0.0808 | | 2.6155 | 10.9953 | 1171 | 2.7743 | 0.0356 | 0.1122 | 0.0216 | 0.0031 | 0.0049 | 0.0435 | 0.0527 | 0.1254 | 0.1399 | 0.0239 | 0.0623 | 0.1768 | 0.1562 | 0.3909 | 0.0054 | 0.08 | 0.0005 | 0.0396 | 0.0015 | 0.0714 | 0.0142 | 0.1175 | | 2.5621 | 12.0 | 1278 | 2.5292 | 0.0473 | 0.1309 | 0.0258 | 0.003 | 0.0027 | 0.0567 | 0.06 | 0.1265 | 0.1404 | 0.0107 | 0.042 | 0.2137 | 0.2226 | 0.4561 | 0.0038 | 0.0629 | 0.0014 | 0.038 | 0.0044 | 0.0643 | 0.0045 | 0.0808 | | 2.4871 | 12.9953 | 1384 | 2.5235 | 0.0418 | 0.1211 | 0.0183 | 0.01 | 0.0017 | 0.0451 | 0.0555 | 0.1225 | 0.1389 | 0.0219 | 0.0412 | 0.2163 | 0.1994 | 0.4287 | 0.0021 | 0.0757 | 0.0014 | 0.063 | 0.0018 | 0.0595 | 0.0041 | 0.0678 | | 2.4874 | 14.0 | 1491 | 2.4597 | 0.0585 | 0.148 | 0.0377 | 0.0142 | 0.0047 | 0.0656 | 0.0746 | 0.1555 | 0.1698 | 0.0353 | 0.0689 | 0.2626 | 0.2705 | 0.4659 | 0.0064 | 0.13 | 0.0012 | 0.0562 | 0.0058 | 0.0929 | 0.0087 | 0.104 | | 2.4629 | 14.9953 | 1597 | 2.3910 | 0.0587 | 0.1563 | 0.0333 | 0.0039 | 0.0092 | 0.0703 | 0.086 | 0.1896 | 0.1994 | 0.0238 | 0.0976 | 0.299 | 0.2543 | 0.4994 | 0.0115 | 0.1971 | 0.0022 | 0.0448 | 0.003 | 0.1238 | 0.0225 | 0.1316 | | 2.4231 | 16.0 | 1704 | 2.6214 | 0.0538 | 0.1494 | 0.0256 | 0.0069 | 0.0102 | 0.0676 | 0.0821 | 0.1857 | 0.2013 | 0.0119 | 0.0926 | 0.3454 | 0.2271 | 0.489 | 0.0093 | 0.1914 | 0.0023 | 0.0656 | 0.0038 | 0.1095 | 0.0266 | 0.1508 | | 2.4166 | 16.9953 | 1810 | 2.5325 | 0.0632 | 0.1615 | 0.0332 | 0.0075 | 0.0096 | 0.0749 | 0.0829 | 0.1743 | 0.1932 | 0.0202 | 0.0914 | 0.3148 | 0.2769 | 0.4756 | 0.0044 | 0.1657 | 0.0038 | 0.0651 | 0.0065 | 0.1286 | 0.0244 | 0.1311 | | 2.3794 | 18.0 | 1917 | 2.4702 | 0.0843 | 0.1921 | 0.0629 | 0.0217 | 0.0134 | 0.0982 | 0.1035 | 0.2074 | 0.2204 | 0.0226 | 0.1022 | 0.3572 | 0.3649 | 0.5555 | 0.0108 | 0.1957 | 0.0031 | 0.0911 | 0.0143 | 0.1286 | 0.0283 | 0.1311 | | 2.3384 | 18.9953 | 2023 | 2.5070 | 0.0737 | 0.1736 | 0.0519 | 0.0007 | 0.012 | 0.0866 | 0.0936 | 0.1942 | 0.2156 | 0.0032 | 0.1048 | 0.3206 | 0.32 | 0.5494 | 0.0125 | 0.2029 | 0.0058 | 0.0932 | 0.0054 | 0.0976 | 0.0249 | 0.135 | | 2.3538 | 20.0 | 2130 | 2.5906 | 0.0647 | 0.1794 | 0.0375 | 0.0033 | 0.0198 | 0.0829 | 0.1073 | 0.2141 | 0.2289 | 0.0059 | 0.1278 | 0.3508 | 0.2457 | 0.5171 | 0.0264 | 0.2214 | 0.0041 | 0.1016 | 0.0081 | 0.1214 | 0.0395 | 0.1831 | | 2.5129 | 20.9953 | 2236 | 2.9187 | 0.0672 | 0.1692 | 0.0533 | 0.0005 | 0.0144 | 0.0905 | 0.1011 | 0.2206 | 0.2352 | 0.002 | 0.1177 | 0.4044 | 0.2685 | 0.5421 | 0.028 | 0.2929 | 0.0083 | 0.1083 | 0.0016 | 0.0881 | 0.0297 | 0.1446 | | 2.7734 | 22.0 | 2343 | 3.1307 | 0.0512 | 0.1436 | 0.0257 | 0.0042 | 0.0134 | 0.0861 | 0.0949 | 0.2129 | 0.2249 | 0.0077 | 0.1169 | 0.3711 | 0.1908 | 0.5091 | 0.0348 | 0.27 | 0.0037 | 0.0625 | 0.0057 | 0.1333 | 0.0212 | 0.1497 | | 2.9978 | 22.9953 | 2449 | 3.1732 | 0.0459 | 0.1255 | 0.027 | 0.0093 | 0.0144 | 0.069 | 0.0842 | 0.2302 | 0.2463 | 0.0128 | 0.1446 | 0.3827 | 0.1654 | 0.5201 | 0.0227 | 0.2414 | 0.0066 | 0.0807 | 0.0138 | 0.1905 | 0.021 | 0.1989 | | 3.3046 | 24.0 | 2556 | 3.4681 | 0.0339 | 0.1034 | 0.0174 | 0.0159 | 0.0106 | 0.0538 | 0.065 | 0.2138 | 0.2341 | 0.1286 | 0.1402 | 0.3069 | 0.1219 | 0.5024 | 0.0072 | 0.1929 | 0.0037 | 0.0823 | 0.0128 | 0.1738 | 0.0241 | 0.2192 | | 3.0429 | 24.9953 | 2662 | 2.8145 | 0.0577 | 0.1646 | 0.0351 | 0.0084 | 0.0234 | 0.0838 | 0.0969 | 0.2569 | 0.2758 | 0.0238 | 0.167 | 0.4128 | 0.1906 | 0.5646 | 0.0341 | 0.2957 | 0.0068 | 0.0885 | 0.0075 | 0.2071 | 0.0493 | 0.2232 | | 2.4662 | 26.0 | 2769 | 2.2661 | 0.0914 | 0.2143 | 0.0758 | 0.0178 | 0.0174 | 0.1372 | 0.127 | 0.2461 | 0.2609 | 0.0259 | 0.167 | 0.3821 | 0.3735 | 0.5579 | 0.0195 | 0.2086 | 0.0065 | 0.0958 | 0.0086 | 0.2071 | 0.0491 | 0.235 | | 2.1285 | 26.9953 | 2875 | 2.2159 | 0.0968 | 0.2383 | 0.0584 | 0.0347 | 0.0279 | 0.1258 | 0.1285 | 0.2658 | 0.2884 | 0.0488 | 0.1964 | 0.4154 | 0.374 | 0.5579 | 0.0305 | 0.28 | 0.0103 | 0.1161 | 0.0161 | 0.2643 | 0.053 | 0.2237 | | 2.1347 | 28.0 | 2982 | 2.5509 | 0.0706 | 0.1836 | 0.0441 | 0.0144 | 0.0222 | 0.1024 | 0.1252 | 0.2744 | 0.2974 | 0.033 | 0.2016 | 0.403 | 0.253 | 0.5677 | 0.0291 | 0.26 | 0.0164 | 0.138 | 0.0144 | 0.2833 | 0.0399 | 0.2379 | | 2.3668 | 28.9953 | 3088 | 2.4054 | 0.0966 | 0.2499 | 0.0643 | 0.0773 | 0.0414 | 0.1119 | 0.1484 | 0.2965 | 0.3169 | 0.1885 | 0.2178 | 0.4012 | 0.3196 | 0.5811 | 0.0377 | 0.2757 | 0.0233 | 0.1432 | 0.0304 | 0.2905 | 0.0722 | 0.2938 | | 2.2677 | 30.0 | 3195 | 2.1192 | 0.1068 | 0.2461 | 0.0794 | 0.036 | 0.0276 | 0.1437 | 0.1434 | 0.3102 | 0.3274 | 0.1926 | 0.2175 | 0.4496 | 0.3972 | 0.5927 | 0.0419 | 0.3186 | 0.0113 | 0.1453 | 0.0285 | 0.2976 | 0.055 | 0.2831 | | 2.2587 | 30.9953 | 3301 | 2.6246 | 0.0757 | 0.2145 | 0.0386 | 0.0579 | 0.0372 | 0.0801 | 0.1214 | 0.2851 | 0.3075 | 0.2843 | 0.2145 | 0.3503 | 0.2378 | 0.536 | 0.0198 | 0.2543 | 0.0159 | 0.15 | 0.0357 | 0.3048 | 0.0695 | 0.2927 | | 2.7778 | 32.0 | 3408 | 2.6193 | 0.0877 | 0.226 | 0.0504 | 0.0423 | 0.0431 | 0.0964 | 0.1302 | 0.2978 | 0.3203 | 0.1287 | 0.232 | 0.3622 | 0.2707 | 0.5768 | 0.0352 | 0.2486 | 0.0169 | 0.1594 | 0.0482 | 0.3167 | 0.0675 | 0.3 | | 2.2752 | 32.9953 | 3514 | 2.2670 | 0.1165 | 0.286 | 0.0836 | 0.0218 | 0.0533 | 0.1523 | 0.1711 | 0.3059 | 0.3261 | 0.0799 | 0.2451 | 0.4439 | 0.4075 | 0.5909 | 0.0485 | 0.2957 | 0.0223 | 0.1656 | 0.0207 | 0.2857 | 0.0834 | 0.2927 | | 2.1109 | 34.0 | 3621 | 2.1783 | 0.1182 | 0.2898 | 0.0878 | 0.0207 | 0.0406 | 0.1695 | 0.1547 | 0.3043 | 0.3242 | 0.0858 | 0.2155 | 0.4605 | 0.402 | 0.5762 | 0.0411 | 0.2757 | 0.0214 | 0.1807 | 0.0475 | 0.3 | 0.079 | 0.2881 | | 2.22 | 34.9953 | 3727 | 2.2399 | 0.1203 | 0.2683 | 0.1016 | 0.0223 | 0.0404 | 0.1578 | 0.1656 | 0.323 | 0.3393 | 0.2078 | 0.2267 | 0.4161 | 0.4218 | 0.6262 | 0.0401 | 0.3057 | 0.0241 | 0.1609 | 0.0327 | 0.3143 | 0.0826 | 0.2893 | | 2.5775 | 36.0 | 3834 | 3.4084 | 0.0927 | 0.2324 | 0.0649 | 0.022 | 0.0379 | 0.115 | 0.1397 | 0.278 | 0.298 | 0.093 | 0.2007 | 0.3901 | 0.2973 | 0.5427 | 0.0292 | 0.2414 | 0.0228 | 0.125 | 0.0481 | 0.3 | 0.0661 | 0.2808 | | 2.9257 | 36.9953 | 3940 | 2.5430 | 0.1051 | 0.2535 | 0.0731 | 0.0469 | 0.0389 | 0.1393 | 0.1607 | 0.317 | 0.3411 | 0.1976 | 0.2365 | 0.4454 | 0.3591 | 0.5909 | 0.0259 | 0.2857 | 0.0213 | 0.2104 | 0.0442 | 0.3262 | 0.075 | 0.2921 | | 2.1766 | 38.0 | 4047 | 2.2615 | 0.1232 | 0.2757 | 0.1013 | 0.0573 | 0.0413 | 0.154 | 0.1625 | 0.3287 | 0.3527 | 0.2073 | 0.24 | 0.4692 | 0.4301 | 0.6073 | 0.0324 | 0.3143 | 0.0249 | 0.2042 | 0.0528 | 0.3357 | 0.0756 | 0.3023 | | 2.2174 | 38.9953 | 4153 | 2.1064 | 0.139 | 0.3199 | 0.1186 | 0.0442 | 0.0476 | 0.1704 | 0.1744 | 0.3165 | 0.3317 | 0.2105 | 0.2171 | 0.4155 | 0.4647 | 0.6122 | 0.0287 | 0.2343 | 0.051 | 0.2016 | 0.05 | 0.3071 | 0.1007 | 0.3034 | | 2.0069 | 40.0 | 4260 | 2.1137 | 0.1274 | 0.2796 | 0.1086 | 0.0711 | 0.0423 | 0.1375 | 0.1705 | 0.3207 | 0.3397 | 0.1597 | 0.225 | 0.4317 | 0.4744 | 0.6293 | 0.0293 | 0.2443 | 0.0292 | 0.2146 | 0.021 | 0.3048 | 0.0831 | 0.3056 | | 2.0335 | 40.9953 | 4366 | 2.0880 | 0.1278 | 0.2884 | 0.1103 | 0.0374 | 0.0416 | 0.1395 | 0.1631 | 0.3068 | 0.3302 | 0.1615 | 0.2089 | 0.4282 | 0.4782 | 0.628 | 0.0342 | 0.2357 | 0.0179 | 0.1745 | 0.0397 | 0.3571 | 0.0689 | 0.2554 | | 2.1166 | 42.0 | 4473 | 2.1062 | 0.1303 | 0.299 | 0.1098 | 0.0324 | 0.0445 | 0.1594 | 0.1709 | 0.3222 | 0.3467 | 0.1433 | 0.2403 | 0.4547 | 0.45 | 0.6012 | 0.0586 | 0.2814 | 0.0363 | 0.2208 | 0.0355 | 0.3381 | 0.0708 | 0.2921 | | 1.9831 | 42.9953 | 4579 | 2.0591 | 0.142 | 0.3266 | 0.1181 | 0.0548 | 0.0532 | 0.166 | 0.1881 | 0.3438 | 0.3595 | 0.1447 | 0.249 | 0.4546 | 0.4789 | 0.6268 | 0.0469 | 0.2686 | 0.0471 | 0.2255 | 0.0494 | 0.3476 | 0.0877 | 0.3288 | | 2.0249 | 44.0 | 4686 | 2.1750 | 0.1331 | 0.3023 | 0.108 | 0.0487 | 0.0453 | 0.1746 | 0.1843 | 0.3345 | 0.3565 | 0.1692 | 0.2419 | 0.449 | 0.4406 | 0.6226 | 0.065 | 0.3257 | 0.0396 | 0.2234 | 0.0421 | 0.3119 | 0.0783 | 0.2989 | | 2.1806 | 44.9953 | 4792 | 2.1668 | 0.1299 | 0.3103 | 0.1009 | 0.0523 | 0.0368 | 0.1587 | 0.1604 | 0.3055 | 0.3313 | 0.1054 | 0.2044 | 0.4652 | 0.4617 | 0.6165 | 0.0563 | 0.27 | 0.0277 | 0.2089 | 0.0341 | 0.2833 | 0.0697 | 0.278 | | 1.9865 | 46.0 | 4899 | 2.1022 | 0.1337 | 0.3124 | 0.1006 | 0.0331 | 0.0573 | 0.1585 | 0.177 | 0.332 | 0.3522 | 0.121 | 0.2542 | 0.4359 | 0.451 | 0.6159 | 0.0228 | 0.2386 | 0.0341 | 0.2339 | 0.0467 | 0.3524 | 0.1137 | 0.3203 | | 2.0517 | 46.9953 | 5005 | 2.0253 | 0.1437 | 0.3449 | 0.1205 | 0.038 | 0.058 | 0.1937 | 0.1831 | 0.3327 | 0.3535 | 0.1973 | 0.2473 | 0.4626 | 0.4515 | 0.6201 | 0.0507 | 0.2729 | 0.0345 | 0.2255 | 0.0846 | 0.3452 | 0.0969 | 0.304 | | 1.8315 | 48.0 | 5112 | 1.9870 | 0.1528 | 0.3572 | 0.1134 | 0.0298 | 0.0525 | 0.2276 | 0.1953 | 0.3464 | 0.3668 | 0.1622 | 0.2418 | 0.5026 | 0.4801 | 0.6305 | 0.0633 | 0.2929 | 0.0491 | 0.2354 | 0.0861 | 0.3714 | 0.0855 | 0.304 | | 1.8105 | 48.9953 | 5218 | 1.9702 | 0.1436 | 0.3227 | 0.1212 | 0.0437 | 0.0429 | 0.2046 | 0.1911 | 0.3314 | 0.3517 | 0.1959 | 0.2283 | 0.4668 | 0.4903 | 0.6372 | 0.0616 | 0.27 | 0.0348 | 0.2479 | 0.066 | 0.331 | 0.0653 | 0.2723 | | 1.7928 | 50.0 | 5325 | 1.9007 | 0.1549 | 0.3631 | 0.1243 | 0.0413 | 0.0563 | 0.2183 | 0.2026 | 0.3488 | 0.369 | 0.13 | 0.2439 | 0.4907 | 0.4838 | 0.6476 | 0.0679 | 0.2914 | 0.0585 | 0.2495 | 0.0713 | 0.3571 | 0.0931 | 0.2994 | | 1.7696 | 50.9953 | 5431 | 1.9786 | 0.1511 | 0.3631 | 0.1196 | 0.0232 | 0.0595 | 0.2346 | 0.204 | 0.3601 | 0.3848 | 0.1566 | 0.2821 | 0.4908 | 0.4767 | 0.6287 | 0.0758 | 0.3171 | 0.0484 | 0.25 | 0.0748 | 0.4214 | 0.0801 | 0.3068 | | 1.7579 | 52.0 | 5538 | 1.9172 | 0.1628 | 0.3659 | 0.132 | 0.0341 | 0.0698 | 0.2224 | 0.2133 | 0.3753 | 0.3968 | 0.2334 | 0.2767 | 0.5034 | 0.498 | 0.6524 | 0.0869 | 0.3343 | 0.0472 | 0.2646 | 0.0795 | 0.4024 | 0.1023 | 0.3305 | | 1.7493 | 52.9953 | 5644 | 1.8806 | 0.1599 | 0.3797 | 0.1159 | 0.0522 | 0.0727 | 0.2175 | 0.2111 | 0.3677 | 0.3969 | 0.2264 | 0.2769 | 0.5069 | 0.4723 | 0.6482 | 0.0713 | 0.3314 | 0.0662 | 0.2792 | 0.066 | 0.3833 | 0.1239 | 0.3424 | | 1.7134 | 54.0 | 5751 | 1.9045 | 0.1663 | 0.3599 | 0.1239 | 0.0372 | 0.0754 | 0.2379 | 0.2115 | 0.3648 | 0.3911 | 0.1374 | 0.2659 | 0.522 | 0.4841 | 0.6476 | 0.0674 | 0.2829 | 0.0588 | 0.2807 | 0.1056 | 0.4095 | 0.1155 | 0.335 | | 1.7611 | 54.9953 | 5857 | 1.9231 | 0.1606 | 0.3967 | 0.1162 | 0.0462 | 0.0722 | 0.2079 | 0.2059 | 0.3553 | 0.3789 | 0.1245 | 0.2652 | 0.4963 | 0.4928 | 0.639 | 0.0981 | 0.3429 | 0.0482 | 0.2406 | 0.0709 | 0.3643 | 0.093 | 0.3079 | | 1.7949 | 56.0 | 5964 | 1.8811 | 0.182 | 0.4156 | 0.1361 | 0.0724 | 0.0814 | 0.2389 | 0.2195 | 0.3801 | 0.3956 | 0.1577 | 0.2722 | 0.4984 | 0.4928 | 0.6366 | 0.1112 | 0.3386 | 0.0831 | 0.2812 | 0.1114 | 0.4119 | 0.1113 | 0.3096 | | 1.7328 | 56.9953 | 6070 | 1.8393 | 0.1802 | 0.4138 | 0.143 | 0.0649 | 0.0869 | 0.2511 | 0.2184 | 0.3899 | 0.4079 | 0.2281 | 0.2913 | 0.5215 | 0.502 | 0.6512 | 0.1311 | 0.3471 | 0.0729 | 0.263 | 0.0834 | 0.4357 | 0.1115 | 0.3424 | | 1.6581 | 58.0 | 6177 | 1.8911 | 0.1818 | 0.4149 | 0.1458 | 0.0567 | 0.0851 | 0.254 | 0.2101 | 0.3745 | 0.3931 | 0.1613 | 0.266 | 0.5025 | 0.4957 | 0.6512 | 0.1287 | 0.3443 | 0.0735 | 0.2505 | 0.083 | 0.3905 | 0.1282 | 0.3288 | | 1.6535 | 58.9953 | 6283 | 1.8845 | 0.1712 | 0.4046 | 0.1308 | 0.0742 | 0.0783 | 0.2418 | 0.2009 | 0.3816 | 0.3972 | 0.2855 | 0.2882 | 0.4836 | 0.5077 | 0.6518 | 0.0892 | 0.3229 | 0.0578 | 0.2589 | 0.0898 | 0.4286 | 0.1113 | 0.3237 | | 1.6606 | 60.0 | 6390 | 1.9009 | 0.1779 | 0.4172 | 0.1297 | 0.0798 | 0.0946 | 0.2376 | 0.2137 | 0.3647 | 0.3862 | 0.235 | 0.2811 | 0.4988 | 0.4882 | 0.6354 | 0.0948 | 0.2729 | 0.0739 | 0.2854 | 0.1031 | 0.4 | 0.1297 | 0.3373 | | 1.6766 | 60.9953 | 6496 | 1.9100 | 0.18 | 0.4238 | 0.1385 | 0.092 | 0.087 | 0.2338 | 0.2233 | 0.384 | 0.3989 | 0.2325 | 0.2852 | 0.4949 | 0.4817 | 0.636 | 0.0971 | 0.3171 | 0.0831 | 0.2714 | 0.1194 | 0.4429 | 0.1185 | 0.3271 | | 1.7227 | 62.0 | 6603 | 1.8943 | 0.173 | 0.3979 | 0.1294 | 0.0935 | 0.0739 | 0.2254 | 0.2172 | 0.3674 | 0.389 | 0.1763 | 0.2647 | 0.5074 | 0.5011 | 0.6463 | 0.1096 | 0.3286 | 0.0731 | 0.2854 | 0.0727 | 0.3786 | 0.1087 | 0.3062 | | 1.6737 | 62.9953 | 6709 | 1.9753 | 0.1645 | 0.398 | 0.1159 | 0.0841 | 0.0849 | 0.2382 | 0.2065 | 0.3693 | 0.3867 | 0.2084 | 0.2771 | 0.4862 | 0.4483 | 0.6171 | 0.0934 | 0.3114 | 0.0869 | 0.3005 | 0.0684 | 0.3738 | 0.1256 | 0.3305 | | 1.6768 | 64.0 | 6816 | 1.8531 | 0.1759 | 0.401 | 0.1408 | 0.065 | 0.0851 | 0.2592 | 0.2178 | 0.3635 | 0.3872 | 0.1646 | 0.2774 | 0.5118 | 0.5142 | 0.6463 | 0.0906 | 0.3114 | 0.062 | 0.2844 | 0.0843 | 0.3571 | 0.1284 | 0.3367 | | 1.6543 | 64.9953 | 6922 | 1.8840 | 0.1828 | 0.4139 | 0.1364 | 0.0964 | 0.0979 | 0.2575 | 0.2195 | 0.3841 | 0.4027 | 0.1731 | 0.3073 | 0.504 | 0.4805 | 0.6409 | 0.1276 | 0.3529 | 0.088 | 0.2969 | 0.093 | 0.3881 | 0.1252 | 0.335 | | 1.6153 | 66.0 | 7029 | 1.9622 | 0.1684 | 0.3804 | 0.1298 | 0.0652 | 0.0743 | 0.235 | 0.2143 | 0.3699 | 0.391 | 0.1739 | 0.2697 | 0.4861 | 0.4784 | 0.6293 | 0.1049 | 0.33 | 0.0461 | 0.2745 | 0.0881 | 0.4024 | 0.1244 | 0.3186 | | 1.6143 | 66.9953 | 7135 | 1.8685 | 0.178 | 0.394 | 0.1375 | 0.0621 | 0.0948 | 0.2429 | 0.2178 | 0.3883 | 0.4114 | 0.2767 | 0.2987 | 0.5148 | 0.5007 | 0.6372 | 0.113 | 0.3529 | 0.0771 | 0.2932 | 0.0741 | 0.4238 | 0.1252 | 0.3497 | | 1.6326 | 68.0 | 7242 | 1.8852 | 0.1745 | 0.3983 | 0.1289 | 0.0568 | 0.0764 | 0.23 | 0.2163 | 0.3856 | 0.4044 | 0.2097 | 0.2825 | 0.5231 | 0.5052 | 0.6482 | 0.1178 | 0.3514 | 0.0672 | 0.2849 | 0.0624 | 0.3905 | 0.1196 | 0.3469 | | 1.6507 | 68.9953 | 7348 | 1.8130 | 0.1821 | 0.4003 | 0.1556 | 0.06 | 0.1034 | 0.2663 | 0.2157 | 0.3933 | 0.4077 | 0.3075 | 0.2933 | 0.5294 | 0.5087 | 0.6579 | 0.1021 | 0.3529 | 0.0749 | 0.2969 | 0.0992 | 0.4024 | 0.1256 | 0.3282 | | 1.5577 | 70.0 | 7455 | 1.8646 | 0.1775 | 0.3934 | 0.1375 | 0.0802 | 0.0721 | 0.2704 | 0.2248 | 0.386 | 0.4115 | 0.2884 | 0.2959 | 0.5086 | 0.5029 | 0.6518 | 0.0973 | 0.3357 | 0.0637 | 0.2979 | 0.0962 | 0.4262 | 0.1273 | 0.3458 | | 1.5784 | 70.9953 | 7561 | 1.7817 | 0.1912 | 0.4163 | 0.1575 | 0.0783 | 0.0846 | 0.2848 | 0.229 | 0.3986 | 0.4184 | 0.3431 | 0.2889 | 0.532 | 0.5187 | 0.6695 | 0.1152 | 0.3429 | 0.0723 | 0.3005 | 0.0953 | 0.4214 | 0.1546 | 0.3576 | | 1.506 | 72.0 | 7668 | 1.7696 | 0.1938 | 0.4236 | 0.1464 | 0.0803 | 0.1105 | 0.2592 | 0.2337 | 0.3895 | 0.4151 | 0.1821 | 0.2993 | 0.5415 | 0.5214 | 0.6634 | 0.0966 | 0.34 | 0.0775 | 0.2932 | 0.1302 | 0.4262 | 0.1434 | 0.3525 | | 1.5384 | 72.9953 | 7774 | 1.8206 | 0.189 | 0.4304 | 0.1388 | 0.0836 | 0.0839 | 0.2802 | 0.2353 | 0.3889 | 0.4041 | 0.2535 | 0.272 | 0.5363 | 0.5046 | 0.6591 | 0.109 | 0.3357 | 0.0785 | 0.2922 | 0.1227 | 0.4 | 0.1301 | 0.3333 | | 1.5022 | 74.0 | 7881 | 1.8055 | 0.197 | 0.4341 | 0.1495 | 0.1022 | 0.0883 | 0.2747 | 0.2256 | 0.3898 | 0.4084 | 0.1913 | 0.2789 | 0.5424 | 0.5225 | 0.661 | 0.1332 | 0.34 | 0.0687 | 0.2891 | 0.1366 | 0.4286 | 0.1239 | 0.3232 | | 1.5157 | 74.9953 | 7987 | 1.7750 | 0.1992 | 0.4404 | 0.1541 | 0.104 | 0.0937 | 0.2516 | 0.2332 | 0.3996 | 0.4154 | 0.1952 | 0.2843 | 0.5587 | 0.5311 | 0.6762 | 0.1331 | 0.36 | 0.0869 | 0.2964 | 0.1116 | 0.4071 | 0.1332 | 0.3373 | | 1.4439 | 76.0 | 8094 | 1.8431 | 0.1877 | 0.4295 | 0.1374 | 0.0729 | 0.0844 | 0.2654 | 0.225 | 0.3975 | 0.4176 | 0.2597 | 0.2999 | 0.544 | 0.5215 | 0.6579 | 0.1322 | 0.3471 | 0.0673 | 0.2875 | 0.0936 | 0.4643 | 0.1241 | 0.3311 | | 1.4989 | 76.9953 | 8200 | 1.8236 | 0.1907 | 0.4343 | 0.1425 | 0.055 | 0.095 | 0.2776 | 0.2183 | 0.3861 | 0.4059 | 0.1827 | 0.2745 | 0.5308 | 0.5238 | 0.6634 | 0.1084 | 0.33 | 0.0754 | 0.2849 | 0.1091 | 0.4 | 0.1368 | 0.3514 | | 1.4759 | 78.0 | 8307 | 1.7953 | 0.1973 | 0.4484 | 0.15 | 0.077 | 0.0949 | 0.2868 | 0.2369 | 0.402 | 0.4161 | 0.1753 | 0.2936 | 0.5428 | 0.5239 | 0.6634 | 0.124 | 0.3514 | 0.0865 | 0.3109 | 0.0945 | 0.3976 | 0.1574 | 0.3571 | | 1.4302 | 78.9953 | 8413 | 1.8257 | 0.1921 | 0.4446 | 0.1476 | 0.0569 | 0.1067 | 0.2706 | 0.2263 | 0.3944 | 0.4135 | 0.17 | 0.2982 | 0.5529 | 0.508 | 0.6591 | 0.1226 | 0.36 | 0.0782 | 0.3021 | 0.1114 | 0.3976 | 0.1403 | 0.3486 | | 1.4879 | 80.0 | 8520 | 1.8216 | 0.1977 | 0.4478 | 0.1511 | 0.0607 | 0.1093 | 0.28 | 0.233 | 0.4025 | 0.4213 | 0.1947 | 0.3143 | 0.5392 | 0.5125 | 0.6573 | 0.1274 | 0.3514 | 0.087 | 0.3177 | 0.1197 | 0.4286 | 0.1421 | 0.3514 | | 1.4674 | 80.9953 | 8626 | 1.8194 | 0.186 | 0.4463 | 0.1374 | 0.056 | 0.0953 | 0.28 | 0.2289 | 0.3882 | 0.4068 | 0.1807 | 0.2878 | 0.5349 | 0.5061 | 0.6457 | 0.1236 | 0.3371 | 0.0662 | 0.276 | 0.0917 | 0.4143 | 0.1424 | 0.361 | | 1.4603 | 82.0 | 8733 | 1.7888 | 0.1925 | 0.4437 | 0.142 | 0.0727 | 0.0973 | 0.2871 | 0.2257 | 0.39 | 0.4054 | 0.1866 | 0.2849 | 0.5352 | 0.5014 | 0.6463 | 0.1343 | 0.33 | 0.0787 | 0.2995 | 0.1028 | 0.4 | 0.1455 | 0.3514 | | 1.4798 | 82.9953 | 8839 | 1.8245 | 0.1922 | 0.4473 | 0.1358 | 0.0696 | 0.1126 | 0.2677 | 0.2258 | 0.3993 | 0.415 | 0.1864 | 0.3067 | 0.5338 | 0.5 | 0.6427 | 0.1361 | 0.3314 | 0.0868 | 0.3073 | 0.0965 | 0.4286 | 0.1414 | 0.365 | | 1.4253 | 84.0 | 8946 | 1.7753 | 0.1932 | 0.4601 | 0.1409 | 0.0801 | 0.1169 | 0.2778 | 0.2316 | 0.4027 | 0.4172 | 0.1949 | 0.3178 | 0.5316 | 0.5108 | 0.6567 | 0.124 | 0.3329 | 0.0831 | 0.2937 | 0.0979 | 0.4452 | 0.1501 | 0.3576 | | 1.4397 | 84.9953 | 9052 | 1.7778 | 0.2023 | 0.4698 | 0.1484 | 0.0747 | 0.1264 | 0.2903 | 0.2312 | 0.4076 | 0.423 | 0.2089 | 0.3229 | 0.5092 | 0.5011 | 0.6494 | 0.1172 | 0.3129 | 0.107 | 0.3214 | 0.1282 | 0.45 | 0.158 | 0.3814 | | 1.4086 | 86.0 | 9159 | 1.7550 | 0.2015 | 0.472 | 0.1626 | 0.0926 | 0.1138 | 0.2819 | 0.2308 | 0.4085 | 0.4275 | 0.2033 | 0.3209 | 0.5665 | 0.5092 | 0.6591 | 0.1319 | 0.3543 | 0.0894 | 0.3042 | 0.1295 | 0.4571 | 0.1473 | 0.3627 | | 1.4261 | 86.9953 | 9265 | 1.7907 | 0.1997 | 0.4662 | 0.1461 | 0.1078 | 0.1194 | 0.2946 | 0.2402 | 0.3993 | 0.4178 | 0.1766 | 0.3128 | 0.5568 | 0.5156 | 0.661 | 0.1338 | 0.3371 | 0.0888 | 0.301 | 0.1181 | 0.431 | 0.1423 | 0.3588 | | 1.3943 | 88.0 | 9372 | 1.7906 | 0.1891 | 0.4431 | 0.133 | 0.0591 | 0.1053 | 0.2925 | 0.234 | 0.4004 | 0.419 | 0.1942 | 0.3249 | 0.5366 | 0.5019 | 0.6488 | 0.1097 | 0.3271 | 0.0877 | 0.3052 | 0.1014 | 0.4524 | 0.1446 | 0.3616 | | 1.4016 | 88.9953 | 9478 | 1.7760 | 0.1929 | 0.4462 | 0.1424 | 0.0828 | 0.1114 | 0.2675 | 0.238 | 0.4045 | 0.4254 | 0.2069 | 0.3236 | 0.5476 | 0.5031 | 0.6616 | 0.121 | 0.3329 | 0.0897 | 0.3104 | 0.098 | 0.4548 | 0.1525 | 0.3672 | | 1.3955 | 90.0 | 9585 | 1.7786 | 0.1955 | 0.4452 | 0.1534 | 0.0952 | 0.1147 | 0.2789 | 0.2383 | 0.3979 | 0.4189 | 0.2118 | 0.3166 | 0.5502 | 0.4977 | 0.6378 | 0.1173 | 0.3286 | 0.0946 | 0.312 | 0.1176 | 0.4405 | 0.1502 | 0.3757 | | 1.4014 | 90.9953 | 9691 | 1.7644 | 0.1975 | 0.4627 | 0.1508 | 0.0865 | 0.1134 | 0.2852 | 0.2394 | 0.4042 | 0.4219 | 0.1963 | 0.3221 | 0.5521 | 0.5035 | 0.6549 | 0.1326 | 0.3371 | 0.0991 | 0.3193 | 0.1154 | 0.4429 | 0.1367 | 0.3554 | | 1.3626 | 92.0 | 9798 | 1.7705 | 0.1993 | 0.4752 | 0.143 | 0.07 | 0.1152 | 0.2855 | 0.2361 | 0.4022 | 0.4221 | 0.1801 | 0.3227 | 0.5575 | 0.5064 | 0.6555 | 0.1307 | 0.3486 | 0.0898 | 0.299 | 0.1215 | 0.4429 | 0.1481 | 0.3644 | | 1.3655 | 92.9953 | 9904 | 1.7689 | 0.2081 | 0.4735 | 0.1425 | 0.0975 | 0.1234 | 0.2855 | 0.2413 | 0.4007 | 0.422 | 0.198 | 0.3091 | 0.5544 | 0.5014 | 0.6616 | 0.1282 | 0.3386 | 0.0918 | 0.3073 | 0.1587 | 0.4381 | 0.1603 | 0.3644 | | 1.3913 | 94.0 | 10011 | 1.7834 | 0.2003 | 0.4624 | 0.149 | 0.0951 | 0.1082 | 0.2823 | 0.2407 | 0.4025 | 0.4227 | 0.2077 | 0.2991 | 0.5416 | 0.4993 | 0.6628 | 0.1198 | 0.3371 | 0.1071 | 0.3208 | 0.1209 | 0.419 | 0.1545 | 0.3734 | | 1.4071 | 94.9953 | 10117 | 1.7609 | 0.2046 | 0.4761 | 0.1521 | 0.0954 | 0.1232 | 0.2968 | 0.2392 | 0.4064 | 0.4251 | 0.1986 | 0.3225 | 0.5536 | 0.4975 | 0.6579 | 0.1359 | 0.3614 | 0.1034 | 0.3068 | 0.1342 | 0.4238 | 0.1522 | 0.3757 | | 1.3651 | 96.0 | 10224 | 1.7628 | 0.2016 | 0.4717 | 0.1487 | 0.0995 | 0.1203 | 0.2895 | 0.2381 | 0.3965 | 0.416 | 0.1919 | 0.3111 | 0.5508 | 0.5079 | 0.664 | 0.1225 | 0.3371 | 0.0941 | 0.3005 | 0.1301 | 0.4095 | 0.1533 | 0.3689 | | 1.3568 | 96.9953 | 10330 | 1.7858 | 0.2008 | 0.4706 | 0.15 | 0.0885 | 0.1223 | 0.286 | 0.2415 | 0.3952 | 0.4149 | 0.2044 | 0.31 | 0.5375 | 0.5041 | 0.6604 | 0.1152 | 0.3214 | 0.0946 | 0.3052 | 0.135 | 0.4214 | 0.1552 | 0.3661 | | 1.3502 | 98.0 | 10437 | 1.7613 | 0.2041 | 0.4599 | 0.1525 | 0.0904 | 0.1249 | 0.2908 | 0.2395 | 0.4097 | 0.4292 | 0.2154 | 0.3233 | 0.553 | 0.5051 | 0.664 | 0.127 | 0.3557 | 0.1024 | 0.3177 | 0.1333 | 0.4357 | 0.1528 | 0.3729 | | 1.3658 | 98.9953 | 10543 | 1.7623 | 0.2016 | 0.4641 | 0.1501 | 0.0908 | 0.1219 | 0.2895 | 0.2398 | 0.4019 | 0.4208 | 0.2099 | 0.3157 | 0.5456 | 0.5034 | 0.6598 | 0.1234 | 0.3314 | 0.095 | 0.3115 | 0.1326 | 0.4262 | 0.1536 | 0.3751 | | 1.3272 | 99.5305 | 10600 | 1.7663 | 0.2022 | 0.4588 | 0.1509 | 0.0948 | 0.1223 | 0.2882 | 0.2396 | 0.405 | 0.4238 | 0.2134 | 0.3177 | 0.5501 | 0.5051 | 0.6628 | 0.1207 | 0.3371 | 0.0983 | 0.3115 | 0.1325 | 0.431 | 0.1545 | 0.3768 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6

# detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session6) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]

henrik895/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7604 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.6061 | 0.4 | 500 | 2.5979 | | 2.2323 | 0.8 | 1000 | 2.0724 | | 1.9294 | 1.2 | 1500 | 1.8745 | | 1.8162 | 1.6 | 2000 | 1.7907 | | 1.7529 | 2.0 | 2500 | 1.7604 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accesories", "bags", "clothing", "shoes" ]

KevinLe/detr_output

# detr_output This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9858 | 6.37 | 1000 | 0.9322 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

myshkin/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.5674 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7444 | 1.0 | 1250 | 5.5674 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

vision-tf/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8164 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.05 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2214 | 0.01 | 25 | 2.3644 | | 2.21 | 0.01 | 50 | 2.1607 | | 2.0636 | 0.01 | 75 | 2.1884 | | 2.1722 | 0.02 | 100 | 2.3081 | | 2.3687 | 0.03 | 125 | 1.9743 | | 2.0154 | 0.03 | 150 | 1.9790 | | 1.8828 | 0.04 | 175 | 1.9207 | | 1.9719 | 0.04 | 200 | 1.8296 | | 1.7973 | 0.04 | 225 | 1.8404 | | 1.687 | 0.05 | 250 | 1.8164 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

cheesebird/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2294 | 0.1 | 250 | 1.5375 | | 1.4499 | 0.2 | 500 | 1.3111 | | 1.2584 | 0.3 | 750 | 1.1323 | | 1.1306 | 0.4 | 1000 | 1.0789 | | 1.1112 | 0.5 | 1250 | 1.0349 | | 1.0478 | 0.6 | 1500 | 0.9710 | | 0.9709 | 0.7 | 1750 | 0.9510 | | 0.96 | 0.8 | 2000 | 0.9061 | | 0.9417 | 0.9 | 2250 | 0.8761 | | 0.939 | 1.0 | 2500 | 0.8684 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

AnettSand/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0269 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4399 | 0.64 | 100 | 1.5428 | | 1.4239 | 1.27 | 200 | 1.2347 | | 1.2024 | 1.91 | 300 | 1.0768 | | 1.099 | 2.55 | 400 | 1.0269 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u2o06hbj) # hustvl-yolos-small-finetuned-10k-cppe5 This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.4674 - Map: 0.3363 - Map 50: 0.6506 - Map 75: 0.2992 - Map Small: 0.2695 - Map Medium: 0.2282 - Map Large: 0.4791 - Mar 1: 0.3441 - Mar 10: 0.4988 - Mar 100: 0.5186 - Mar Small: 0.3192 - Mar Medium: 0.3884 - Mar Large: 0.6982 - Map Coverall: 0.607 - Mar 100 Coverall: 0.7716 - Map Face Shield: 0.3854 - Mar 100 Face Shield: 0.5883 - Map Gloves: 0.2283 - Mar 100 Gloves: 0.4093 - Map Goggles: 0.1228 - Mar 100 Goggles: 0.3319 - Map Mask: 0.3379 - Mar 100 Mask: 0.4916 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 1.9479 | 1.0 | 107 | 1.6080 | 0.15 | 0.3271 | 0.1142 | 0.0227 | 0.0603 | 0.1779 | 0.1663 | 0.3089 | 0.3414 | 0.098 | 0.2108 | 0.3973 | 0.499 | 0.7191 | 0.048 | 0.2467 | 0.0276 | 0.3377 | 0.0156 | 0.0764 | 0.1598 | 0.3268 | | 1.4786 | 2.0 | 214 | 1.4219 | 0.1899 | 0.3864 | 0.1627 | 0.0539 | 0.0911 | 0.2756 | 0.2423 | 0.4081 | 0.4299 | 0.0864 | 0.3003 | 0.5424 | 0.5552 | 0.7951 | 0.1065 | 0.4933 | 0.0707 | 0.326 | 0.042 | 0.2028 | 0.1752 | 0.3324 | | 1.3842 | 3.0 | 321 | 1.3797 | 0.2174 | 0.4379 | 0.1755 | 0.0584 | 0.1132 | 0.326 | 0.2616 | 0.4277 | 0.4505 | 0.2266 | 0.3179 | 0.61 | 0.5588 | 0.7698 | 0.1542 | 0.475 | 0.0952 | 0.35 | 0.0469 | 0.2764 | 0.2321 | 0.3816 | | 1.2986 | 4.0 | 428 | 1.3926 | 0.1978 | 0.4118 | 0.1625 | 0.0355 | 0.0902 | 0.3168 | 0.2456 | 0.4139 | 0.4312 | 0.181 | 0.3023 | 0.5948 | 0.56 | 0.7568 | 0.1293 | 0.455 | 0.1054 | 0.3574 | 0.0389 | 0.2681 | 0.1553 | 0.319 | | 1.233 | 5.0 | 535 | 1.3301 | 0.2271 | 0.5034 | 0.1694 | 0.0505 | 0.1223 | 0.3357 | 0.2595 | 0.4181 | 0.4355 | 0.205 | 0.304 | 0.5722 | 0.5531 | 0.766 | 0.2005 | 0.475 | 0.1371 | 0.3647 | 0.0537 | 0.2486 | 0.1912 | 0.3229 | | 1.1644 | 6.0 | 642 | 1.2556 | 0.2497 | 0.5164 | 0.1981 | 0.0579 | 0.1447 | 0.3657 | 0.2915 | 0.4434 | 0.4598 | 0.1981 | 0.3224 | 0.6256 | 0.5888 | 0.7753 | 0.1927 | 0.4933 | 0.1338 | 0.373 | 0.0736 | 0.2667 | 0.2597 | 0.3905 | | 1.1229 | 7.0 | 749 | 1.2388 | 0.234 | 0.4963 | 0.1996 | 0.1279 | 0.1367 | 0.3492 | 0.2638 | 0.4433 | 0.4576 | 0.2508 | 0.3912 | 0.6242 | 0.5616 | 0.7895 | 0.1503 | 0.4883 | 0.1564 | 0.373 | 0.043 | 0.2736 | 0.2589 | 0.3637 | | 1.0939 | 8.0 | 856 | 1.2988 | 0.2343 | 0.5195 | 0.1773 | 0.0506 | 0.1319 | 0.3447 | 0.2687 | 0.3976 | 0.4065 | 0.1377 | 0.2645 | 0.5857 | 0.5806 | 0.7494 | 0.1693 | 0.4083 | 0.1114 | 0.3059 | 0.0683 | 0.2069 | 0.2419 | 0.362 | | 1.0571 | 9.0 | 963 | 1.2067 | 0.2604 | 0.5346 | 0.2265 | 0.0671 | 0.1565 | 0.383 | 0.3001 | 0.4545 | 0.4655 | 0.1538 | 0.3488 | 0.625 | 0.6037 | 0.7586 | 0.2 | 0.49 | 0.1749 | 0.3858 | 0.0758 | 0.3097 | 0.2477 | 0.3832 | | 1.0535 | 10.0 | 1070 | 1.2278 | 0.2534 | 0.5112 | 0.2324 | 0.0556 | 0.1444 | 0.3914 | 0.3011 | 0.4593 | 0.4806 | 0.1686 | 0.3462 | 0.6597 | 0.6104 | 0.7821 | 0.1993 | 0.525 | 0.1614 | 0.3956 | 0.0553 | 0.3014 | 0.2407 | 0.3989 | | 0.9948 | 11.0 | 1177 | 1.2097 | 0.2653 | 0.5317 | 0.2346 | 0.2441 | 0.1608 | 0.3892 | 0.3125 | 0.4766 | 0.4903 | 0.2854 | 0.3756 | 0.643 | 0.5968 | 0.7648 | 0.2183 | 0.5533 | 0.1797 | 0.3936 | 0.0557 | 0.3306 | 0.276 | 0.4089 | | 0.9766 | 12.0 | 1284 | 1.2409 | 0.258 | 0.5423 | 0.2228 | 0.1649 | 0.1503 | 0.3925 | 0.2986 | 0.4553 | 0.4667 | 0.2045 | 0.3461 | 0.6238 | 0.5955 | 0.7667 | 0.2124 | 0.5267 | 0.1877 | 0.3613 | 0.0559 | 0.3 | 0.2382 | 0.3788 | | 0.9642 | 13.0 | 1391 | 1.2483 | 0.2733 | 0.5497 | 0.2377 | 0.1841 | 0.1614 | 0.401 | 0.3115 | 0.4527 | 0.4643 | 0.2125 | 0.3235 | 0.6653 | 0.6113 | 0.7821 | 0.2527 | 0.55 | 0.1924 | 0.3544 | 0.0694 | 0.2639 | 0.2406 | 0.3709 | | 0.9632 | 14.0 | 1498 | 1.2315 | 0.2728 | 0.544 | 0.2363 | 0.0893 | 0.1691 | 0.3978 | 0.308 | 0.4605 | 0.4713 | 0.1705 | 0.345 | 0.6593 | 0.6124 | 0.7753 | 0.2228 | 0.4933 | 0.1615 | 0.3475 | 0.0861 | 0.325 | 0.2814 | 0.4151 | | 0.9342 | 15.0 | 1605 | 1.2185 | 0.2843 | 0.5699 | 0.2436 | 0.2202 | 0.1798 | 0.4107 | 0.313 | 0.4628 | 0.4732 | 0.2514 | 0.3365 | 0.6666 | 0.6132 | 0.7858 | 0.2452 | 0.54 | 0.1971 | 0.3578 | 0.0855 | 0.2736 | 0.2807 | 0.4089 | | 0.9118 | 16.0 | 1712 | 1.2268 | 0.2759 | 0.5763 | 0.2359 | 0.1335 | 0.1694 | 0.4276 | 0.3045 | 0.4556 | 0.4712 | 0.2254 | 0.3336 | 0.6805 | 0.6205 | 0.8 | 0.2553 | 0.53 | 0.161 | 0.349 | 0.0765 | 0.2833 | 0.2661 | 0.3939 | | 0.9031 | 17.0 | 1819 | 1.2224 | 0.2794 | 0.5673 | 0.2443 | 0.2481 | 0.1638 | 0.41 | 0.3079 | 0.4683 | 0.4888 | 0.275 | 0.3562 | 0.6834 | 0.6053 | 0.8012 | 0.2647 | 0.5367 | 0.1652 | 0.3735 | 0.1022 | 0.3542 | 0.2594 | 0.3782 | | 0.8556 | 18.0 | 1926 | 1.1809 | 0.2933 | 0.5872 | 0.265 | 0.2545 | 0.1984 | 0.3951 | 0.3132 | 0.4787 | 0.496 | 0.2908 | 0.3581 | 0.6792 | 0.5871 | 0.7981 | 0.2753 | 0.5567 | 0.1937 | 0.3971 | 0.0936 | 0.2903 | 0.3171 | 0.438 | | 0.8221 | 19.0 | 2033 | 1.2087 | 0.2939 | 0.5973 | 0.249 | 0.0952 | 0.1777 | 0.4381 | 0.312 | 0.4685 | 0.4845 | 0.133 | 0.3389 | 0.6883 | 0.6256 | 0.8111 | 0.2686 | 0.5233 | 0.1739 | 0.3603 | 0.1 | 0.3069 | 0.3015 | 0.4207 | | 0.8202 | 20.0 | 2140 | 1.2266 | 0.2934 | 0.5939 | 0.2522 | 0.1408 | 0.1867 | 0.4349 | 0.3234 | 0.4769 | 0.4945 | 0.1756 | 0.3613 | 0.6994 | 0.617 | 0.7975 | 0.3123 | 0.5533 | 0.1686 | 0.3637 | 0.0951 | 0.3486 | 0.2741 | 0.4095 | | 0.7954 | 21.0 | 2247 | 1.2556 | 0.2905 | 0.6004 | 0.2432 | 0.1632 | 0.1839 | 0.438 | 0.3135 | 0.4719 | 0.4865 | 0.2116 | 0.3531 | 0.6842 | 0.6094 | 0.7741 | 0.2976 | 0.56 | 0.1763 | 0.3574 | 0.089 | 0.3069 | 0.2802 | 0.4341 | | 0.7868 | 22.0 | 2354 | 1.2723 | 0.2717 | 0.559 | 0.2363 | 0.1494 | 0.1689 | 0.411 | 0.293 | 0.4624 | 0.4771 | 0.1901 | 0.3415 | 0.6822 | 0.5933 | 0.7735 | 0.3068 | 0.5717 | 0.1651 | 0.3358 | 0.0718 | 0.3236 | 0.2213 | 0.381 | | 0.7827 | 23.0 | 2461 | 1.2710 | 0.2957 | 0.6092 | 0.2317 | 0.2667 | 0.1878 | 0.4432 | 0.3189 | 0.4785 | 0.4928 | 0.2995 | 0.3684 | 0.6803 | 0.6028 | 0.7802 | 0.3233 | 0.5533 | 0.1795 | 0.3843 | 0.1219 | 0.3333 | 0.2511 | 0.4128 | | 0.7795 | 24.0 | 2568 | 1.2305 | 0.3039 | 0.6021 | 0.2739 | 0.2054 | 0.2003 | 0.4373 | 0.3229 | 0.4902 | 0.5068 | 0.2637 | 0.3887 | 0.6889 | 0.6232 | 0.766 | 0.2872 | 0.575 | 0.1954 | 0.3838 | 0.1011 | 0.3528 | 0.3125 | 0.4564 | | 0.7524 | 25.0 | 2675 | 1.2481 | 0.2897 | 0.5968 | 0.2423 | 0.2147 | 0.1875 | 0.4391 | 0.3168 | 0.4866 | 0.5029 | 0.2532 | 0.3815 | 0.689 | 0.598 | 0.779 | 0.2973 | 0.58 | 0.1842 | 0.3833 | 0.1061 | 0.3417 | 0.2627 | 0.4307 | | 0.7446 | 26.0 | 2782 | 1.2481 | 0.2894 | 0.5835 | 0.2479 | 0.16 | 0.182 | 0.441 | 0.3083 | 0.4857 | 0.496 | 0.1905 | 0.3783 | 0.6953 | 0.6217 | 0.7765 | 0.2919 | 0.5733 | 0.1736 | 0.3652 | 0.068 | 0.3319 | 0.292 | 0.433 | | 0.7203 | 27.0 | 2889 | 1.2720 | 0.2871 | 0.5801 | 0.2338 | 0.2113 | 0.1852 | 0.4258 | 0.3107 | 0.48 | 0.4955 | 0.2698 | 0.3781 | 0.6844 | 0.6106 | 0.7698 | 0.2779 | 0.5633 | 0.2001 | 0.3819 | 0.0842 | 0.3333 | 0.2625 | 0.4291 | | 0.7239 | 28.0 | 2996 | 1.2166 | 0.3043 | 0.621 | 0.256 | 0.1515 | 0.1926 | 0.4491 | 0.3286 | 0.4874 | 0.5037 | 0.2054 | 0.3733 | 0.6882 | 0.6196 | 0.7728 | 0.3166 | 0.58 | 0.1917 | 0.3799 | 0.095 | 0.3319 | 0.2985 | 0.4536 | | 0.6987 | 29.0 | 3103 | 1.2685 | 0.3008 | 0.6003 | 0.256 | 0.2512 | 0.1964 | 0.4285 | 0.3231 | 0.4964 | 0.5118 | 0.3015 | 0.3921 | 0.6779 | 0.6247 | 0.7716 | 0.2988 | 0.5933 | 0.1999 | 0.3819 | 0.0914 | 0.3514 | 0.2895 | 0.4609 | | 0.6776 | 30.0 | 3210 | 1.2834 | 0.2976 | 0.6053 | 0.2574 | 0.1637 | 0.1951 | 0.4457 | 0.3311 | 0.4801 | 0.4952 | 0.2704 | 0.3691 | 0.6739 | 0.6165 | 0.787 | 0.28 | 0.59 | 0.194 | 0.3652 | 0.1218 | 0.3236 | 0.2759 | 0.4101 | | 0.6695 | 31.0 | 3317 | 1.2599 | 0.2957 | 0.5942 | 0.268 | 0.1376 | 0.1935 | 0.4377 | 0.3115 | 0.4812 | 0.4988 | 0.2589 | 0.3643 | 0.6915 | 0.6154 | 0.7852 | 0.2669 | 0.585 | 0.2035 | 0.3936 | 0.0936 | 0.2917 | 0.2991 | 0.4385 | | 0.6829 | 32.0 | 3424 | 1.3085 | 0.2938 | 0.5904 | 0.2481 | 0.2294 | 0.1851 | 0.4296 | 0.311 | 0.4789 | 0.497 | 0.2771 | 0.3744 | 0.6878 | 0.622 | 0.7741 | 0.3067 | 0.5867 | 0.1709 | 0.3505 | 0.1139 | 0.3681 | 0.2557 | 0.4056 | | 0.6632 | 33.0 | 3531 | 1.2422 | 0.2996 | 0.6001 | 0.2578 | 0.1768 | 0.1909 | 0.4405 | 0.3214 | 0.4901 | 0.5081 | 0.2888 | 0.3812 | 0.6588 | 0.6126 | 0.7728 | 0.2943 | 0.5967 | 0.2104 | 0.4064 | 0.0948 | 0.3208 | 0.2858 | 0.4436 | | 0.6518 | 34.0 | 3638 | 1.2245 | 0.3093 | 0.611 | 0.2619 | 0.1701 | 0.2059 | 0.4612 | 0.3295 | 0.4808 | 0.498 | 0.2206 | 0.3699 | 0.6679 | 0.6104 | 0.7809 | 0.3062 | 0.5633 | 0.2068 | 0.3794 | 0.1203 | 0.3222 | 0.303 | 0.4441 | | 0.6649 | 35.0 | 3745 | 1.2282 | 0.3067 | 0.621 | 0.267 | 0.2252 | 0.2014 | 0.4684 | 0.3313 | 0.4843 | 0.5019 | 0.2741 | 0.3718 | 0.6808 | 0.5992 | 0.7796 | 0.3058 | 0.5617 | 0.2148 | 0.4064 | 0.1257 | 0.3194 | 0.2883 | 0.4425 | | 0.6373 | 36.0 | 3852 | 1.3044 | 0.3085 | 0.6184 | 0.2823 | 0.248 | 0.2014 | 0.4489 | 0.3186 | 0.4898 | 0.5107 | 0.2978 | 0.3889 | 0.6776 | 0.6084 | 0.7784 | 0.3151 | 0.59 | 0.2058 | 0.3985 | 0.113 | 0.3153 | 0.3 | 0.4715 | | 0.6139 | 37.0 | 3959 | 1.2725 | 0.3138 | 0.6336 | 0.2796 | 0.1862 | 0.203 | 0.4697 | 0.3262 | 0.4928 | 0.5143 | 0.2357 | 0.387 | 0.6958 | 0.6116 | 0.7802 | 0.3125 | 0.5667 | 0.2204 | 0.3961 | 0.1065 | 0.3625 | 0.3181 | 0.4659 | | 0.6087 | 38.0 | 4066 | 1.2936 | 0.3072 | 0.6272 | 0.2668 | 0.2027 | 0.2048 | 0.4465 | 0.3151 | 0.4832 | 0.5024 | 0.2461 | 0.3726 | 0.6757 | 0.6071 | 0.7981 | 0.3166 | 0.575 | 0.1965 | 0.3814 | 0.1308 | 0.3375 | 0.2848 | 0.4201 | | 0.613 | 39.0 | 4173 | 1.2992 | 0.3233 | 0.6431 | 0.3037 | 0.1967 | 0.2164 | 0.4755 | 0.3249 | 0.4887 | 0.5027 | 0.2438 | 0.3761 | 0.6875 | 0.6188 | 0.7753 | 0.332 | 0.5717 | 0.2113 | 0.3873 | 0.1314 | 0.325 | 0.3232 | 0.4542 | | 0.6009 | 40.0 | 4280 | 1.3210 | 0.3105 | 0.6141 | 0.2801 | 0.196 | 0.1976 | 0.446 | 0.3223 | 0.4704 | 0.4859 | 0.2349 | 0.3542 | 0.6723 | 0.6392 | 0.7722 | 0.3152 | 0.5433 | 0.1995 | 0.3672 | 0.0986 | 0.2847 | 0.3001 | 0.462 | | 0.5766 | 41.0 | 4387 | 1.2828 | 0.3157 | 0.6377 | 0.2786 | 0.2062 | 0.2068 | 0.4597 | 0.3266 | 0.4825 | 0.5013 | 0.2481 | 0.3722 | 0.6859 | 0.6119 | 0.7753 | 0.3294 | 0.5683 | 0.2074 | 0.4 | 0.1285 | 0.3139 | 0.3013 | 0.4492 | | 0.5692 | 42.0 | 4494 | 1.3361 | 0.3209 | 0.6257 | 0.2844 | 0.2785 | 0.2123 | 0.4655 | 0.3311 | 0.4922 | 0.5103 | 0.3317 | 0.3787 | 0.6923 | 0.6219 | 0.7802 | 0.3437 | 0.5767 | 0.1992 | 0.3897 | 0.1221 | 0.3347 | 0.3177 | 0.4704 | | 0.5563 | 43.0 | 4601 | 1.2864 | 0.3284 | 0.6405 | 0.2969 | 0.2098 | 0.2261 | 0.4568 | 0.3312 | 0.5031 | 0.5202 | 0.2639 | 0.399 | 0.6864 | 0.6278 | 0.7821 | 0.3447 | 0.5733 | 0.2249 | 0.4186 | 0.1179 | 0.3486 | 0.327 | 0.4782 | | 0.5581 | 44.0 | 4708 | 1.2884 | 0.3309 | 0.6319 | 0.3104 | 0.2022 | 0.2285 | 0.4627 | 0.337 | 0.5046 | 0.5237 | 0.268 | 0.4064 | 0.6992 | 0.6404 | 0.7944 | 0.353 | 0.595 | 0.2145 | 0.3877 | 0.1283 | 0.3639 | 0.3185 | 0.4777 | | 0.5464 | 45.0 | 4815 | 1.3207 | 0.3205 | 0.6256 | 0.2849 | 0.1818 | 0.2114 | 0.4495 | 0.3295 | 0.493 | 0.5136 | 0.2564 | 0.3824 | 0.7016 | 0.6279 | 0.787 | 0.3335 | 0.5867 | 0.2226 | 0.4064 | 0.1144 | 0.3194 | 0.304 | 0.4687 | | 0.5445 | 46.0 | 4922 | 1.2675 | 0.3266 | 0.6518 | 0.295 | 0.2526 | 0.2217 | 0.4655 | 0.3354 | 0.4991 | 0.5164 | 0.3 | 0.3932 | 0.6916 | 0.626 | 0.7883 | 0.3552 | 0.5817 | 0.2189 | 0.4054 | 0.1285 | 0.3347 | 0.3045 | 0.4721 | | 0.5247 | 47.0 | 5029 | 1.3173 | 0.3311 | 0.6464 | 0.2924 | 0.2393 | 0.226 | 0.4541 | 0.339 | 0.4983 | 0.5182 | 0.2804 | 0.3948 | 0.6897 | 0.6356 | 0.784 | 0.3544 | 0.6 | 0.2171 | 0.4034 | 0.1408 | 0.3333 | 0.3077 | 0.4704 | | 0.5302 | 48.0 | 5136 | 1.2731 | 0.326 | 0.631 | 0.2795 | 0.2521 | 0.2174 | 0.4665 | 0.3267 | 0.4931 | 0.5104 | 0.2953 | 0.3882 | 0.6879 | 0.6296 | 0.784 | 0.3588 | 0.585 | 0.1986 | 0.3961 | 0.1284 | 0.3333 | 0.3147 | 0.4536 | | 0.5149 | 49.0 | 5243 | 1.2684 | 0.3152 | 0.6229 | 0.2801 | 0.2283 | 0.2075 | 0.4572 | 0.3264 | 0.4836 | 0.5022 | 0.281 | 0.3707 | 0.6804 | 0.6224 | 0.7772 | 0.3308 | 0.5433 | 0.2041 | 0.4078 | 0.1049 | 0.3153 | 0.3136 | 0.4676 | | 0.5118 | 50.0 | 5350 | 1.3018 | 0.3252 | 0.6248 | 0.305 | 0.2196 | 0.2206 | 0.4821 | 0.3287 | 0.4858 | 0.5015 | 0.2654 | 0.3794 | 0.6771 | 0.6159 | 0.7691 | 0.3332 | 0.5717 | 0.2209 | 0.3961 | 0.1375 | 0.3167 | 0.3185 | 0.4542 | | 0.502 | 51.0 | 5457 | 1.2466 | 0.3319 | 0.653 | 0.2901 | 0.2399 | 0.2178 | 0.4883 | 0.3388 | 0.4905 | 0.5081 | 0.289 | 0.3748 | 0.6816 | 0.626 | 0.7772 | 0.357 | 0.5783 | 0.2109 | 0.402 | 0.1552 | 0.3208 | 0.3103 | 0.462 | | 0.506 | 52.0 | 5564 | 1.2553 | 0.3153 | 0.64 | 0.2721 | 0.24 | 0.2077 | 0.4769 | 0.3287 | 0.4818 | 0.4982 | 0.2784 | 0.372 | 0.6928 | 0.6264 | 0.766 | 0.3397 | 0.5717 | 0.2087 | 0.3956 | 0.1133 | 0.3111 | 0.2885 | 0.4464 | | 0.5066 | 53.0 | 5671 | 1.3476 | 0.3352 | 0.6334 | 0.2955 | 0.2544 | 0.2323 | 0.4958 | 0.3383 | 0.4952 | 0.5163 | 0.3038 | 0.3849 | 0.7196 | 0.6251 | 0.7759 | 0.3453 | 0.5733 | 0.2273 | 0.4162 | 0.1552 | 0.3319 | 0.3231 | 0.4844 | | 0.4992 | 54.0 | 5778 | 1.3008 | 0.3303 | 0.6458 | 0.292 | 0.1557 | 0.2268 | 0.4735 | 0.3312 | 0.4849 | 0.5011 | 0.228 | 0.3711 | 0.691 | 0.611 | 0.7673 | 0.3633 | 0.5567 | 0.2247 | 0.3995 | 0.1374 | 0.2986 | 0.3149 | 0.4832 | | 0.4791 | 55.0 | 5885 | 1.3185 | 0.3348 | 0.6544 | 0.2884 | 0.2444 | 0.2309 | 0.4804 | 0.3387 | 0.4981 | 0.5153 | 0.2973 | 0.3873 | 0.7085 | 0.6284 | 0.7784 | 0.3575 | 0.595 | 0.2163 | 0.4034 | 0.1542 | 0.3278 | 0.3175 | 0.4721 | | 0.4628 | 56.0 | 5992 | 1.2985 | 0.3266 | 0.6258 | 0.2912 | 0.216 | 0.2286 | 0.4531 | 0.3407 | 0.4845 | 0.5015 | 0.2562 | 0.3845 | 0.6729 | 0.6301 | 0.7747 | 0.3721 | 0.5833 | 0.2105 | 0.402 | 0.1221 | 0.3097 | 0.2983 | 0.438 | | 0.4568 | 57.0 | 6099 | 1.2744 | 0.3368 | 0.6356 | 0.3091 | 0.2112 | 0.2331 | 0.4793 | 0.3421 | 0.4889 | 0.5086 | 0.2813 | 0.3778 | 0.691 | 0.6273 | 0.7741 | 0.3848 | 0.58 | 0.2022 | 0.4078 | 0.1421 | 0.2972 | 0.3276 | 0.4838 | | 0.4508 | 58.0 | 6206 | 1.3367 | 0.3387 | 0.6541 | 0.3073 | 0.256 | 0.2365 | 0.4842 | 0.3444 | 0.4925 | 0.5096 | 0.3095 | 0.3798 | 0.6947 | 0.6259 | 0.7735 | 0.3822 | 0.5767 | 0.2121 | 0.4005 | 0.1487 | 0.3125 | 0.3246 | 0.4849 | | 0.4476 | 59.0 | 6313 | 1.2988 | 0.3422 | 0.6574 | 0.3041 | 0.2575 | 0.2349 | 0.4833 | 0.3421 | 0.4973 | 0.5173 | 0.3087 | 0.3878 | 0.7027 | 0.6439 | 0.7858 | 0.3656 | 0.5683 | 0.239 | 0.4191 | 0.1374 | 0.3333 | 0.325 | 0.4799 | | 0.4418 | 60.0 | 6420 | 1.3153 | 0.336 | 0.6532 | 0.3026 | 0.2298 | 0.2296 | 0.4988 | 0.342 | 0.4952 | 0.5178 | 0.2729 | 0.3987 | 0.6979 | 0.617 | 0.7735 | 0.3692 | 0.5767 | 0.2324 | 0.4152 | 0.1367 | 0.3347 | 0.3246 | 0.4888 | | 0.4255 | 61.0 | 6527 | 1.3474 | 0.3351 | 0.6402 | 0.3107 | 0.2307 | 0.2303 | 0.4998 | 0.3483 | 0.5026 | 0.5188 | 0.2808 | 0.3929 | 0.7016 | 0.6216 | 0.7741 | 0.3898 | 0.5933 | 0.2099 | 0.3975 | 0.1265 | 0.3361 | 0.3277 | 0.4927 | | 0.4398 | 62.0 | 6634 | 1.3079 | 0.3235 | 0.6414 | 0.2716 | 0.203 | 0.2249 | 0.4655 | 0.3377 | 0.4969 | 0.5139 | 0.2522 | 0.3896 | 0.691 | 0.6164 | 0.7716 | 0.3404 | 0.575 | 0.2371 | 0.4216 | 0.1347 | 0.3347 | 0.2889 | 0.4665 | | 0.4373 | 63.0 | 6741 | 1.3848 | 0.3227 | 0.6411 | 0.2715 | 0.2586 | 0.2268 | 0.4554 | 0.338 | 0.4883 | 0.5056 | 0.2983 | 0.3867 | 0.6765 | 0.6107 | 0.7599 | 0.3369 | 0.59 | 0.2328 | 0.4044 | 0.1322 | 0.3153 | 0.3011 | 0.4587 | | 0.4287 | 64.0 | 6848 | 1.3676 | 0.3218 | 0.6456 | 0.2789 | 0.2563 | 0.2225 | 0.4598 | 0.3365 | 0.4915 | 0.5088 | 0.3148 | 0.3826 | 0.6923 | 0.5985 | 0.7549 | 0.3578 | 0.5667 | 0.2206 | 0.4142 | 0.1304 | 0.3417 | 0.3018 | 0.4665 | | 0.4085 | 65.0 | 6955 | 1.3785 | 0.3343 | 0.6465 | 0.2967 | 0.2321 | 0.2383 | 0.4732 | 0.3415 | 0.4994 | 0.514 | 0.2836 | 0.3928 | 0.6854 | 0.6048 | 0.763 | 0.3561 | 0.5817 | 0.2355 | 0.4078 | 0.1399 | 0.3319 | 0.335 | 0.4855 | | 0.4018 | 66.0 | 7062 | 1.3817 | 0.3259 | 0.6478 | 0.279 | 0.2321 | 0.227 | 0.4848 | 0.3364 | 0.4897 | 0.5103 | 0.2796 | 0.3899 | 0.6871 | 0.6028 | 0.7605 | 0.3393 | 0.57 | 0.2203 | 0.4015 | 0.1491 | 0.3306 | 0.318 | 0.4888 | | 0.4005 | 67.0 | 7169 | 1.3791 | 0.3305 | 0.6483 | 0.2948 | 0.2344 | 0.225 | 0.4871 | 0.3358 | 0.4895 | 0.5095 | 0.2826 | 0.3829 | 0.6944 | 0.6162 | 0.7654 | 0.3629 | 0.5683 | 0.2217 | 0.3941 | 0.1194 | 0.3222 | 0.3324 | 0.4972 | | 0.4 | 68.0 | 7276 | 1.3844 | 0.3413 | 0.6686 | 0.2994 | 0.2306 | 0.2315 | 0.4953 | 0.3424 | 0.4996 | 0.5178 | 0.2897 | 0.3871 | 0.7112 | 0.618 | 0.7728 | 0.3626 | 0.58 | 0.2307 | 0.4137 | 0.1373 | 0.3208 | 0.3581 | 0.5017 | | 0.3961 | 69.0 | 7383 | 1.3469 | 0.334 | 0.6535 | 0.2764 | 0.2806 | 0.2249 | 0.4906 | 0.3449 | 0.5058 | 0.5229 | 0.3374 | 0.3884 | 0.7273 | 0.6161 | 0.7778 | 0.3543 | 0.585 | 0.2291 | 0.4088 | 0.1308 | 0.3514 | 0.3396 | 0.4916 | | 0.3817 | 70.0 | 7490 | 1.3834 | 0.328 | 0.6577 | 0.286 | 0.2902 | 0.2249 | 0.4676 | 0.3436 | 0.4968 | 0.5121 | 0.3332 | 0.3912 | 0.6866 | 0.6162 | 0.7593 | 0.3558 | 0.5717 | 0.2175 | 0.4093 | 0.1222 | 0.3361 | 0.3282 | 0.4844 | | 0.3851 | 71.0 | 7597 | 1.3745 | 0.3268 | 0.6354 | 0.281 | 0.2392 | 0.2237 | 0.4644 | 0.3386 | 0.4965 | 0.5132 | 0.3088 | 0.3918 | 0.685 | 0.6184 | 0.7654 | 0.3537 | 0.575 | 0.2253 | 0.4039 | 0.1238 | 0.3528 | 0.313 | 0.4687 | | 0.3773 | 72.0 | 7704 | 1.3953 | 0.3325 | 0.6475 | 0.2841 | 0.2436 | 0.2233 | 0.4887 | 0.3397 | 0.4968 | 0.5134 | 0.3069 | 0.3873 | 0.6961 | 0.6198 | 0.7673 | 0.3492 | 0.575 | 0.2159 | 0.4088 | 0.1404 | 0.325 | 0.3375 | 0.4911 | | 0.3709 | 73.0 | 7811 | 1.3560 | 0.3299 | 0.6478 | 0.2891 | 0.2394 | 0.2246 | 0.4904 | 0.3415 | 0.4962 | 0.5133 | 0.2887 | 0.3893 | 0.6974 | 0.6232 | 0.7648 | 0.3428 | 0.5567 | 0.2255 | 0.4074 | 0.13 | 0.3444 | 0.3281 | 0.4933 | | 0.386 | 74.0 | 7918 | 1.3967 | 0.3336 | 0.6619 | 0.2908 | 0.2852 | 0.23 | 0.4811 | 0.3439 | 0.4963 | 0.5149 | 0.3293 | 0.3951 | 0.6897 | 0.6098 | 0.7636 | 0.3737 | 0.5967 | 0.2245 | 0.401 | 0.1329 | 0.3319 | 0.3271 | 0.4816 | | 0.3584 | 75.0 | 8025 | 1.3931 | 0.3342 | 0.6622 | 0.3032 | 0.2654 | 0.2288 | 0.4765 | 0.3417 | 0.495 | 0.513 | 0.3084 | 0.3815 | 0.7017 | 0.6155 | 0.7784 | 0.3649 | 0.57 | 0.2321 | 0.402 | 0.1249 | 0.3222 | 0.3336 | 0.4922 | | 0.3481 | 76.0 | 8132 | 1.3925 | 0.3368 | 0.6585 | 0.2895 | 0.269 | 0.2329 | 0.4833 | 0.348 | 0.5001 | 0.5193 | 0.3074 | 0.3935 | 0.7015 | 0.6196 | 0.7821 | 0.3493 | 0.5783 | 0.2393 | 0.4118 | 0.1409 | 0.3292 | 0.3348 | 0.495 | | 0.3512 | 77.0 | 8239 | 1.3984 | 0.3323 | 0.6513 | 0.2849 | 0.2895 | 0.2248 | 0.4827 | 0.3393 | 0.4967 | 0.5136 | 0.3289 | 0.382 | 0.7079 | 0.6188 | 0.7772 | 0.339 | 0.5783 | 0.2318 | 0.4074 | 0.1319 | 0.3208 | 0.3402 | 0.4844 | | 0.3366 | 78.0 | 8346 | 1.4160 | 0.3414 | 0.6618 | 0.3016 | 0.263 | 0.2376 | 0.4876 | 0.3428 | 0.4978 | 0.5138 | 0.3062 | 0.3873 | 0.6903 | 0.6113 | 0.7704 | 0.3714 | 0.595 | 0.2483 | 0.4093 | 0.131 | 0.3056 | 0.3449 | 0.4888 | | 0.3278 | 79.0 | 8453 | 1.4257 | 0.3367 | 0.6401 | 0.3062 | 0.2399 | 0.2326 | 0.4775 | 0.3408 | 0.4939 | 0.5115 | 0.2817 | 0.3838 | 0.6977 | 0.6067 | 0.771 | 0.3648 | 0.5717 | 0.2445 | 0.4108 | 0.1211 | 0.3069 | 0.3463 | 0.4972 | | 0.3225 | 80.0 | 8560 | 1.3995 | 0.3355 | 0.6592 | 0.2994 | 0.2158 | 0.2357 | 0.4778 | 0.3447 | 0.496 | 0.5105 | 0.2617 | 0.3863 | 0.6889 | 0.6033 | 0.7562 | 0.3597 | 0.5817 | 0.2395 | 0.4039 | 0.1327 | 0.3153 | 0.3423 | 0.4955 | | 0.3197 | 81.0 | 8667 | 1.3828 | 0.3368 | 0.6525 | 0.3008 | 0.239 | 0.2342 | 0.48 | 0.3525 | 0.503 | 0.5196 | 0.2863 | 0.3951 | 0.6975 | 0.6113 | 0.7735 | 0.3803 | 0.5817 | 0.2268 | 0.4029 | 0.1239 | 0.3347 | 0.3415 | 0.505 | | 0.3119 | 82.0 | 8774 | 1.3821 | 0.3367 | 0.6504 | 0.313 | 0.2674 | 0.2299 | 0.4826 | 0.3486 | 0.4961 | 0.5121 | 0.309 | 0.3872 | 0.692 | 0.6122 | 0.763 | 0.3691 | 0.58 | 0.2295 | 0.4108 | 0.1337 | 0.3097 | 0.3391 | 0.4972 | | 0.3022 | 83.0 | 8881 | 1.4340 | 0.3378 | 0.6458 | 0.2983 | 0.2439 | 0.2336 | 0.4668 | 0.3417 | 0.4952 | 0.5138 | 0.2866 | 0.3936 | 0.6793 | 0.6111 | 0.7636 | 0.3687 | 0.5783 | 0.2383 | 0.4206 | 0.1301 | 0.3139 | 0.341 | 0.4927 | | 0.3011 | 84.0 | 8988 | 1.4571 | 0.3335 | 0.6497 | 0.2893 | 0.2644 | 0.2254 | 0.4719 | 0.3399 | 0.4901 | 0.5069 | 0.3052 | 0.3811 | 0.6801 | 0.6158 | 0.7654 | 0.3622 | 0.5783 | 0.2278 | 0.4029 | 0.1355 | 0.3069 | 0.3261 | 0.481 | | 0.2972 | 85.0 | 9095 | 1.4258 | 0.3338 | 0.6461 | 0.2916 | 0.2397 | 0.2283 | 0.4751 | 0.3449 | 0.499 | 0.516 | 0.2907 | 0.3911 | 0.6938 | 0.606 | 0.7574 | 0.3745 | 0.6017 | 0.2265 | 0.4025 | 0.1277 | 0.3264 | 0.3343 | 0.4922 | | 0.2859 | 86.0 | 9202 | 1.4684 | 0.329 | 0.6399 | 0.2879 | 0.2591 | 0.2221 | 0.4732 | 0.3452 | 0.4957 | 0.5163 | 0.3076 | 0.3911 | 0.6931 | 0.6055 | 0.766 | 0.3602 | 0.5983 | 0.2173 | 0.3936 | 0.1339 | 0.3306 | 0.328 | 0.4927 | | 0.2921 | 87.0 | 9309 | 1.4191 | 0.3332 | 0.646 | 0.2982 | 0.2655 | 0.2265 | 0.477 | 0.3415 | 0.5005 | 0.5196 | 0.3141 | 0.3895 | 0.7048 | 0.6053 | 0.7679 | 0.3804 | 0.6133 | 0.2223 | 0.402 | 0.1294 | 0.3278 | 0.3286 | 0.4872 | | 0.2788 | 88.0 | 9416 | 1.4109 | 0.3327 | 0.6484 | 0.2959 | 0.2407 | 0.2284 | 0.4755 | 0.3454 | 0.498 | 0.5162 | 0.2875 | 0.3884 | 0.7014 | 0.6128 | 0.771 | 0.3555 | 0.585 | 0.2313 | 0.4025 | 0.1274 | 0.3333 | 0.3366 | 0.4894 | | 0.2808 | 89.0 | 9523 | 1.4585 | 0.3333 | 0.6453 | 0.3076 | 0.2643 | 0.2279 | 0.4756 | 0.3423 | 0.4977 | 0.5168 | 0.3141 | 0.388 | 0.6946 | 0.6058 | 0.7673 | 0.3719 | 0.5967 | 0.2322 | 0.4118 | 0.1281 | 0.3139 | 0.3286 | 0.4944 | | 0.2747 | 90.0 | 9630 | 1.4490 | 0.3338 | 0.6433 | 0.2976 | 0.2402 | 0.2257 | 0.4754 | 0.3455 | 0.4997 | 0.5175 | 0.2859 | 0.3942 | 0.6893 | 0.6087 | 0.7667 | 0.3793 | 0.595 | 0.2274 | 0.4083 | 0.1186 | 0.3264 | 0.3347 | 0.4911 | | 0.2528 | 91.0 | 9737 | 1.4493 | 0.3362 | 0.6504 | 0.2926 | 0.2687 | 0.2288 | 0.4798 | 0.342 | 0.5006 | 0.5198 | 0.3141 | 0.3965 | 0.6867 | 0.6044 | 0.7704 | 0.389 | 0.6 | 0.2308 | 0.4103 | 0.1191 | 0.3222 | 0.3375 | 0.4961 | | 0.2593 | 92.0 | 9844 | 1.4320 | 0.334 | 0.6486 | 0.2993 | 0.2461 | 0.2263 | 0.4702 | 0.3423 | 0.4986 | 0.517 | 0.2961 | 0.3884 | 0.6883 | 0.6089 | 0.7679 | 0.3741 | 0.5917 | 0.2337 | 0.4078 | 0.1149 | 0.3208 | 0.3382 | 0.4966 | | 0.2685 | 93.0 | 9951 | 1.4475 | 0.3357 | 0.6478 | 0.2992 | 0.2672 | 0.2258 | 0.4738 | 0.3425 | 0.4978 | 0.5165 | 0.3123 | 0.3859 | 0.7037 | 0.6143 | 0.771 | 0.3774 | 0.5883 | 0.2311 | 0.4132 | 0.1173 | 0.325 | 0.3386 | 0.4849 | | 0.2618 | 94.0 | 10058 | 1.4451 | 0.3385 | 0.6593 | 0.2988 | 0.2673 | 0.2324 | 0.4768 | 0.3457 | 0.5009 | 0.519 | 0.316 | 0.3898 | 0.6985 | 0.6104 | 0.7716 | 0.387 | 0.5967 | 0.2296 | 0.4039 | 0.1281 | 0.3278 | 0.3373 | 0.495 | | 0.2513 | 95.0 | 10165 | 1.4426 | 0.3403 | 0.6552 | 0.313 | 0.2645 | 0.2336 | 0.4795 | 0.3429 | 0.5006 | 0.5179 | 0.3109 | 0.391 | 0.6934 | 0.6134 | 0.7735 | 0.3953 | 0.595 | 0.2311 | 0.4025 | 0.1209 | 0.3278 | 0.3408 | 0.4911 | | 0.2596 | 96.0 | 10272 | 1.4438 | 0.338 | 0.6557 | 0.3033 | 0.2669 | 0.2288 | 0.4786 | 0.344 | 0.5005 | 0.5189 | 0.317 | 0.3915 | 0.6914 | 0.6119 | 0.7722 | 0.3964 | 0.595 | 0.2265 | 0.4054 | 0.1177 | 0.3347 | 0.3374 | 0.4872 | | 0.2363 | 97.0 | 10379 | 1.4447 | 0.3379 | 0.6526 | 0.3044 | 0.2687 | 0.2297 | 0.4771 | 0.3437 | 0.5013 | 0.5196 | 0.32 | 0.3893 | 0.6969 | 0.608 | 0.7735 | 0.3861 | 0.5917 | 0.2335 | 0.4083 | 0.122 | 0.3319 | 0.3396 | 0.4927 | | 0.2346 | 98.0 | 10486 | 1.4613 | 0.3366 | 0.6512 | 0.299 | 0.267 | 0.2288 | 0.4809 | 0.3425 | 0.5006 | 0.5201 | 0.3166 | 0.391 | 0.6999 | 0.6083 | 0.7722 | 0.3887 | 0.5933 | 0.2285 | 0.4088 | 0.1186 | 0.3319 | 0.3388 | 0.4944 | | 0.2452 | 99.0 | 10593 | 1.4607 | 0.3362 | 0.6498 | 0.3001 | 0.2676 | 0.2286 | 0.4805 | 0.344 | 0.4982 | 0.5179 | 0.3168 | 0.3889 | 0.6997 | 0.6076 | 0.7716 | 0.3863 | 0.585 | 0.2264 | 0.4049 | 0.124 | 0.3347 | 0.3368 | 0.4933 | | 0.2287 | 100.0 | 10700 | 1.4674 | 0.3363 | 0.6506 | 0.2992 | 0.2695 | 0.2282 | 0.4791 | 0.3441 | 0.4988 | 0.5186 | 0.3192 | 0.3884 | 0.6982 | 0.607 | 0.7716 | 0.3854 | 0.5883 | 0.2283 | 0.4093 | 0.1228 | 0.3319 | 0.3379 | 0.4916 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

leylaut/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0419 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.0815 | 1.0 | 2500 | 1.0419 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

kuchidareo/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 313 | 5.5361 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

goouthy/detr_output

# detr_output This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 8.3626 - eval_runtime: 131.87 - eval_samples_per_second: 7.583 - eval_steps_per_second: 0.948 - epoch: 1.6 - step: 500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

oskarkuuse/detr2

# detr2 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2191 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3967 | 1.0 | 1250 | 1.5527 | | 1.7891 | 2.0 | 2500 | 1.4216 | | 1.537 | 3.0 | 3750 | 1.3198 | | 1.2472 | 4.0 | 5000 | 1.2333 | | 1.1635 | 5.0 | 6250 | 1.2191 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

asldlaskdlk/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2692 | 1.0 | 2500 | 1.2485 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

alizeyn/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9499 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1069 | 0.2 | 500 | 1.4327 | | 1.2885 | 0.4 | 1000 | 1.1961 | | 1.1377 | 0.6 | 1500 | 1.0482 | | 1.0395 | 0.8 | 2000 | 0.9764 | | 1.0037 | 1.0 | 2500 | 0.9499 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "2", "3", "1", "0" ]

depek/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8756 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7508 | 0.16 | 300 | 1.7472 | | 1.412 | 0.32 | 600 | 1.3611 | | 1.2549 | 0.48 | 900 | 1.1682 | | 1.0701 | 0.64 | 1200 | 1.0696 | | 0.9812 | 0.8 | 1500 | 1.0103 | | 1.0346 | 0.96 | 1800 | 0.9782 | | 1.014 | 1.12 | 2100 | 0.9429 | | 0.9691 | 1.28 | 2400 | 0.9248 | | 0.9749 | 1.44 | 2700 | 0.8947 | | 0.9707 | 1.6 | 3000 | 0.8887 | | 0.938 | 1.76 | 3300 | 0.8889 | | 0.9477 | 1.92 | 3600 | 0.8756 | ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

jbossenk/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9448 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6853 | 0.32 | 50 | 1.8717 | | 1.6419 | 0.64 | 100 | 1.4752 | | 1.4029 | 0.96 | 150 | 1.2728 | | 1.2596 | 1.27 | 200 | 1.1683 | | 1.1761 | 1.59 | 250 | 1.0847 | | 1.106 | 1.91 | 300 | 1.0396 | | 1.0586 | 2.23 | 350 | 0.9886 | | 1.0258 | 2.55 | 400 | 0.9653 | | 1.0151 | 2.87 | 450 | 0.9448 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "clothing", "shoes", "bags", "accessories" ]

gregorrehand/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1209 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5644 | 0.4 | 500 | 2.4934 | | 2.1886 | 0.8 | 1000 | 2.1209 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

Teele/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.0407 | 1.0 | 3334 | 0.9664 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

obednsiah/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.3956 | 0.16 | 50 | 4.0045 | | 3.5668 | 0.32 | 100 | 3.3500 | | 2.9609 | 0.48 | 150 | 2.9119 | | 2.6387 | 0.64 | 200 | 2.5813 | | 2.411 | 0.8 | 250 | 2.4280 | | 2.3426 | 0.96 | 300 | 2.3771 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

Naghma/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9572 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8945 | 1.0 | 2500 | 1.9572 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessory", "top", "bottom", "shoe" ]

Karl-Erik/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.2889 - eval_runtime: 145.9792 - eval_samples_per_second: 6.85 - eval_steps_per_second: 0.856 - epoch: 0.64 - step: 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

RandomCatLover/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

AnetteHabanen/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

qubvel-hf/detr-resnet-50-finetuned-10k-cppe5-no-trainer

# detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 0.9865 - Map: 0.3192 - Map 50: 0.6187 - Map 75: 0.3039 - Map small: 0.271 - Map medium: 0.2307 - Map large: 0.4635 - Mar 1: 0.2947 - Mar 10: 0.4873 - Mar 100: 0.5005 - Mar small: 0.3421 - Mar medium: 0.4012 - Mar large: 0.6176 - Map Coverall: 0.5908 - Mar 100 Coverall: 0.6978 - Map Face Shield: 0.3773 - Mar 100 Face Shield: 0.6353 - Map Gloves: 0.2142 - Mar 100 Gloves: 0.3902 - Map Goggles: 0.1483 - Mar 100 Goggles: 0.3906 - Map Mask: 0.2653 - Mar 100 Mask: 0.3880 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

RoblabWhGe/rescuedet-deformable-detr

# detr-sensetime-finetuned-firedetv7 This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the RoblabWhGe/FireDetDataset dataset. It achieves the following results on the evaluation set: - Loss: 0.9075 - Map: 0.3883 - Map 50: 0.7422 - Map 75: 0.3707 - Map Small: 0.2639 - Map Medium: 0.4432 - Map Large: 0.5149 - Mar 1: 0.1286 - Mar 10: 0.419 - Mar 100: 0.5325 - Mar Small: 0.3975 - Mar Medium: 0.5729 - Mar Large: 0.68 - Map Fire: 0.2322 - Mar 100 Fire: 0.4708 - Map Vehicle: 0.5547 - Mar 100 Vehicle: 0.6431 - Map Human: 0.3779 - Mar 100 Human: 0.4834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 300.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Fire | Mar 100 Fire | Map Vehicle | Mar 100 Vehicle | Map Human | Mar 100 Human | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------:|:------------:|:-----------:|:---------------:|:---------:|:-------------:| | 2.3092 | 1.0 | 674 | 1.4485 | 0.0919 | 0.2097 | 0.0678 | 0.0306 | 0.1068 | 0.1693 | 0.0613 | 0.2179 | 0.3221 | 0.1226 | 0.4115 | 0.4564 | 0.0289 | 0.3272 | 0.1826 | 0.3908 | 0.064 | 0.2482 | | 1.4291 | 2.0 | 1348 | 1.3315 | 0.1676 | 0.3524 | 0.1505 | 0.0696 | 0.2179 | 0.2573 | 0.0816 | 0.2714 | 0.3792 | 0.1992 | 0.4641 | 0.4846 | 0.1022 | 0.3921 | 0.2843 | 0.473 | 0.1163 | 0.2724 | | 1.3582 | 3.0 | 2022 | 1.3595 | 0.1631 | 0.376 | 0.1207 | 0.0557 | 0.2285 | 0.2385 | 0.0763 | 0.2541 | 0.3545 | 0.1701 | 0.4375 | 0.4512 | 0.1117 | 0.3678 | 0.2722 | 0.4536 | 0.1055 | 0.2421 | | 1.325 | 4.0 | 2696 | 1.3166 | 0.1733 | 0.3837 | 0.1372 | 0.0488 | 0.2463 | 0.2682 | 0.0845 | 0.2665 | 0.3579 | 0.1626 | 0.4454 | 0.4994 | 0.1374 | 0.3755 | 0.2715 | 0.4389 | 0.111 | 0.2592 | | 1.2992 | 5.0 | 3370 | 1.2836 | 0.2032 | 0.4185 | 0.1693 | 0.0725 | 0.279 | 0.3113 | 0.089 | 0.2913 | 0.389 | 0.1825 | 0.476 | 0.5379 | 0.12 | 0.3659 | 0.3327 | 0.4907 | 0.1569 | 0.3104 | | 1.2567 | 6.0 | 4044 | 1.2566 | 0.1983 | 0.441 | 0.1491 | 0.0793 | 0.2675 | 0.3082 | 0.0858 | 0.2833 | 0.376 | 0.2 | 0.4517 | 0.5109 | 0.1268 | 0.372 | 0.3034 | 0.4574 | 0.1647 | 0.2986 | | 1.2463 | 7.0 | 4718 | 1.2504 | 0.2 | 0.4294 | 0.1663 | 0.0593 | 0.2782 | 0.2958 | 0.0855 | 0.2876 | 0.3814 | 0.1742 | 0.4864 | 0.4725 | 0.1371 | 0.3881 | 0.3302 | 0.4757 | 0.1327 | 0.2803 | | 1.2326 | 8.0 | 5392 | 1.1854 | 0.2271 | 0.4778 | 0.1937 | 0.0831 | 0.3006 | 0.3862 | 0.0957 | 0.3085 | 0.4177 | 0.2162 | 0.5003 | 0.6019 | 0.1627 | 0.4278 | 0.3514 | 0.5103 | 0.1672 | 0.315 | | 1.2341 | 9.0 | 6066 | 1.1747 | 0.2402 | 0.4989 | 0.2152 | 0.0883 | 0.3169 | 0.3904 | 0.0998 | 0.3214 | 0.4236 | 0.2155 | 0.5053 | 0.6261 | 0.1607 | 0.4196 | 0.3735 | 0.5206 | 0.1864 | 0.3304 | | 1.2104 | 10.0 | 6740 | 1.2319 | 0.2166 | 0.4601 | 0.1802 | 0.0898 | 0.2835 | 0.3558 | 0.092 | 0.3095 | 0.4034 | 0.2244 | 0.48 | 0.5565 | 0.1428 | 0.4141 | 0.3491 | 0.5038 | 0.158 | 0.2922 | | 1.2041 | 11.0 | 7414 | 1.1567 | 0.2454 | 0.5063 | 0.2099 | 0.0949 | 0.3142 | 0.3948 | 0.0997 | 0.3216 | 0.4216 | 0.237 | 0.5019 | 0.5881 | 0.1653 | 0.4139 | 0.3698 | 0.515 | 0.2012 | 0.3358 | | 1.1603 | 12.0 | 8088 | 1.1399 | 0.2389 | 0.4934 | 0.2012 | 0.0844 | 0.3131 | 0.4267 | 0.0994 | 0.3272 | 0.434 | 0.2415 | 0.5103 | 0.6273 | 0.1496 | 0.4389 | 0.3871 | 0.5337 | 0.18 | 0.3295 | | 1.1662 | 13.0 | 8762 | 1.1462 | 0.2508 | 0.5201 | 0.2152 | 0.1004 | 0.3212 | 0.4309 | 0.1039 | 0.3274 | 0.432 | 0.2385 | 0.5088 | 0.6173 | 0.1727 | 0.4271 | 0.3674 | 0.5271 | 0.2124 | 0.3419 | | 1.17 | 14.0 | 9436 | 1.1814 | 0.2356 | 0.4893 | 0.2009 | 0.0751 | 0.3165 | 0.4019 | 0.0988 | 0.3158 | 0.4128 | 0.1977 | 0.5091 | 0.581 | 0.1631 | 0.4266 | 0.3716 | 0.5052 | 0.172 | 0.3067 | | 1.1494 | 15.0 | 10110 | 1.1660 | 0.2296 | 0.4782 | 0.1998 | 0.0744 | 0.2922 | 0.4074 | 0.1022 | 0.3051 | 0.4049 | 0.1996 | 0.4834 | 0.5917 | 0.1749 | 0.4223 | 0.3565 | 0.4912 | 0.1575 | 0.3012 | | 1.1326 | 16.0 | 10784 | 1.1720 | 0.2421 | 0.4943 | 0.2137 | 0.0865 | 0.3114 | 0.4159 | 0.1035 | 0.3222 | 0.4235 | 0.2065 | 0.5105 | 0.6135 | 0.1738 | 0.438 | 0.3649 | 0.5101 | 0.1874 | 0.3223 | | 1.1311 | 17.0 | 11458 | 1.1736 | 0.2324 | 0.4753 | 0.2005 | 0.1042 | 0.2986 | 0.3579 | 0.097 | 0.3132 | 0.4198 | 0.2203 | 0.5024 | 0.5958 | 0.1657 | 0.4223 | 0.338 | 0.5052 | 0.1936 | 0.3319 | | 1.1184 | 18.0 | 12132 | 1.1249 | 0.2552 | 0.5162 | 0.2308 | 0.1071 | 0.318 | 0.412 | 0.1036 | 0.3329 | 0.4394 | 0.2474 | 0.5142 | 0.6152 | 0.1678 | 0.4308 | 0.3912 | 0.5381 | 0.2066 | 0.3493 | | 1.12 | 19.0 | 12806 | 1.1269 | 0.259 | 0.518 | 0.2346 | 0.1004 | 0.3339 | 0.426 | 0.1034 | 0.3352 | 0.4395 | 0.2364 | 0.5295 | 0.6315 | 0.1738 | 0.4404 | 0.3945 | 0.5386 | 0.2088 | 0.3396 | | 1.1243 | 20.0 | 13480 | 1.1313 | 0.2379 | 0.5002 | 0.1982 | 0.1056 | 0.3004 | 0.3883 | 0.0984 | 0.3176 | 0.4421 | 0.273 | 0.5127 | 0.6014 | 0.1418 | 0.4307 | 0.3832 | 0.5523 | 0.1887 | 0.3433 | | 1.1015 | 21.0 | 14154 | 1.1511 | 0.2454 | 0.5096 | 0.2091 | 0.1018 | 0.3132 | 0.3902 | 0.1015 | 0.3228 | 0.4279 | 0.242 | 0.5172 | 0.5663 | 0.167 | 0.435 | 0.3837 | 0.5237 | 0.1855 | 0.3251 | | 1.0967 | 22.0 | 14828 | 1.1409 | 0.2503 | 0.5218 | 0.2146 | 0.11 | 0.3064 | 0.3983 | 0.1038 | 0.3227 | 0.4392 | 0.2592 | 0.5007 | 0.5895 | 0.1586 | 0.4338 | 0.3879 | 0.548 | 0.2044 | 0.3358 | | 1.0997 | 23.0 | 15502 | 1.1347 | 0.2475 | 0.5142 | 0.2037 | 0.0897 | 0.3175 | 0.4267 | 0.1053 | 0.3245 | 0.4281 | 0.2301 | 0.5028 | 0.6142 | 0.1533 | 0.4098 | 0.402 | 0.5404 | 0.1874 | 0.3342 | | 1.0982 | 24.0 | 16176 | 1.1326 | 0.2603 | 0.5254 | 0.2328 | 0.1091 | 0.3282 | 0.4279 | 0.1088 | 0.3287 | 0.4347 | 0.2373 | 0.5205 | 0.6112 | 0.1577 | 0.4108 | 0.416 | 0.548 | 0.2071 | 0.3452 | | 1.0832 | 25.0 | 16850 | 1.1195 | 0.2552 | 0.5257 | 0.214 | 0.0971 | 0.3179 | 0.4274 | 0.1047 | 0.3254 | 0.4318 | 0.244 | 0.5058 | 0.6107 | 0.1775 | 0.4359 | 0.3881 | 0.5234 | 0.1999 | 0.336 | | 1.0823 | 26.0 | 17524 | 1.1438 | 0.2553 | 0.5247 | 0.2186 | 0.0956 | 0.3215 | 0.4308 | 0.1031 | 0.3244 | 0.4269 | 0.2346 | 0.4868 | 0.6238 | 0.1749 | 0.4181 | 0.4047 | 0.5369 | 0.1863 | 0.3255 | | 1.0821 | 27.0 | 18198 | 1.0968 | 0.2709 | 0.5451 | 0.2287 | 0.1078 | 0.3398 | 0.4644 | 0.1124 | 0.3393 | 0.4453 | 0.2423 | 0.5328 | 0.6554 | 0.1925 | 0.4444 | 0.4101 | 0.5461 | 0.2101 | 0.3455 | | 1.0744 | 28.0 | 18872 | 1.0950 | 0.2535 | 0.5186 | 0.2156 | 0.0946 | 0.3197 | 0.4518 | 0.1074 | 0.3273 | 0.4394 | 0.2249 | 0.5239 | 0.6597 | 0.1655 | 0.4375 | 0.3885 | 0.5345 | 0.2066 | 0.3461 | | 1.0572 | 29.0 | 19546 | 1.0748 | 0.2704 | 0.5723 | 0.2239 | 0.117 | 0.3371 | 0.4488 | 0.1077 | 0.3343 | 0.4451 | 0.2645 | 0.5111 | 0.6237 | 0.1841 | 0.4412 | 0.4019 | 0.5397 | 0.2251 | 0.3544 | | 1.0517 | 30.0 | 20220 | 1.0936 | 0.2707 | 0.5561 | 0.22 | 0.1127 | 0.346 | 0.4501 | 0.1074 | 0.3336 | 0.4343 | 0.2522 | 0.514 | 0.6226 | 0.1764 | 0.4299 | 0.4146 | 0.531 | 0.2211 | 0.342 | | 1.0287 | 31.0 | 20894 | 1.1122 | 0.2551 | 0.5578 | 0.1931 | 0.0958 | 0.3256 | 0.4326 | 0.1055 | 0.314 | 0.4199 | 0.2166 | 0.5026 | 0.6243 | 0.175 | 0.419 | 0.3732 | 0.5033 | 0.217 | 0.3375 | | 1.0528 | 32.0 | 21568 | 1.0791 | 0.2626 | 0.5484 | 0.2227 | 0.1011 | 0.3333 | 0.4451 | 0.1083 | 0.3374 | 0.4389 | 0.2384 | 0.5194 | 0.6338 | 0.1753 | 0.4437 | 0.4161 | 0.5493 | 0.1962 | 0.3237 | | 1.0465 | 33.0 | 22242 | 1.1751 | 0.239 | 0.486 | 0.2122 | 0.0887 | 0.3104 | 0.3949 | 0.0997 | 0.3183 | 0.4192 | 0.2304 | 0.5026 | 0.5903 | 0.1521 | 0.4148 | 0.3879 | 0.5103 | 0.1769 | 0.3323 | | 1.0499 | 34.0 | 22916 | 1.0786 | 0.27 | 0.5536 | 0.2241 | 0.1222 | 0.3381 | 0.4472 | 0.1075 | 0.3362 | 0.443 | 0.2441 | 0.5186 | 0.623 | 0.1742 | 0.4458 | 0.4103 | 0.5412 | 0.2256 | 0.342 | | 1.0382 | 35.0 | 23590 | 1.0990 | 0.2602 | 0.548 | 0.2168 | 0.117 | 0.3275 | 0.4423 | 0.1062 | 0.334 | 0.4356 | 0.2532 | 0.514 | 0.6148 | 0.1686 | 0.4328 | 0.4087 | 0.5423 | 0.2034 | 0.3315 | | 1.0273 | 36.0 | 24264 | 1.0926 | 0.2579 | 0.531 | 0.2222 | 0.0956 | 0.3419 | 0.4373 | 0.1066 | 0.3281 | 0.4336 | 0.2441 | 0.5092 | 0.6179 | 0.164 | 0.4586 | 0.4031 | 0.5199 | 0.2065 | 0.3224 | | 1.0475 | 37.0 | 24938 | 1.0754 | 0.2714 | 0.5592 | 0.2288 | 0.1325 | 0.3444 | 0.4221 | 0.108 | 0.3471 | 0.4535 | 0.2888 | 0.5277 | 0.5865 | 0.1639 | 0.4355 | 0.4265 | 0.5631 | 0.2238 | 0.3621 | | 1.0262 | 38.0 | 25612 | 1.0378 | 0.2858 | 0.5729 | 0.2508 | 0.1226 | 0.3592 | 0.4762 | 0.1121 | 0.346 | 0.4607 | 0.2832 | 0.5275 | 0.6548 | 0.1827 | 0.4504 | 0.4407 | 0.5652 | 0.234 | 0.3663 | | 1.0155 | 39.0 | 26286 | 1.0401 | 0.2859 | 0.5747 | 0.2428 | 0.139 | 0.3549 | 0.4554 | 0.1085 | 0.3526 | 0.4676 | 0.287 | 0.5385 | 0.6314 | 0.1631 | 0.4501 | 0.4445 | 0.5819 | 0.25 | 0.3709 | | 1.0218 | 40.0 | 26960 | 1.0469 | 0.2879 | 0.579 | 0.2464 | 0.1251 | 0.3659 | 0.4663 | 0.1097 | 0.3513 | 0.4581 | 0.2504 | 0.5374 | 0.6601 | 0.1818 | 0.4428 | 0.4499 | 0.5784 | 0.232 | 0.353 | | 1.0164 | 41.0 | 27634 | 1.0488 | 0.2941 | 0.5835 | 0.2629 | 0.1398 | 0.3747 | 0.467 | 0.1087 | 0.3535 | 0.4651 | 0.2803 | 0.5441 | 0.6476 | 0.1734 | 0.4346 | 0.4556 | 0.5822 | 0.2532 | 0.3784 | | 1.0253 | 42.0 | 28308 | 1.0604 | 0.2804 | 0.564 | 0.2487 | 0.1337 | 0.3414 | 0.4713 | 0.1098 | 0.348 | 0.4543 | 0.2721 | 0.5264 | 0.6485 | 0.1778 | 0.4452 | 0.4339 | 0.5596 | 0.2294 | 0.3581 | | 1.0327 | 43.0 | 28982 | 1.0828 | 0.279 | 0.5572 | 0.2536 | 0.1319 | 0.34 | 0.4657 | 0.1122 | 0.3483 | 0.4495 | 0.2686 | 0.5173 | 0.657 | 0.1807 | 0.454 | 0.4318 | 0.5477 | 0.2246 | 0.3469 | | 1.0131 | 44.0 | 29656 | 1.0594 | 0.286 | 0.5791 | 0.251 | 0.1261 | 0.3611 | 0.469 | 0.1097 | 0.3506 | 0.4566 | 0.2701 | 0.5418 | 0.6371 | 0.1899 | 0.4445 | 0.4354 | 0.5686 | 0.2326 | 0.3567 | | 1.0171 | 45.0 | 30330 | 1.0614 | 0.2795 | 0.5742 | 0.2456 | 0.1245 | 0.3433 | 0.4644 | 0.1092 | 0.3446 | 0.453 | 0.2668 | 0.5302 | 0.64 | 0.1724 | 0.4413 | 0.4444 | 0.5652 | 0.2217 | 0.3526 | | 1.0071 | 46.0 | 31004 | 1.0887 | 0.2823 | 0.577 | 0.2438 | 0.1307 | 0.3619 | 0.4408 | 0.1067 | 0.3455 | 0.4504 | 0.2721 | 0.5371 | 0.6152 | 0.1776 | 0.4465 | 0.4286 | 0.5431 | 0.2407 | 0.3617 | | 1.0017 | 47.0 | 31678 | 1.0296 | 0.3015 | 0.5872 | 0.274 | 0.1392 | 0.3768 | 0.4809 | 0.111 | 0.3651 | 0.4698 | 0.2732 | 0.5505 | 0.6646 | 0.1891 | 0.4563 | 0.4573 | 0.574 | 0.2582 | 0.379 | | 1.0108 | 48.0 | 32352 | 1.0046 | 0.3051 | 0.6009 | 0.2686 | 0.1429 | 0.3709 | 0.4927 | 0.1132 | 0.3652 | 0.4779 | 0.2781 | 0.5475 | 0.6821 | 0.1949 | 0.4569 | 0.4652 | 0.5835 | 0.2552 | 0.3933 | | 0.9953 | 49.0 | 33026 | 1.0458 | 0.2879 | 0.5832 | 0.2558 | 0.1334 | 0.36 | 0.4751 | 0.1128 | 0.3509 | 0.4653 | 0.2825 | 0.5324 | 0.6498 | 0.1805 | 0.4487 | 0.4463 | 0.5734 | 0.237 | 0.374 | | 0.988 | 50.0 | 33700 | 1.0375 | 0.2902 | 0.5856 | 0.2503 | 0.1204 | 0.3624 | 0.494 | 0.1163 | 0.3502 | 0.4562 | 0.2493 | 0.5332 | 0.6619 | 0.1916 | 0.4489 | 0.4473 | 0.5621 | 0.2316 | 0.3575 | | 0.987 | 51.0 | 34374 | 1.0530 | 0.283 | 0.5758 | 0.239 | 0.1114 | 0.3675 | 0.4677 | 0.1086 | 0.3487 | 0.4554 | 0.2579 | 0.55 | 0.6453 | 0.1933 | 0.4568 | 0.4361 | 0.5577 | 0.2198 | 0.3517 | | 0.9847 | 52.0 | 35048 | 1.0420 | 0.2985 | 0.6017 | 0.2538 | 0.1318 | 0.3745 | 0.4696 | 0.1135 | 0.3602 | 0.462 | 0.269 | 0.5293 | 0.6626 | 0.1892 | 0.4373 | 0.4604 | 0.5771 | 0.2458 | 0.3716 | | 0.9899 | 53.0 | 35722 | 1.0385 | 0.2966 | 0.6054 | 0.2515 | 0.1359 | 0.365 | 0.4984 | 0.1129 | 0.3599 | 0.4646 | 0.2667 | 0.5349 | 0.6759 | 0.178 | 0.4376 | 0.4631 | 0.5838 | 0.2488 | 0.3722 | | 0.9822 | 54.0 | 36396 | 1.0391 | 0.286 | 0.5813 | 0.2451 | 0.1199 | 0.3699 | 0.4542 | 0.1117 | 0.3573 | 0.4638 | 0.2812 | 0.5427 | 0.6336 | 0.1803 | 0.4482 | 0.4456 | 0.5747 | 0.2322 | 0.3685 | | 0.9909 | 55.0 | 37070 | 1.0179 | 0.3014 | 0.614 | 0.2664 | 0.137 | 0.3692 | 0.4866 | 0.1095 | 0.355 | 0.4697 | 0.2856 | 0.538 | 0.654 | 0.1924 | 0.4494 | 0.4548 | 0.5721 | 0.2572 | 0.3876 | | 0.9824 | 56.0 | 37744 | 1.0204 | 0.3041 | 0.6131 | 0.2713 | 0.1378 | 0.3752 | 0.4993 | 0.1176 | 0.3626 | 0.4726 | 0.2844 | 0.5382 | 0.6799 | 0.1936 | 0.4484 | 0.461 | 0.5794 | 0.2578 | 0.39 | | 0.9846 | 57.0 | 38418 | 1.0147 | 0.3015 | 0.6003 | 0.2691 | 0.1371 | 0.371 | 0.506 | 0.1177 | 0.3616 | 0.4654 | 0.2781 | 0.534 | 0.6706 | 0.2033 | 0.4593 | 0.4513 | 0.5634 | 0.25 | 0.3735 | | 0.9575 | 58.0 | 39092 | 1.0353 | 0.2965 | 0.5951 | 0.2558 | 0.1284 | 0.3816 | 0.4743 | 0.1115 | 0.3576 | 0.4725 | 0.296 | 0.5441 | 0.658 | 0.1754 | 0.4413 | 0.4674 | 0.5962 | 0.2465 | 0.3801 | | 0.9534 | 59.0 | 39766 | 1.0171 | 0.3013 | 0.6065 | 0.2579 | 0.1376 | 0.3672 | 0.4851 | 0.114 | 0.3641 | 0.4706 | 0.2906 | 0.5278 | 0.6724 | 0.1951 | 0.4507 | 0.4612 | 0.5817 | 0.2477 | 0.3795 | | 0.9647 | 60.0 | 40440 | 1.0087 | 0.3039 | 0.6032 | 0.2574 | 0.1397 | 0.3725 | 0.4939 | 0.1142 | 0.3628 | 0.4762 | 0.2996 | 0.5378 | 0.6726 | 0.1811 | 0.4549 | 0.4698 | 0.5779 | 0.2609 | 0.3958 | | 0.955 | 61.0 | 41114 | 1.0005 | 0.3042 | 0.6139 | 0.2629 | 0.139 | 0.3747 | 0.4977 | 0.1137 | 0.3612 | 0.4819 | 0.3019 | 0.5541 | 0.6822 | 0.1966 | 0.4724 | 0.4632 | 0.5792 | 0.2528 | 0.394 | | 0.9578 | 62.0 | 41788 | 1.0119 | 0.3033 | 0.5942 | 0.2689 | 0.1347 | 0.3767 | 0.4916 | 0.1172 | 0.3611 | 0.4738 | 0.2781 | 0.5484 | 0.6538 | 0.1957 | 0.4496 | 0.4595 | 0.5801 | 0.2547 | 0.3918 | | 0.9479 | 63.0 | 42462 | 0.9970 | 0.3071 | 0.6156 | 0.2735 | 0.1444 | 0.3751 | 0.5048 | 0.1148 | 0.3625 | 0.4795 | 0.2921 | 0.5521 | 0.6759 | 0.2043 | 0.4591 | 0.4754 | 0.6064 | 0.2415 | 0.373 | | 0.9451 | 64.0 | 43136 | 0.9811 | 0.3188 | 0.631 | 0.2818 | 0.1653 | 0.3851 | 0.5075 | 0.1188 | 0.3737 | 0.4914 | 0.3064 | 0.5666 | 0.6819 | 0.2076 | 0.4673 | 0.4899 | 0.6165 | 0.2589 | 0.3903 | | 0.9343 | 65.0 | 43810 | 0.9991 | 0.3086 | 0.6282 | 0.2609 | 0.1535 | 0.3664 | 0.4856 | 0.1166 | 0.3672 | 0.4778 | 0.2992 | 0.5346 | 0.666 | 0.1954 | 0.451 | 0.4667 | 0.5866 | 0.2636 | 0.3959 | | 0.9376 | 66.0 | 44484 | 1.0005 | 0.298 | 0.6142 | 0.2512 | 0.1428 | 0.3717 | 0.4918 | 0.1119 | 0.3572 | 0.4675 | 0.2789 | 0.5377 | 0.6779 | 0.1863 | 0.4488 | 0.4535 | 0.5673 | 0.2544 | 0.3865 | | 0.9576 | 67.0 | 45158 | 1.0466 | 0.2933 | 0.5895 | 0.256 | 0.1229 | 0.374 | 0.4706 | 0.1142 | 0.3558 | 0.462 | 0.2572 | 0.5465 | 0.6613 | 0.2047 | 0.4525 | 0.4428 | 0.5714 | 0.2324 | 0.3622 | | 0.9431 | 68.0 | 45832 | 1.0323 | 0.3069 | 0.6177 | 0.2644 | 0.1538 | 0.3787 | 0.4848 | 0.1148 | 0.3627 | 0.4683 | 0.2848 | 0.5395 | 0.6538 | 0.1913 | 0.4484 | 0.4545 | 0.5606 | 0.275 | 0.3958 | | 0.9422 | 69.0 | 46506 | 1.0326 | 0.2959 | 0.6173 | 0.2394 | 0.1413 | 0.3551 | 0.4762 | 0.1125 | 0.3596 | 0.4676 | 0.29 | 0.5364 | 0.6402 | 0.1948 | 0.458 | 0.4394 | 0.56 | 0.2536 | 0.3847 | | 0.9305 | 70.0 | 47180 | 1.0143 | 0.3075 | 0.6141 | 0.2725 | 0.13 | 0.381 | 0.5014 | 0.117 | 0.3672 | 0.4777 | 0.2862 | 0.5588 | 0.6698 | 0.2209 | 0.4712 | 0.4692 | 0.5953 | 0.2325 | 0.3665 | | 0.9441 | 71.0 | 47854 | 1.0367 | 0.306 | 0.615 | 0.2636 | 0.129 | 0.381 | 0.5 | 0.1152 | 0.3621 | 0.467 | 0.2636 | 0.5479 | 0.6694 | 0.203 | 0.4461 | 0.4705 | 0.5917 | 0.2446 | 0.3632 | | 0.9395 | 72.0 | 48528 | 1.0061 | 0.3082 | 0.6214 | 0.2676 | 0.1601 | 0.3709 | 0.4941 | 0.1118 | 0.3664 | 0.4769 | 0.3152 | 0.5422 | 0.6463 | 0.1871 | 0.4447 | 0.4611 | 0.5833 | 0.2764 | 0.4027 | | 0.9271 | 73.0 | 49202 | 0.9797 | 0.3129 | 0.6364 | 0.2697 | 0.1432 | 0.378 | 0.4921 | 0.1185 | 0.3713 | 0.4883 | 0.305 | 0.5545 | 0.6736 | 0.2148 | 0.4801 | 0.4565 | 0.5894 | 0.2675 | 0.3953 | | 0.9239 | 74.0 | 49876 | 0.9843 | 0.3237 | 0.6409 | 0.2844 | 0.1558 | 0.3953 | 0.4989 | 0.1209 | 0.3791 | 0.4926 | 0.3094 | 0.5589 | 0.6721 | 0.2118 | 0.4772 | 0.4719 | 0.5843 | 0.2874 | 0.4162 | | 0.9236 | 75.0 | 50550 | 0.9649 | 0.3312 | 0.6391 | 0.3052 | 0.1805 | 0.397 | 0.5042 | 0.1225 | 0.3792 | 0.4966 | 0.3223 | 0.5652 | 0.666 | 0.2222 | 0.481 | 0.489 | 0.5977 | 0.2824 | 0.4112 | | 0.9191 | 76.0 | 51224 | 0.9947 | 0.3198 | 0.624 | 0.2893 | 0.1705 | 0.3875 | 0.4904 | 0.1159 | 0.3727 | 0.4956 | 0.3186 | 0.5633 | 0.6799 | 0.2021 | 0.4807 | 0.4768 | 0.591 | 0.2805 | 0.4149 | | 0.9025 | 77.0 | 51898 | 1.0002 | 0.3125 | 0.6273 | 0.2792 | 0.152 | 0.3829 | 0.5067 | 0.1155 | 0.3671 | 0.4826 | 0.296 | 0.5487 | 0.6858 | 0.1979 | 0.4706 | 0.4724 | 0.5828 | 0.2673 | 0.3944 | | 0.9002 | 78.0 | 52572 | 0.9713 | 0.3269 | 0.6441 | 0.291 | 0.1672 | 0.3953 | 0.5014 | 0.1174 | 0.3806 | 0.4862 | 0.2991 | 0.5549 | 0.6714 | 0.2061 | 0.4607 | 0.4896 | 0.5931 | 0.285 | 0.4048 | | 0.9161 | 79.0 | 53246 | 0.9722 | 0.3257 | 0.6387 | 0.2961 | 0.1583 | 0.4061 | 0.4885 | 0.1186 | 0.3795 | 0.4885 | 0.3039 | 0.5638 | 0.6599 | 0.205 | 0.4591 | 0.4971 | 0.602 | 0.275 | 0.4044 | | 0.8976 | 80.0 | 53920 | 0.9792 | 0.3129 | 0.6328 | 0.2735 | 0.1502 | 0.3805 | 0.4845 | 0.1189 | 0.3692 | 0.4815 | 0.3082 | 0.5371 | 0.6575 | 0.2097 | 0.462 | 0.458 | 0.5721 | 0.271 | 0.4105 | | 0.8937 | 81.0 | 54594 | 0.9829 | 0.3193 | 0.6462 | 0.287 | 0.166 | 0.3862 | 0.4792 | 0.1193 | 0.3736 | 0.4833 | 0.3041 | 0.5409 | 0.6557 | 0.205 | 0.4603 | 0.4706 | 0.5853 | 0.2823 | 0.4043 | | 0.9052 | 82.0 | 55268 | 0.9866 | 0.328 | 0.6347 | 0.3024 | 0.1747 | 0.397 | 0.4929 | 0.1201 | 0.375 | 0.492 | 0.3141 | 0.5529 | 0.6774 | 0.2116 | 0.4759 | 0.4934 | 0.5951 | 0.2791 | 0.405 | | 0.8972 | 83.0 | 55942 | 0.9669 | 0.3278 | 0.6458 | 0.2873 | 0.1753 | 0.399 | 0.507 | 0.1198 | 0.3789 | 0.4941 | 0.3131 | 0.5639 | 0.6744 | 0.2186 | 0.481 | 0.4884 | 0.5974 | 0.2766 | 0.4039 | | 0.8934 | 84.0 | 56616 | 0.9701 | 0.323 | 0.6436 | 0.2876 | 0.1568 | 0.4018 | 0.4937 | 0.1203 | 0.3776 | 0.4865 | 0.2984 | 0.5618 | 0.6769 | 0.2167 | 0.4705 | 0.485 | 0.599 | 0.2674 | 0.3901 | | 0.8985 | 85.0 | 57290 | 0.9505 | 0.3303 | 0.6579 | 0.29 | 0.1764 | 0.4045 | 0.5082 | 0.1221 | 0.382 | 0.4993 | 0.3313 | 0.5661 | 0.684 | 0.2114 | 0.4853 | 0.4987 | 0.6056 | 0.2808 | 0.4069 | | 0.8829 | 86.0 | 57964 | 0.9648 | 0.3164 | 0.6437 | 0.2728 | 0.1558 | 0.3957 | 0.4941 | 0.1207 | 0.3756 | 0.4869 | 0.3155 | 0.5485 | 0.6746 | 0.216 | 0.474 | 0.4564 | 0.5753 | 0.2766 | 0.4113 | | 0.8992 | 87.0 | 58638 | 1.0045 | 0.3058 | 0.6105 | 0.2732 | 0.1346 | 0.3941 | 0.502 | 0.1182 | 0.3658 | 0.4697 | 0.2734 | 0.5427 | 0.6796 | 0.1981 | 0.454 | 0.4597 | 0.5773 | 0.2596 | 0.3778 | | 0.892 | 88.0 | 59312 | 0.9759 | 0.3218 | 0.6415 | 0.2847 | 0.1782 | 0.3844 | 0.4847 | 0.1169 | 0.3754 | 0.4861 | 0.3259 | 0.5415 | 0.6484 | 0.2071 | 0.4643 | 0.4796 | 0.5873 | 0.2787 | 0.4068 | | 0.8904 | 89.0 | 59986 | 0.9567 | 0.3394 | 0.6613 | 0.3054 | 0.1847 | 0.4101 | 0.494 | 0.1215 | 0.3884 | 0.4955 | 0.3268 | 0.5552 | 0.6596 | 0.2218 | 0.4668 | 0.5035 | 0.6069 | 0.293 | 0.4129 | | 0.8864 | 90.0 | 60660 | 0.9877 | 0.3248 | 0.641 | 0.2916 | 0.1694 | 0.3945 | 0.4758 | 0.1192 | 0.3788 | 0.4838 | 0.2952 | 0.5492 | 0.6629 | 0.212 | 0.4621 | 0.4807 | 0.5912 | 0.2818 | 0.3981 | | 0.8781 | 91.0 | 61334 | 0.9849 | 0.3261 | 0.6402 | 0.2899 | 0.1761 | 0.4013 | 0.4945 | 0.1178 | 0.3812 | 0.4864 | 0.3098 | 0.5569 | 0.6573 | 0.2094 | 0.4653 | 0.491 | 0.5913 | 0.2778 | 0.4026 | | 0.8904 | 92.0 | 62008 | 0.9475 | 0.3312 | 0.6614 | 0.2863 | 0.1787 | 0.3999 | 0.5045 | 0.119 | 0.3828 | 0.4958 | 0.3147 | 0.5539 | 0.6744 | 0.2215 | 0.48 | 0.4827 | 0.5971 | 0.2892 | 0.4105 | | 0.8783 | 93.0 | 62682 | 0.9780 | 0.3273 | 0.6475 | 0.2883 | 0.1683 | 0.3992 | 0.4984 | 0.1206 | 0.3815 | 0.4892 | 0.3067 | 0.5612 | 0.6683 | 0.2066 | 0.4677 | 0.4963 | 0.6042 | 0.2789 | 0.3957 | | 0.8868 | 94.0 | 63356 | 0.9746 | 0.3174 | 0.6521 | 0.2671 | 0.1616 | 0.3947 | 0.497 | 0.1175 | 0.3728 | 0.4736 | 0.2928 | 0.5429 | 0.6784 | 0.1966 | 0.4493 | 0.4861 | 0.5936 | 0.2694 | 0.3778 | | 0.875 | 95.0 | 64030 | 0.9725 | 0.3269 | 0.6426 | 0.2893 | 0.1774 | 0.3963 | 0.4761 | 0.116 | 0.3774 | 0.4863 | 0.303 | 0.5487 | 0.6627 | 0.1941 | 0.4545 | 0.4957 | 0.5964 | 0.2909 | 0.4079 | | 0.8652 | 96.0 | 64704 | 0.9415 | 0.337 | 0.6644 | 0.3015 | 0.1829 | 0.41 | 0.5059 | 0.12 | 0.3925 | 0.497 | 0.3167 | 0.5691 | 0.676 | 0.2253 | 0.4811 | 0.5043 | 0.6047 | 0.2815 | 0.405 | | 0.857 | 97.0 | 65378 | 0.9420 | 0.3391 | 0.671 | 0.2965 | 0.1913 | 0.4082 | 0.5038 | 0.1183 | 0.3907 | 0.4944 | 0.3169 | 0.5608 | 0.6833 | 0.2217 | 0.4691 | 0.5028 | 0.6008 | 0.2927 | 0.4132 | | 0.8668 | 98.0 | 66052 | 0.9563 | 0.3344 | 0.6578 | 0.2896 | 0.1764 | 0.4059 | 0.4994 | 0.119 | 0.3849 | 0.4944 | 0.3103 | 0.564 | 0.6725 | 0.2138 | 0.4641 | 0.493 | 0.6013 | 0.2963 | 0.4178 | | 0.8671 | 99.0 | 66726 | 0.9706 | 0.3213 | 0.6491 | 0.2762 | 0.1767 | 0.3852 | 0.5062 | 0.1146 | 0.3801 | 0.4858 | 0.3316 | 0.5428 | 0.6644 | 0.1875 | 0.4527 | 0.4843 | 0.5925 | 0.2921 | 0.4122 | | 0.8659 | 100.0 | 67400 | 0.9395 | 0.3433 | 0.6648 | 0.3058 | 0.1904 | 0.4175 | 0.5218 | 0.1219 | 0.3938 | 0.5023 | 0.3281 | 0.5733 | 0.6826 | 0.2194 | 0.4728 | 0.5166 | 0.6155 | 0.294 | 0.4187 | | 0.8529 | 101.0 | 68074 | 0.9567 | 0.3379 | 0.6574 | 0.2999 | 0.1795 | 0.4112 | 0.5067 | 0.1203 | 0.3837 | 0.4982 | 0.3237 | 0.5676 | 0.6709 | 0.2113 | 0.4636 | 0.507 | 0.6129 | 0.2955 | 0.418 | | 0.8539 | 102.0 | 68748 | 0.9393 | 0.342 | 0.671 | 0.2987 | 0.1892 | 0.4127 | 0.5174 | 0.1237 | 0.39 | 0.5016 | 0.3248 | 0.5696 | 0.6834 | 0.2155 | 0.4684 | 0.5162 | 0.6145 | 0.2941 | 0.422 | | 0.8419 | 103.0 | 69422 | 0.9411 | 0.3433 | 0.6691 | 0.3064 | 0.1895 | 0.4056 | 0.5045 | 0.1204 | 0.3904 | 0.4993 | 0.3258 | 0.5585 | 0.6776 | 0.2156 | 0.466 | 0.5161 | 0.6185 | 0.2983 | 0.4134 | | 0.8423 | 104.0 | 70096 | 0.9401 | 0.3462 | 0.6765 | 0.3175 | 0.2003 | 0.4128 | 0.5127 | 0.1215 | 0.3925 | 0.5013 | 0.3409 | 0.5576 | 0.6768 | 0.2206 | 0.4736 | 0.5086 | 0.6039 | 0.3096 | 0.4263 | | 0.8428 | 105.0 | 70770 | 0.9328 | 0.3399 | 0.6628 | 0.2988 | 0.1781 | 0.4138 | 0.5098 | 0.1223 | 0.3879 | 0.498 | 0.3273 | 0.5627 | 0.6661 | 0.2248 | 0.4817 | 0.4986 | 0.6008 | 0.2962 | 0.4116 | | 0.8431 | 106.0 | 71444 | 0.9601 | 0.3356 | 0.6542 | 0.3056 | 0.1798 | 0.4043 | 0.4963 | 0.1203 | 0.385 | 0.4944 | 0.3128 | 0.5512 | 0.6908 | 0.2008 | 0.4548 | 0.5057 | 0.6147 | 0.3003 | 0.4137 | | 0.841 | 107.0 | 72118 | 0.9473 | 0.3371 | 0.6537 | 0.2891 | 0.1764 | 0.407 | 0.534 | 0.1239 | 0.3885 | 0.4983 | 0.3059 | 0.5687 | 0.6988 | 0.2091 | 0.4691 | 0.5072 | 0.6121 | 0.2951 | 0.4136 | | 0.8371 | 108.0 | 72792 | 0.9739 | 0.3416 | 0.6564 | 0.3032 | 0.1892 | 0.4142 | 0.509 | 0.1229 | 0.3927 | 0.4974 | 0.3211 | 0.5685 | 0.6692 | 0.2085 | 0.4636 | 0.5161 | 0.6155 | 0.3002 | 0.413 | | 0.843 | 109.0 | 73466 | 0.9271 | 0.3472 | 0.6755 | 0.3147 | 0.204 | 0.4107 | 0.5176 | 0.1226 | 0.3944 | 0.5048 | 0.3402 | 0.5614 | 0.6771 | 0.2184 | 0.4894 | 0.5158 | 0.6062 | 0.3074 | 0.4187 | | 0.8392 | 110.0 | 74140 | 0.9525 | 0.3493 | 0.672 | 0.3123 | 0.1894 | 0.4279 | 0.5139 | 0.125 | 0.3966 | 0.5024 | 0.3236 | 0.5779 | 0.6804 | 0.2205 | 0.4743 | 0.5259 | 0.6217 | 0.3016 | 0.4111 | | 0.8363 | 111.0 | 74814 | 0.9500 | 0.3372 | 0.6655 | 0.2969 | 0.188 | 0.4077 | 0.5044 | 0.123 | 0.387 | 0.4988 | 0.3337 | 0.5579 | 0.6861 | 0.2169 | 0.4801 | 0.5005 | 0.6067 | 0.2943 | 0.4095 | | 0.8327 | 112.0 | 75488 | 0.9441 | 0.3476 | 0.6802 | 0.3042 | 0.1958 | 0.4161 | 0.5233 | 0.1214 | 0.3935 | 0.5041 | 0.3348 | 0.5717 | 0.6777 | 0.2349 | 0.4849 | 0.5061 | 0.6109 | 0.3019 | 0.4165 | | 0.8383 | 113.0 | 76162 | 0.9553 | 0.3384 | 0.6695 | 0.3031 | 0.1759 | 0.4156 | 0.5094 | 0.1235 | 0.3867 | 0.4916 | 0.3118 | 0.5633 | 0.6824 | 0.218 | 0.4643 | 0.5271 | 0.6271 | 0.2702 | 0.3833 | | 0.8289 | 114.0 | 76836 | 0.9386 | 0.3513 | 0.6849 | 0.317 | 0.1902 | 0.4215 | 0.5248 | 0.1255 | 0.3975 | 0.5078 | 0.3418 | 0.565 | 0.696 | 0.2302 | 0.4829 | 0.5207 | 0.6266 | 0.303 | 0.4138 | | 0.8277 | 115.0 | 77510 | 0.9126 | 0.3588 | 0.6899 | 0.3214 | 0.2084 | 0.417 | 0.5419 | 0.1296 | 0.3984 | 0.5136 | 0.3474 | 0.5653 | 0.7002 | 0.2294 | 0.4744 | 0.5313 | 0.6307 | 0.3157 | 0.4356 | | 0.8258 | 116.0 | 78184 | 0.9158 | 0.3468 | 0.6842 | 0.3006 | 0.2008 | 0.413 | 0.5166 | 0.1225 | 0.3942 | 0.5092 | 0.349 | 0.5687 | 0.6854 | 0.2222 | 0.4852 | 0.5146 | 0.6237 | 0.3037 | 0.4189 | | 0.8254 | 117.0 | 78858 | 0.9784 | 0.3367 | 0.6674 | 0.2911 | 0.1807 | 0.408 | 0.5215 | 0.122 | 0.3862 | 0.4903 | 0.3199 | 0.5604 | 0.6789 | 0.2159 | 0.4644 | 0.4992 | 0.6005 | 0.295 | 0.4061 | | 0.8227 | 118.0 | 79532 | 0.9196 | 0.3577 | 0.6909 | 0.3203 | 0.2076 | 0.4175 | 0.5176 | 0.1251 | 0.401 | 0.5189 | 0.3517 | 0.5745 | 0.6897 | 0.2276 | 0.4797 | 0.5282 | 0.6343 | 0.3174 | 0.4426 | | 0.815 | 119.0 | 80206 | 0.9294 | 0.3503 | 0.6839 | 0.3158 | 0.1935 | 0.4198 | 0.508 | 0.1238 | 0.3932 | 0.5051 | 0.3339 | 0.5705 | 0.6839 | 0.2249 | 0.4702 | 0.5143 | 0.6149 | 0.3118 | 0.4302 | | 0.8157 | 120.0 | 80880 | 0.9266 | 0.3496 | 0.6771 | 0.3085 | 0.192 | 0.416 | 0.5157 | 0.1233 | 0.3969 | 0.5065 | 0.3455 | 0.5631 | 0.6867 | 0.226 | 0.4812 | 0.5113 | 0.6044 | 0.3115 | 0.4339 | | 0.803 | 121.0 | 81554 | 0.9207 | 0.3522 | 0.6994 | 0.2967 | 0.212 | 0.4152 | 0.5082 | 0.1229 | 0.3919 | 0.5107 | 0.3581 | 0.5669 | 0.6635 | 0.2353 | 0.4873 | 0.5013 | 0.6046 | 0.3199 | 0.4402 | | 0.8069 | 122.0 | 82228 | 0.9433 | 0.3413 | 0.6782 | 0.2963 | 0.1838 | 0.4043 | 0.501 | 0.125 | 0.3863 | 0.4943 | 0.3196 | 0.5574 | 0.6757 | 0.2246 | 0.4778 | 0.5057 | 0.6072 | 0.2935 | 0.3979 | | 0.8027 | 123.0 | 82902 | 0.9324 | 0.3567 | 0.6809 | 0.3193 | 0.2019 | 0.4193 | 0.5365 | 0.1284 | 0.4005 | 0.5096 | 0.3597 | 0.5663 | 0.69 | 0.2201 | 0.4698 | 0.5288 | 0.6263 | 0.321 | 0.4327 | | 0.7997 | 124.0 | 83576 | 0.9269 | 0.3514 | 0.6773 | 0.3127 | 0.1931 | 0.4206 | 0.5108 | 0.1267 | 0.3972 | 0.5094 | 0.3444 | 0.5798 | 0.6852 | 0.2339 | 0.4863 | 0.5202 | 0.625 | 0.3003 | 0.4169 | | 0.7842 | 125.0 | 84250 | 0.9350 | 0.3533 | 0.6834 | 0.311 | 0.2027 | 0.4267 | 0.5216 | 0.1249 | 0.396 | 0.5069 | 0.3302 | 0.5708 | 0.6897 | 0.2251 | 0.4734 | 0.5137 | 0.6136 | 0.3212 | 0.4338 | | 0.7911 | 126.0 | 84924 | 0.9341 | 0.3415 | 0.6854 | 0.2923 | 0.1878 | 0.4115 | 0.5173 | 0.1213 | 0.389 | 0.5017 | 0.3348 | 0.5708 | 0.6804 | 0.2212 | 0.4755 | 0.5044 | 0.6113 | 0.2988 | 0.4183 | | 0.795 | 127.0 | 85598 | 0.9290 | 0.3521 | 0.6987 | 0.3081 | 0.2037 | 0.4196 | 0.5203 | 0.1243 | 0.3965 | 0.5027 | 0.3448 | 0.5608 | 0.673 | 0.2279 | 0.4791 | 0.5088 | 0.599 | 0.3196 | 0.4301 | | 0.7913 | 128.0 | 86272 | 0.9014 | 0.3665 | 0.7039 | 0.3274 | 0.2171 | 0.434 | 0.5217 | 0.1245 | 0.4073 | 0.5171 | 0.3538 | 0.5804 | 0.6872 | 0.2441 | 0.4885 | 0.5227 | 0.6203 | 0.3327 | 0.4425 | | 0.8024 | 129.0 | 86946 | 0.9343 | 0.3526 | 0.6873 | 0.3128 | 0.2008 | 0.4171 | 0.517 | 0.124 | 0.3944 | 0.5035 | 0.3368 | 0.5583 | 0.6855 | 0.2363 | 0.4816 | 0.5101 | 0.6067 | 0.3115 | 0.4223 | | 0.7843 | 130.0 | 87620 | 0.9140 | 0.3621 | 0.6958 | 0.3192 | 0.2116 | 0.4287 | 0.5193 | 0.1256 | 0.4057 | 0.5133 | 0.3561 | 0.5713 | 0.6821 | 0.2311 | 0.4804 | 0.5303 | 0.6237 | 0.3249 | 0.4359 | | 0.7838 | 131.0 | 88294 | 0.9106 | 0.3595 | 0.6948 | 0.3128 | 0.2067 | 0.4276 | 0.5219 | 0.1259 | 0.4097 | 0.5186 | 0.3534 | 0.5829 | 0.6998 | 0.2371 | 0.4949 | 0.5243 | 0.6289 | 0.3171 | 0.4319 | | 0.7874 | 132.0 | 88968 | 0.9256 | 0.3548 | 0.6956 | 0.3141 | 0.2083 | 0.4152 | 0.5121 | 0.1208 | 0.4011 | 0.504 | 0.3422 | 0.5547 | 0.6818 | 0.2255 | 0.4768 | 0.5211 | 0.61 | 0.3178 | 0.4252 | | 0.7815 | 133.0 | 89642 | 0.9369 | 0.358 | 0.6973 | 0.3153 | 0.2093 | 0.4241 | 0.5212 | 0.1249 | 0.3996 | 0.5074 | 0.3349 | 0.5688 | 0.6878 | 0.2339 | 0.4744 | 0.5253 | 0.6232 | 0.3149 | 0.4245 | | 0.7787 | 134.0 | 90316 | 0.9380 | 0.3584 | 0.6903 | 0.3202 | 0.2172 | 0.4207 | 0.5172 | 0.1209 | 0.4024 | 0.5127 | 0.3404 | 0.5719 | 0.6907 | 0.2306 | 0.4773 | 0.532 | 0.6306 | 0.3124 | 0.4303 | | 0.7758 | 135.0 | 90990 | 0.9247 | 0.3603 | 0.6984 | 0.3199 | 0.2095 | 0.431 | 0.5111 | 0.1243 | 0.404 | 0.5148 | 0.3493 | 0.5771 | 0.6828 | 0.2328 | 0.4862 | 0.5255 | 0.6281 | 0.3226 | 0.4301 | | 0.7685 | 136.0 | 91664 | 0.9077 | 0.3582 | 0.6927 | 0.3225 | 0.2121 | 0.4284 | 0.502 | 0.1249 | 0.4034 | 0.5167 | 0.3515 | 0.5834 | 0.6809 | 0.222 | 0.4854 | 0.5355 | 0.6359 | 0.3171 | 0.4288 | | 0.7715 | 137.0 | 92338 | 0.9177 | 0.3589 | 0.6928 | 0.3281 | 0.2065 | 0.4255 | 0.5242 | 0.126 | 0.4038 | 0.5112 | 0.3341 | 0.5752 | 0.6956 | 0.2259 | 0.4768 | 0.5313 | 0.6209 | 0.3197 | 0.4359 | | 0.7791 | 138.0 | 93012 | 0.9369 | 0.3584 | 0.697 | 0.3212 | 0.2059 | 0.4234 | 0.498 | 0.125 | 0.4008 | 0.5107 | 0.3362 | 0.5722 | 0.6837 | 0.2253 | 0.4677 | 0.5289 | 0.625 | 0.3212 | 0.4395 | | 0.7696 | 139.0 | 93686 | 0.9342 | 0.3528 | 0.685 | 0.3189 | 0.2025 | 0.4157 | 0.5091 | 0.1213 | 0.3973 | 0.5084 | 0.3457 | 0.5647 | 0.6786 | 0.2141 | 0.4687 | 0.5204 | 0.6183 | 0.324 | 0.4383 | | 0.7708 | 140.0 | 94360 | 0.9159 | 0.3558 | 0.6961 | 0.3299 | 0.202 | 0.4249 | 0.5104 | 0.1246 | 0.3996 | 0.51 | 0.3354 | 0.5756 | 0.6758 | 0.2243 | 0.4783 | 0.5192 | 0.6203 | 0.3239 | 0.4314 | | 0.7646 | 141.0 | 95034 | 0.9219 | 0.3568 | 0.6901 | 0.3169 | 0.2012 | 0.4194 | 0.5181 | 0.1239 | 0.4025 | 0.5137 | 0.3454 | 0.5737 | 0.6785 | 0.2238 | 0.4807 | 0.5251 | 0.6219 | 0.3217 | 0.4385 | | 0.7674 | 142.0 | 95708 | 0.9096 | 0.3575 | 0.6982 | 0.3154 | 0.2063 | 0.4157 | 0.513 | 0.127 | 0.4045 | 0.5181 | 0.3504 | 0.5767 | 0.677 | 0.2356 | 0.491 | 0.5129 | 0.6186 | 0.3239 | 0.4446 | | 0.767 | 143.0 | 96382 | 0.9192 | 0.3593 | 0.6997 | 0.3248 | 0.2059 | 0.4301 | 0.5155 | 0.1257 | 0.4044 | 0.517 | 0.3481 | 0.5772 | 0.6821 | 0.2281 | 0.48 | 0.5251 | 0.6291 | 0.3246 | 0.4419 | | 0.7725 | 144.0 | 97056 | 0.9118 | 0.3645 | 0.6943 | 0.3364 | 0.2289 | 0.4229 | 0.5149 | 0.1268 | 0.4078 | 0.5253 | 0.3698 | 0.5823 | 0.6831 | 0.2382 | 0.4886 | 0.5239 | 0.641 | 0.3313 | 0.4463 | | 0.7513 | 145.0 | 97730 | 0.9032 | 0.3582 | 0.6976 | 0.3163 | 0.2137 | 0.4206 | 0.5186 | 0.1253 | 0.399 | 0.5188 | 0.3494 | 0.5822 | 0.6808 | 0.2355 | 0.4932 | 0.5171 | 0.6237 | 0.3218 | 0.4396 | | 0.7616 | 146.0 | 98404 | 0.9334 | 0.3589 | 0.691 | 0.332 | 0.2037 | 0.4251 | 0.5086 | 0.1265 | 0.401 | 0.5094 | 0.3381 | 0.5696 | 0.6734 | 0.2339 | 0.4769 | 0.516 | 0.6136 | 0.3268 | 0.4378 | | 0.7522 | 147.0 | 99078 | 0.9363 | 0.3411 | 0.6925 | 0.2947 | 0.1824 | 0.4081 | 0.5223 | 0.1222 | 0.3863 | 0.4952 | 0.3158 | 0.5594 | 0.6856 | 0.2294 | 0.4721 | 0.5007 | 0.6042 | 0.2932 | 0.4092 | | 0.7521 | 148.0 | 99752 | 0.9323 | 0.353 | 0.694 | 0.3218 | 0.1999 | 0.4198 | 0.5247 | 0.125 | 0.397 | 0.5008 | 0.334 | 0.5615 | 0.6902 | 0.2293 | 0.4729 | 0.5317 | 0.6206 | 0.2981 | 0.4088 | | 0.7349 | 149.0 | 100426 | 0.9164 | 0.3652 | 0.6992 | 0.3364 | 0.2146 | 0.4328 | 0.5255 | 0.1274 | 0.405 | 0.5128 | 0.3483 | 0.5717 | 0.6951 | 0.2412 | 0.4809 | 0.5344 | 0.6258 | 0.3201 | 0.4316 | | 0.7411 | 150.0 | 101100 | 0.9186 | 0.3618 | 0.7051 | 0.3256 | 0.2195 | 0.4211 | 0.5316 | 0.1263 | 0.4008 | 0.5147 | 0.3425 | 0.5725 | 0.699 | 0.232 | 0.4831 | 0.5192 | 0.623 | 0.3342 | 0.4378 | | 0.7414 | 151.0 | 101774 | 0.9410 | 0.3481 | 0.6967 | 0.2979 | 0.2037 | 0.4108 | 0.5253 | 0.1243 | 0.3935 | 0.4969 | 0.3247 | 0.5573 | 0.6971 | 0.2325 | 0.473 | 0.5156 | 0.6139 | 0.2961 | 0.4038 | | 0.7483 | 152.0 | 102448 | 0.9197 | 0.36 | 0.706 | 0.3219 | 0.2113 | 0.4295 | 0.5198 | 0.1253 | 0.4031 | 0.5052 | 0.341 | 0.5733 | 0.6855 | 0.2445 | 0.4826 | 0.5167 | 0.6069 | 0.3189 | 0.4261 | | 0.7479 | 153.0 | 103122 | 0.9263 | 0.3626 | 0.7009 | 0.3281 | 0.2255 | 0.4219 | 0.5232 | 0.1259 | 0.3992 | 0.5126 | 0.3575 | 0.5689 | 0.6931 | 0.2301 | 0.4729 | 0.5335 | 0.6273 | 0.3241 | 0.4377 | | 0.7453 | 154.0 | 103796 | 0.9261 | 0.3472 | 0.6895 | 0.306 | 0.2092 | 0.4055 | 0.5186 | 0.1238 | 0.395 | 0.504 | 0.3423 | 0.5582 | 0.6901 | 0.2379 | 0.4854 | 0.5063 | 0.6042 | 0.2975 | 0.4223 | | 0.7385 | 155.0 | 104470 | 0.9238 | 0.3557 | 0.6999 | 0.3134 | 0.2171 | 0.4286 | 0.5169 | 0.1284 | 0.4038 | 0.5117 | 0.3532 | 0.5763 | 0.6915 | 0.2299 | 0.4867 | 0.5298 | 0.6278 | 0.3075 | 0.4206 | | 0.7455 | 156.0 | 105144 | 0.9065 | 0.3737 | 0.7106 | 0.3506 | 0.2334 | 0.4396 | 0.531 | 0.1281 | 0.4133 | 0.525 | 0.3686 | 0.5767 | 0.6969 | 0.2379 | 0.4797 | 0.5397 | 0.6422 | 0.3435 | 0.4532 | | 0.7295 | 157.0 | 105818 | 0.9046 | 0.368 | 0.7082 | 0.3382 | 0.2258 | 0.4293 | 0.5203 | 0.1279 | 0.4097 | 0.5214 | 0.3654 | 0.5738 | 0.689 | 0.2449 | 0.4872 | 0.5266 | 0.6289 | 0.3326 | 0.448 | | 0.719 | 158.0 | 106492 | 0.9235 | 0.3608 | 0.7025 | 0.3263 | 0.2144 | 0.4319 | 0.514 | 0.1241 | 0.4037 | 0.5157 | 0.345 | 0.5852 | 0.6885 | 0.2278 | 0.4793 | 0.5307 | 0.6322 | 0.3239 | 0.4354 | | 0.7288 | 159.0 | 107166 | 0.9041 | 0.3665 | 0.7148 | 0.3323 | 0.2195 | 0.4296 | 0.5263 | 0.1257 | 0.405 | 0.5189 | 0.3594 | 0.5722 | 0.6949 | 0.237 | 0.4821 | 0.5247 | 0.6232 | 0.3377 | 0.4514 | | 0.7212 | 160.0 | 107840 | 0.9100 | 0.366 | 0.7096 | 0.3289 | 0.2198 | 0.4288 | 0.5389 | 0.1267 | 0.4079 | 0.5165 | 0.3559 | 0.573 | 0.6983 | 0.2419 | 0.4888 | 0.5306 | 0.6245 | 0.3254 | 0.436 | | 0.7329 | 161.0 | 108514 | 0.9069 | 0.3618 | 0.7068 | 0.3258 | 0.2165 | 0.4287 | 0.5244 | 0.1251 | 0.4039 | 0.5189 | 0.358 | 0.5778 | 0.7006 | 0.2376 | 0.489 | 0.5286 | 0.626 | 0.3193 | 0.4418 | | 0.7277 | 162.0 | 109188 | 0.9315 | 0.3524 | 0.6896 | 0.3154 | 0.2006 | 0.4185 | 0.5229 | 0.1255 | 0.3975 | 0.5037 | 0.3338 | 0.5609 | 0.6952 | 0.2219 | 0.4696 | 0.5213 | 0.6203 | 0.3139 | 0.4211 | | 0.7259 | 163.0 | 109862 | 0.9106 | 0.3672 | 0.7106 | 0.3346 | 0.2218 | 0.4299 | 0.5228 | 0.129 | 0.4121 | 0.5201 | 0.3654 | 0.5769 | 0.6917 | 0.2394 | 0.4902 | 0.532 | 0.6258 | 0.3303 | 0.4443 | | 0.721 | 164.0 | 110536 | 0.9176 | 0.3631 | 0.6997 | 0.326 | 0.2306 | 0.4145 | 0.5214 | 0.1275 | 0.4073 | 0.5187 | 0.3682 | 0.5663 | 0.695 | 0.2233 | 0.4734 | 0.5376 | 0.6356 | 0.3283 | 0.447 | | 0.7185 | 165.0 | 111210 | 0.9117 | 0.3541 | 0.6993 | 0.3038 | 0.2154 | 0.4139 | 0.5239 | 0.125 | 0.4017 | 0.5093 | 0.3535 | 0.5622 | 0.688 | 0.2156 | 0.4852 | 0.5266 | 0.6155 | 0.3203 | 0.4273 | | 0.7195 | 166.0 | 111884 | 0.9102 | 0.3624 | 0.7079 | 0.3256 | 0.2167 | 0.4221 | 0.526 | 0.1245 | 0.4076 | 0.5142 | 0.3563 | 0.571 | 0.6949 | 0.2333 | 0.4828 | 0.527 | 0.6154 | 0.3268 | 0.4444 | | 0.7038 | 167.0 | 112558 | 0.9128 | 0.3598 | 0.7 | 0.3183 | 0.2203 | 0.4209 | 0.5171 | 0.1227 | 0.402 | 0.5156 | 0.3688 | 0.5662 | 0.6775 | 0.2155 | 0.4702 | 0.5313 | 0.6284 | 0.3327 | 0.4481 | | 0.7169 | 168.0 | 113232 | 0.9213 | 0.3699 | 0.7115 | 0.3385 | 0.2137 | 0.4351 | 0.5303 | 0.1265 | 0.4081 | 0.5178 | 0.3618 | 0.5748 | 0.6827 | 0.2378 | 0.4749 | 0.5268 | 0.6265 | 0.345 | 0.4519 | | 0.7131 | 169.0 | 113906 | 0.8994 | 0.3711 | 0.7156 | 0.3336 | 0.2216 | 0.4325 | 0.5238 | 0.1243 | 0.4114 | 0.5199 | 0.3716 | 0.5775 | 0.6868 | 0.241 | 0.4875 | 0.5336 | 0.6211 | 0.3387 | 0.4512 | | 0.7006 | 170.0 | 114580 | 0.9152 | 0.3638 | 0.7141 | 0.3274 | 0.2249 | 0.4315 | 0.5145 | 0.123 | 0.404 | 0.5108 | 0.3585 | 0.5702 | 0.6872 | 0.2308 | 0.4729 | 0.5316 | 0.6291 | 0.329 | 0.4303 | | 0.7079 | 171.0 | 115254 | 0.9157 | 0.3715 | 0.7098 | 0.346 | 0.2179 | 0.4358 | 0.534 | 0.1272 | 0.41 | 0.516 | 0.3513 | 0.575 | 0.688 | 0.229 | 0.4734 | 0.5438 | 0.6271 | 0.3418 | 0.4474 | | 0.7124 | 172.0 | 115928 | 0.9151 | 0.3702 | 0.7079 | 0.3351 | 0.2281 | 0.4318 | 0.5306 | 0.1264 | 0.409 | 0.5156 | 0.3583 | 0.5787 | 0.6863 | 0.2326 | 0.47 | 0.5402 | 0.6268 | 0.3379 | 0.45 | | 0.6943 | 173.0 | 116602 | 0.8994 | 0.3737 | 0.7191 | 0.343 | 0.2347 | 0.4341 | 0.535 | 0.1257 | 0.4123 | 0.5207 | 0.3651 | 0.5757 | 0.6867 | 0.2324 | 0.4764 | 0.5433 | 0.6299 | 0.3454 | 0.4558 | | 0.6954 | 174.0 | 117276 | 0.9231 | 0.3678 | 0.7175 | 0.3304 | 0.2209 | 0.4322 | 0.5201 | 0.1258 | 0.406 | 0.5146 | 0.3529 | 0.5687 | 0.6909 | 0.2324 | 0.4711 | 0.535 | 0.6263 | 0.3361 | 0.4463 | | 0.7029 | 175.0 | 117950 | 0.9124 | 0.3681 | 0.7049 | 0.3358 | 0.2149 | 0.4416 | 0.5207 | 0.1277 | 0.4076 | 0.5165 | 0.3455 | 0.5828 | 0.6818 | 0.2336 | 0.4814 | 0.5385 | 0.6217 | 0.3322 | 0.4465 | | 0.7015 | 176.0 | 118624 | 0.9070 | 0.3699 | 0.7113 | 0.3287 | 0.2223 | 0.4385 | 0.5302 | 0.1265 | 0.41 | 0.5193 | 0.3567 | 0.5772 | 0.688 | 0.2389 | 0.4885 | 0.5392 | 0.6296 | 0.3316 | 0.44 | | 0.6986 | 177.0 | 119298 | 0.9114 | 0.3705 | 0.7157 | 0.3371 | 0.223 | 0.4422 | 0.5259 | 0.1258 | 0.4102 | 0.5213 | 0.3576 | 0.5881 | 0.6911 | 0.2423 | 0.4891 | 0.5285 | 0.6221 | 0.3406 | 0.4527 | | 0.696 | 178.0 | 119972 | 0.9084 | 0.3735 | 0.7095 | 0.3413 | 0.2241 | 0.4423 | 0.5247 | 0.126 | 0.4118 | 0.5217 | 0.362 | 0.5814 | 0.6876 | 0.2397 | 0.4897 | 0.5403 | 0.627 | 0.3406 | 0.4484 | | 0.6947 | 179.0 | 120646 | 0.9165 | 0.3652 | 0.71 | 0.3222 | 0.2239 | 0.4324 | 0.5155 | 0.1244 | 0.4055 | 0.5199 | 0.3638 | 0.5765 | 0.6836 | 0.2297 | 0.4793 | 0.5322 | 0.6296 | 0.3338 | 0.4507 | | 0.6839 | 180.0 | 121320 | 0.9187 | 0.3694 | 0.7158 | 0.3367 | 0.2223 | 0.4332 | 0.5333 | 0.1257 | 0.4089 | 0.5156 | 0.3479 | 0.5724 | 0.6943 | 0.2401 | 0.479 | 0.5338 | 0.6297 | 0.3344 | 0.4382 | | 0.6827 | 181.0 | 121994 | 0.9069 | 0.3738 | 0.715 | 0.3385 | 0.2411 | 0.4343 | 0.5185 | 0.1249 | 0.4102 | 0.5229 | 0.3635 | 0.5758 | 0.6906 | 0.2369 | 0.4839 | 0.5393 | 0.6301 | 0.3453 | 0.4548 | | 0.6815 | 182.0 | 122668 | 0.8978 | 0.3707 | 0.7203 | 0.3308 | 0.2263 | 0.4343 | 0.517 | 0.1261 | 0.4088 | 0.5241 | 0.3718 | 0.5745 | 0.6894 | 0.2438 | 0.4919 | 0.5321 | 0.6296 | 0.3363 | 0.4507 | | 0.6839 | 183.0 | 123342 | 0.9090 | 0.367 | 0.7203 | 0.3306 | 0.2142 | 0.432 | 0.5231 | 0.1244 | 0.4028 | 0.5124 | 0.3462 | 0.5662 | 0.6937 | 0.2342 | 0.4722 | 0.5338 | 0.6222 | 0.3331 | 0.4428 | | 0.6862 | 184.0 | 124016 | 0.8963 | 0.3687 | 0.7183 | 0.3343 | 0.2208 | 0.4363 | 0.5313 | 0.1272 | 0.4081 | 0.5197 | 0.3655 | 0.5755 | 0.688 | 0.2328 | 0.4815 | 0.5341 | 0.6276 | 0.3392 | 0.45 | | 0.6679 | 185.0 | 124690 | 0.8899 | 0.3766 | 0.7263 | 0.3309 | 0.2418 | 0.4348 | 0.5213 | 0.126 | 0.4145 | 0.5292 | 0.3902 | 0.5748 | 0.686 | 0.2321 | 0.4819 | 0.5496 | 0.6405 | 0.3481 | 0.4653 | | 0.6733 | 186.0 | 125364 | 0.9037 | 0.3724 | 0.7205 | 0.335 | 0.2338 | 0.4345 | 0.5274 | 0.1255 | 0.4145 | 0.5277 | 0.381 | 0.58 | 0.6888 | 0.2312 | 0.4914 | 0.5429 | 0.6345 | 0.3431 | 0.4573 | | 0.6797 | 187.0 | 126038 | 0.9226 | 0.3715 | 0.7261 | 0.3296 | 0.2249 | 0.4362 | 0.5249 | 0.1272 | 0.4086 | 0.5195 | 0.3645 | 0.5731 | 0.6792 | 0.236 | 0.4852 | 0.5436 | 0.6304 | 0.335 | 0.4428 | | 0.6737 | 188.0 | 126712 | 0.8917 | 0.3766 | 0.7257 | 0.3377 | 0.2381 | 0.4389 | 0.5243 | 0.1255 | 0.4144 | 0.5284 | 0.3752 | 0.5831 | 0.6806 | 0.2334 | 0.4845 | 0.5425 | 0.6377 | 0.3538 | 0.4628 | | 0.6622 | 189.0 | 127386 | 0.9061 | 0.3755 | 0.724 | 0.3462 | 0.2286 | 0.4403 | 0.5293 | 0.1263 | 0.4124 | 0.525 | 0.369 | 0.5837 | 0.6843 | 0.2445 | 0.4921 | 0.5385 | 0.6268 | 0.3435 | 0.456 | | 0.6732 | 190.0 | 128060 | 0.9081 | 0.3662 | 0.7146 | 0.3294 | 0.2195 | 0.431 | 0.5138 | 0.128 | 0.4042 | 0.5206 | 0.3688 | 0.5747 | 0.6761 | 0.2282 | 0.4845 | 0.5328 | 0.6258 | 0.3374 | 0.4516 | | 0.6593 | 191.0 | 128734 | 0.8968 | 0.3729 | 0.7255 | 0.3414 | 0.226 | 0.4377 | 0.5332 | 0.1278 | 0.4121 | 0.5279 | 0.3801 | 0.5818 | 0.6885 | 0.2371 | 0.4925 | 0.545 | 0.6382 | 0.3365 | 0.453 | | 0.6631 | 192.0 | 129408 | 0.8903 | 0.3765 | 0.7278 | 0.3313 | 0.2312 | 0.4327 | 0.5252 | 0.1283 | 0.4148 | 0.5279 | 0.376 | 0.576 | 0.6854 | 0.2363 | 0.4896 | 0.5477 | 0.6374 | 0.3455 | 0.4567 | | 0.6583 | 193.0 | 130082 | 0.8890 | 0.379 | 0.727 | 0.342 | 0.242 | 0.4362 | 0.5392 | 0.1272 | 0.4163 | 0.5286 | 0.3787 | 0.5779 | 0.6999 | 0.2451 | 0.4891 | 0.5476 | 0.6426 | 0.3442 | 0.4542 | | 0.6583 | 194.0 | 130756 | 0.9139 | 0.3624 | 0.7256 | 0.3182 | 0.2216 | 0.4211 | 0.5211 | 0.1232 | 0.4034 | 0.5126 | 0.3521 | 0.563 | 0.6929 | 0.2344 | 0.4739 | 0.5179 | 0.6201 | 0.3348 | 0.4439 | | 0.6638 | 195.0 | 131430 | 0.9190 | 0.3627 | 0.7174 | 0.3231 | 0.2262 | 0.4209 | 0.5235 | 0.1263 | 0.4019 | 0.5129 | 0.359 | 0.5644 | 0.6883 | 0.2314 | 0.4791 | 0.5241 | 0.6188 | 0.3327 | 0.4408 | | 0.656 | 196.0 | 132104 | 0.9119 | 0.3735 | 0.7141 | 0.3528 | 0.2262 | 0.4352 | 0.5194 | 0.1282 | 0.4069 | 0.5195 | 0.3663 | 0.5683 | 0.6813 | 0.2313 | 0.4716 | 0.5328 | 0.6234 | 0.3565 | 0.4636 | | 0.6591 | 197.0 | 132778 | 0.9007 | 0.3758 | 0.7205 | 0.3428 | 0.2362 | 0.4328 | 0.5218 | 0.1281 | 0.413 | 0.5261 | 0.3806 | 0.5708 | 0.6915 | 0.2337 | 0.4859 | 0.54 | 0.6309 | 0.3535 | 0.4616 | | 0.6579 | 198.0 | 133452 | 0.9165 | 0.3727 | 0.7219 | 0.3365 | 0.2271 | 0.4335 | 0.5244 | 0.1263 | 0.4101 | 0.5182 | 0.3628 | 0.5737 | 0.6931 | 0.2417 | 0.4814 | 0.5356 | 0.624 | 0.3409 | 0.4492 | | 0.6506 | 199.0 | 134126 | 0.9074 | 0.3708 | 0.7188 | 0.3439 | 0.229 | 0.4328 | 0.518 | 0.1261 | 0.4145 | 0.5196 | 0.3716 | 0.5689 | 0.6889 | 0.2289 | 0.4773 | 0.5439 | 0.6348 | 0.3396 | 0.4465 | | 0.6562 | 200.0 | 134800 | 0.8944 | 0.3742 | 0.724 | 0.3406 | 0.2332 | 0.4334 | 0.5212 | 0.1246 | 0.4132 | 0.5188 | 0.3672 | 0.5666 | 0.6823 | 0.2273 | 0.4663 | 0.5397 | 0.6309 | 0.3555 | 0.4592 | | 0.6485 | 201.0 | 135474 | 0.8983 | 0.3768 | 0.7315 | 0.3439 | 0.2339 | 0.4421 | 0.5188 | 0.1247 | 0.4105 | 0.5222 | 0.3704 | 0.576 | 0.6861 | 0.2288 | 0.4705 | 0.5481 | 0.6333 | 0.3535 | 0.4629 | | 0.6583 | 202.0 | 136148 | 0.9362 | 0.3545 | 0.7029 | 0.3156 | 0.2045 | 0.435 | 0.5097 | 0.1218 | 0.3976 | 0.5059 | 0.3352 | 0.5695 | 0.689 | 0.2187 | 0.4677 | 0.5367 | 0.6275 | 0.308 | 0.4224 | | 0.6458 | 203.0 | 136822 | 0.9076 | 0.3721 | 0.7188 | 0.3398 | 0.2239 | 0.4348 | 0.5193 | 0.1264 | 0.4102 | 0.5206 | 0.3566 | 0.5785 | 0.6935 | 0.237 | 0.4825 | 0.5397 | 0.6268 | 0.3396 | 0.4524 | | 0.642 | 204.0 | 137496 | 0.9140 | 0.3664 | 0.7155 | 0.3346 | 0.2231 | 0.4351 | 0.5212 | 0.1268 | 0.402 | 0.5144 | 0.3449 | 0.5804 | 0.6941 | 0.2359 | 0.4738 | 0.5383 | 0.6278 | 0.325 | 0.4416 | | 0.645 | 205.0 | 138170 | 0.9019 | 0.3747 | 0.7289 | 0.3457 | 0.2277 | 0.4403 | 0.5268 | 0.128 | 0.4096 | 0.5228 | 0.3615 | 0.578 | 0.6981 | 0.2339 | 0.4745 | 0.5421 | 0.6348 | 0.3481 | 0.4591 | | 0.6376 | 206.0 | 138844 | 0.9077 | 0.3733 | 0.7243 | 0.3436 | 0.2337 | 0.4348 | 0.5275 | 0.1276 | 0.4098 | 0.521 | 0.3716 | 0.5731 | 0.6874 | 0.2361 | 0.4762 | 0.5498 | 0.6382 | 0.3339 | 0.4486 | | 0.6416 | 207.0 | 139518 | 0.8914 | 0.3792 | 0.7295 | 0.3545 | 0.2456 | 0.4348 | 0.526 | 0.1272 | 0.4187 | 0.528 | 0.382 | 0.5725 | 0.6948 | 0.2371 | 0.4843 | 0.5529 | 0.6423 | 0.3476 | 0.4574 | | 0.634 | 208.0 | 140192 | 0.9061 | 0.3661 | 0.7218 | 0.3271 | 0.2207 | 0.4298 | 0.5096 | 0.1269 | 0.408 | 0.5211 | 0.3669 | 0.5752 | 0.6809 | 0.2261 | 0.4814 | 0.5357 | 0.6304 | 0.3364 | 0.4516 | | 0.6335 | 209.0 | 140866 | 0.9002 | 0.3677 | 0.7281 | 0.3289 | 0.2329 | 0.4282 | 0.5194 | 0.1267 | 0.41 | 0.5199 | 0.3611 | 0.5716 | 0.6987 | 0.229 | 0.4828 | 0.5356 | 0.6281 | 0.3385 | 0.4489 | | 0.6302 | 210.0 | 141540 | 0.8955 | 0.3767 | 0.7324 | 0.3432 | 0.2384 | 0.4333 | 0.5291 | 0.1286 | 0.4173 | 0.5256 | 0.37 | 0.5764 | 0.6883 | 0.2367 | 0.4767 | 0.5399 | 0.634 | 0.3535 | 0.4661 | | 0.6199 | 211.0 | 142214 | 0.8979 | 0.3792 | 0.7353 | 0.3472 | 0.2463 | 0.4402 | 0.5101 | 0.1286 | 0.4187 | 0.5344 | 0.3821 | 0.587 | 0.6946 | 0.2419 | 0.4995 | 0.5421 | 0.6423 | 0.3536 | 0.4613 | | 0.6164 | 212.0 | 142888 | 0.9057 | 0.3743 | 0.7306 | 0.3464 | 0.2374 | 0.4365 | 0.5114 | 0.1249 | 0.4148 | 0.5255 | 0.3746 | 0.5769 | 0.6919 | 0.2353 | 0.4806 | 0.5378 | 0.6382 | 0.3499 | 0.4576 | | 0.6184 | 213.0 | 143562 | 0.8919 | 0.3785 | 0.7338 | 0.3531 | 0.2407 | 0.4394 | 0.5215 | 0.126 | 0.4164 | 0.5291 | 0.3779 | 0.5812 | 0.6906 | 0.2349 | 0.4909 | 0.5465 | 0.6356 | 0.354 | 0.4609 | | 0.6234 | 214.0 | 144236 | 0.8890 | 0.3816 | 0.7378 | 0.3565 | 0.2538 | 0.4348 | 0.5286 | 0.1284 | 0.4164 | 0.5276 | 0.3801 | 0.5707 | 0.6903 | 0.2303 | 0.4743 | 0.548 | 0.6379 | 0.3666 | 0.4705 | | 0.6244 | 215.0 | 144910 | 0.8916 | 0.3801 | 0.7309 | 0.3586 | 0.2515 | 0.4337 | 0.5222 | 0.1273 | 0.4161 | 0.5272 | 0.3785 | 0.5734 | 0.6925 | 0.2393 | 0.4847 | 0.5506 | 0.6377 | 0.3505 | 0.4592 | | 0.6212 | 216.0 | 145584 | 0.8984 | 0.3805 | 0.7294 | 0.3514 | 0.2475 | 0.4363 | 0.531 | 0.1294 | 0.4138 | 0.5258 | 0.3819 | 0.5675 | 0.6996 | 0.2388 | 0.4833 | 0.5439 | 0.631 | 0.359 | 0.463 | | 0.6106 | 217.0 | 146258 | 0.9122 | 0.3752 | 0.7228 | 0.3512 | 0.2492 | 0.4295 | 0.5183 | 0.1277 | 0.4091 | 0.5252 | 0.3776 | 0.566 | 0.6972 | 0.2253 | 0.472 | 0.5393 | 0.6381 | 0.3611 | 0.4656 | | 0.6127 | 218.0 | 146932 | 0.9043 | 0.3769 | 0.7289 | 0.3432 | 0.2501 | 0.4359 | 0.5198 | 0.1276 | 0.4136 | 0.5258 | 0.3742 | 0.5754 | 0.6971 | 0.2261 | 0.4739 | 0.55 | 0.6426 | 0.3546 | 0.461 | | 0.6144 | 219.0 | 147606 | 0.9077 | 0.3753 | 0.7305 | 0.353 | 0.2416 | 0.4368 | 0.5205 | 0.1239 | 0.4126 | 0.5235 | 0.3673 | 0.5754 | 0.6903 | 0.2299 | 0.4759 | 0.5387 | 0.6297 | 0.3573 | 0.4649 | | 0.6064 | 220.0 | 148280 | 0.9145 | 0.374 | 0.724 | 0.3392 | 0.2351 | 0.433 | 0.5212 | 0.1272 | 0.4096 | 0.522 | 0.3685 | 0.5661 | 0.6911 | 0.2267 | 0.474 | 0.5437 | 0.632 | 0.3516 | 0.4599 | | 0.6065 | 221.0 | 148954 | 0.9038 | 0.3782 | 0.7305 | 0.3471 | 0.2464 | 0.4333 | 0.5142 | 0.1262 | 0.4136 | 0.5246 | 0.3772 | 0.5685 | 0.6769 | 0.2269 | 0.4705 | 0.5441 | 0.6359 | 0.3638 | 0.4673 | | 0.605 | 222.0 | 149628 | 0.8916 | 0.3832 | 0.7358 | 0.3534 | 0.2518 | 0.4397 | 0.5256 | 0.1281 | 0.4174 | 0.5328 | 0.3852 | 0.5803 | 0.6891 | 0.2352 | 0.4867 | 0.5463 | 0.6404 | 0.368 | 0.4712 | | 0.5999 | 223.0 | 150302 | 0.9010 | 0.3852 | 0.734 | 0.3557 | 0.2555 | 0.4468 | 0.5187 | 0.1294 | 0.4188 | 0.5302 | 0.3862 | 0.5785 | 0.6829 | 0.2343 | 0.4771 | 0.5534 | 0.6456 | 0.3678 | 0.468 | | 0.6048 | 224.0 | 150976 | 0.8917 | 0.3858 | 0.7325 | 0.3647 | 0.2535 | 0.4433 | 0.5283 | 0.1274 | 0.4161 | 0.529 | 0.3763 | 0.5772 | 0.6994 | 0.231 | 0.4721 | 0.5566 | 0.6443 | 0.3699 | 0.4706 | | 0.5953 | 225.0 | 151650 | 0.9025 | 0.3826 | 0.7318 | 0.3538 | 0.2447 | 0.4434 | 0.5207 | 0.1265 | 0.4142 | 0.5255 | 0.3736 | 0.5753 | 0.684 | 0.2342 | 0.4759 | 0.5501 | 0.6337 | 0.3635 | 0.4671 | | 0.6101 | 226.0 | 152324 | 0.9039 | 0.3783 | 0.7278 | 0.3559 | 0.2389 | 0.441 | 0.5256 | 0.1265 | 0.4129 | 0.5224 | 0.3613 | 0.5761 | 0.6851 | 0.2272 | 0.4715 | 0.5495 | 0.6395 | 0.3581 | 0.4563 | | 0.593 | 227.0 | 152998 | 0.9035 | 0.3825 | 0.7304 | 0.3535 | 0.2473 | 0.4413 | 0.5247 | 0.1281 | 0.4172 | 0.528 | 0.3757 | 0.5769 | 0.692 | 0.2391 | 0.4802 | 0.5464 | 0.6382 | 0.362 | 0.4656 | | 0.5995 | 228.0 | 153672 | 0.9083 | 0.3794 | 0.7297 | 0.3441 | 0.2496 | 0.4342 | 0.5178 | 0.1264 | 0.4142 | 0.5297 | 0.3819 | 0.5773 | 0.697 | 0.2295 | 0.4837 | 0.5461 | 0.6358 | 0.3627 | 0.4697 | | 0.6016 | 229.0 | 154346 | 0.9108 | 0.3741 | 0.7277 | 0.3384 | 0.2429 | 0.4316 | 0.5104 | 0.1268 | 0.4109 | 0.5229 | 0.3762 | 0.5719 | 0.6881 | 0.2325 | 0.4785 | 0.5422 | 0.6325 | 0.3476 | 0.4576 | | 0.6008 | 230.0 | 155020 | 0.8996 | 0.3818 | 0.736 | 0.3452 | 0.2521 | 0.4341 | 0.517 | 0.1302 | 0.417 | 0.531 | 0.3928 | 0.5695 | 0.6946 | 0.2347 | 0.4781 | 0.5473 | 0.6371 | 0.3634 | 0.4779 | | 0.6038 | 231.0 | 155694 | 0.9017 | 0.3782 | 0.7279 | 0.3403 | 0.252 | 0.4353 | 0.5178 | 0.1282 | 0.4137 | 0.527 | 0.3819 | 0.574 | 0.6874 | 0.2281 | 0.4774 | 0.5509 | 0.6412 | 0.3558 | 0.4623 | | 0.5983 | 232.0 | 156368 | 0.9024 | 0.3801 | 0.7262 | 0.3458 | 0.251 | 0.436 | 0.5109 | 0.1268 | 0.4129 | 0.5299 | 0.3872 | 0.5748 | 0.6814 | 0.2292 | 0.475 | 0.5488 | 0.6404 | 0.3624 | 0.4742 | | 0.5905 | 233.0 | 157042 | 0.9003 | 0.3828 | 0.7349 | 0.3451 | 0.2498 | 0.4389 | 0.5156 | 0.1264 | 0.4143 | 0.5283 | 0.3861 | 0.572 | 0.6823 | 0.2307 | 0.467 | 0.5504 | 0.6399 | 0.3672 | 0.478 | | 0.5894 | 234.0 | 157716 | 0.9006 | 0.3848 | 0.7332 | 0.3529 | 0.2483 | 0.4449 | 0.515 | 0.1285 | 0.4164 | 0.5325 | 0.3851 | 0.5786 | 0.6782 | 0.2381 | 0.4815 | 0.5471 | 0.6387 | 0.3692 | 0.4773 | | 0.5891 | 235.0 | 158390 | 0.9039 | 0.3845 | 0.7311 | 0.3574 | 0.2565 | 0.4386 | 0.5118 | 0.127 | 0.4189 | 0.5309 | 0.3932 | 0.5725 | 0.6872 | 0.2312 | 0.4731 | 0.5522 | 0.6446 | 0.37 | 0.4751 | | 0.5845 | 236.0 | 159064 | 0.9055 | 0.3826 | 0.7323 | 0.351 | 0.2473 | 0.4424 | 0.5204 | 0.1241 | 0.4151 | 0.5313 | 0.3756 | 0.5821 | 0.6938 | 0.2331 | 0.4807 | 0.5502 | 0.6392 | 0.3646 | 0.474 | | 0.5811 | 237.0 | 159738 | 0.9109 | 0.3768 | 0.7261 | 0.3539 | 0.2417 | 0.4402 | 0.5118 | 0.1262 | 0.411 | 0.5232 | 0.3657 | 0.5761 | 0.6883 | 0.2244 | 0.4677 | 0.5466 | 0.6335 | 0.3595 | 0.4684 | | 0.5785 | 238.0 | 160412 | 0.9032 | 0.3791 | 0.7253 | 0.3562 | 0.2531 | 0.4382 | 0.5054 | 0.1263 | 0.4121 | 0.5284 | 0.393 | 0.5749 | 0.6746 | 0.2231 | 0.4726 | 0.5486 | 0.6374 | 0.3656 | 0.4753 | | 0.5866 | 239.0 | 161086 | 0.9085 | 0.3743 | 0.7325 | 0.3389 | 0.2456 | 0.4301 | 0.5144 | 0.1268 | 0.4135 | 0.522 | 0.3788 | 0.5718 | 0.6829 | 0.2286 | 0.4706 | 0.5401 | 0.6302 | 0.3543 | 0.4652 | | 0.5908 | 240.0 | 161760 | 0.9156 | 0.3757 | 0.7338 | 0.3459 | 0.2458 | 0.432 | 0.5066 | 0.1263 | 0.4156 | 0.5235 | 0.3777 | 0.5742 | 0.6814 | 0.229 | 0.4719 | 0.5447 | 0.6348 | 0.3534 | 0.4637 | | 0.5843 | 241.0 | 162434 | 0.8990 | 0.38 | 0.7358 | 0.3539 | 0.2485 | 0.4353 | 0.5192 | 0.1276 | 0.4188 | 0.5282 | 0.3842 | 0.574 | 0.6908 | 0.2277 | 0.4747 | 0.5488 | 0.6356 | 0.3636 | 0.4743 | | 0.5841 | 242.0 | 163108 | 0.9133 | 0.3761 | 0.7327 | 0.3518 | 0.2414 | 0.4367 | 0.5116 | 0.125 | 0.4145 | 0.5197 | 0.3697 | 0.5767 | 0.6742 | 0.2283 | 0.4676 | 0.5466 | 0.6366 | 0.3535 | 0.455 | | 0.5883 | 243.0 | 163782 | 0.9081 | 0.3795 | 0.73 | 0.356 | 0.2472 | 0.4382 | 0.5136 | 0.1274 | 0.4129 | 0.5223 | 0.3721 | 0.5705 | 0.6814 | 0.2296 | 0.4674 | 0.554 | 0.6404 | 0.3548 | 0.4591 | | 0.5741 | 244.0 | 164456 | 0.9041 | 0.3845 | 0.7383 | 0.3598 | 0.2517 | 0.4437 | 0.5126 | 0.1285 | 0.4208 | 0.5279 | 0.3797 | 0.578 | 0.6758 | 0.2331 | 0.4744 | 0.5538 | 0.6394 | 0.3665 | 0.47 | | 0.5839 | 245.0 | 165130 | 0.9027 | 0.382 | 0.7323 | 0.3539 | 0.2607 | 0.4403 | 0.5048 | 0.1261 | 0.4175 | 0.5302 | 0.3897 | 0.5764 | 0.674 | 0.2281 | 0.4743 | 0.5503 | 0.6418 | 0.3677 | 0.4746 | | 0.5669 | 246.0 | 165804 | 0.9144 | 0.3816 | 0.7337 | 0.3596 | 0.2587 | 0.4368 | 0.5101 | 0.1277 | 0.4128 | 0.5238 | 0.3818 | 0.5669 | 0.68 | 0.2283 | 0.463 | 0.5459 | 0.6324 | 0.3706 | 0.476 | | 0.5771 | 247.0 | 166478 | 0.9048 | 0.3818 | 0.7276 | 0.3603 | 0.2577 | 0.4396 | 0.5212 | 0.1264 | 0.4165 | 0.5268 | 0.3825 | 0.574 | 0.6893 | 0.2287 | 0.4717 | 0.553 | 0.6425 | 0.3636 | 0.4662 | | 0.5851 | 248.0 | 167152 | 0.9139 | 0.3793 | 0.7331 | 0.3533 | 0.2543 | 0.4351 | 0.5104 | 0.1269 | 0.415 | 0.5264 | 0.382 | 0.5715 | 0.6781 | 0.2268 | 0.4681 | 0.5443 | 0.6376 | 0.3669 | 0.4735 | | 0.5684 | 249.0 | 167826 | 0.8998 | 0.3822 | 0.7374 | 0.3526 | 0.2568 | 0.4417 | 0.5165 | 0.1283 | 0.4177 | 0.5276 | 0.3771 | 0.5759 | 0.6864 | 0.2305 | 0.4711 | 0.5516 | 0.6402 | 0.3644 | 0.4714 | | 0.564 | 250.0 | 168500 | 0.9078 | 0.3795 | 0.7386 | 0.3467 | 0.2538 | 0.4363 | 0.5131 | 0.1261 | 0.4178 | 0.5264 | 0.3853 | 0.5691 | 0.6765 | 0.2281 | 0.4708 | 0.5432 | 0.6379 | 0.3671 | 0.4705 | | 0.5688 | 251.0 | 169174 | 0.9198 | 0.3779 | 0.734 | 0.3454 | 0.2513 | 0.4354 | 0.5152 | 0.1269 | 0.4178 | 0.5282 | 0.3862 | 0.5744 | 0.6821 | 0.2283 | 0.473 | 0.5443 | 0.6428 | 0.3612 | 0.4687 | | 0.5773 | 252.0 | 169848 | 0.9195 | 0.3769 | 0.7389 | 0.3388 | 0.248 | 0.4372 | 0.5128 | 0.1258 | 0.4159 | 0.5205 | 0.3782 | 0.5665 | 0.6794 | 0.2311 | 0.4646 | 0.5473 | 0.6425 | 0.3524 | 0.4543 | | 0.5603 | 253.0 | 170522 | 0.9128 | 0.3841 | 0.7364 | 0.353 | 0.2535 | 0.4425 | 0.5187 | 0.1278 | 0.4199 | 0.5279 | 0.3786 | 0.5801 | 0.6824 | 0.2353 | 0.4714 | 0.55 | 0.6407 | 0.3671 | 0.4717 | | 0.5569 | 254.0 | 171196 | 0.9131 | 0.3806 | 0.734 | 0.3413 | 0.2513 | 0.4402 | 0.5118 | 0.1266 | 0.4143 | 0.5273 | 0.3837 | 0.5719 | 0.685 | 0.2292 | 0.4679 | 0.5454 | 0.6381 | 0.3671 | 0.4759 | | 0.5582 | 255.0 | 171870 | 0.9064 | 0.3798 | 0.7373 | 0.3469 | 0.2539 | 0.4373 | 0.5106 | 0.1269 | 0.4175 | 0.527 | 0.386 | 0.5725 | 0.6794 | 0.2277 | 0.4681 | 0.5465 | 0.6395 | 0.3654 | 0.4734 | | 0.5619 | 256.0 | 172544 | 0.9147 | 0.3749 | 0.7309 | 0.3352 | 0.2434 | 0.4374 | 0.5114 | 0.1263 | 0.4134 | 0.5233 | 0.3721 | 0.5777 | 0.6883 | 0.2268 | 0.473 | 0.5444 | 0.6377 | 0.3534 | 0.4592 | | 0.5491 | 257.0 | 173218 | 0.9117 | 0.3787 | 0.7377 | 0.3439 | 0.2519 | 0.4378 | 0.5095 | 0.1261 | 0.4145 | 0.5237 | 0.3818 | 0.5709 | 0.6749 | 0.2289 | 0.4665 | 0.5411 | 0.6342 | 0.3662 | 0.4703 | | 0.5609 | 258.0 | 173892 | 0.9110 | 0.38 | 0.7362 | 0.3517 | 0.2572 | 0.4346 | 0.5069 | 0.1272 | 0.4178 | 0.5287 | 0.3893 | 0.5737 | 0.6796 | 0.2239 | 0.4717 | 0.5476 | 0.6373 | 0.3683 | 0.4772 | | 0.5608 | 259.0 | 174566 | 0.9192 | 0.3803 | 0.733 | 0.3532 | 0.2543 | 0.438 | 0.5147 | 0.1282 | 0.4131 | 0.5252 | 0.3841 | 0.5746 | 0.6789 | 0.2284 | 0.4698 | 0.5473 | 0.6356 | 0.3653 | 0.4703 | | 0.5488 | 260.0 | 175240 | 0.9231 | 0.3787 | 0.7325 | 0.3526 | 0.2544 | 0.4336 | 0.5085 | 0.1259 | 0.4142 | 0.5245 | 0.3774 | 0.5752 | 0.676 | 0.2259 | 0.4662 | 0.5464 | 0.6373 | 0.3638 | 0.47 | | 0.5537 | 261.0 | 175914 | 0.9194 | 0.3812 | 0.7324 | 0.3557 | 0.2541 | 0.4402 | 0.5081 | 0.1279 | 0.4148 | 0.5255 | 0.3795 | 0.5774 | 0.675 | 0.2248 | 0.467 | 0.5531 | 0.6377 | 0.3657 | 0.4718 | | 0.5564 | 262.0 | 176588 | 0.9197 | 0.3788 | 0.7362 | 0.3527 | 0.2536 | 0.4364 | 0.4998 | 0.1258 | 0.4153 | 0.5257 | 0.3871 | 0.5697 | 0.6747 | 0.2242 | 0.4677 | 0.5509 | 0.6394 | 0.3614 | 0.47 | | 0.5532 | 263.0 | 177262 | 0.9117 | 0.3835 | 0.7387 | 0.3575 | 0.2585 | 0.44 | 0.5066 | 0.1292 | 0.4155 | 0.5284 | 0.3912 | 0.5752 | 0.6743 | 0.227 | 0.466 | 0.5517 | 0.642 | 0.3717 | 0.4771 | | 0.5501 | 264.0 | 177936 | 0.9117 | 0.3831 | 0.737 | 0.3573 | 0.2586 | 0.4375 | 0.5138 | 0.1275 | 0.416 | 0.5328 | 0.3923 | 0.5765 | 0.6873 | 0.2265 | 0.4731 | 0.553 | 0.6469 | 0.3697 | 0.4784 | | 0.5519 | 265.0 | 178610 | 0.9051 | 0.3855 | 0.7399 | 0.3554 | 0.2626 | 0.4415 | 0.5101 | 0.1275 | 0.4201 | 0.5323 | 0.3928 | 0.5774 | 0.6804 | 0.229 | 0.4731 | 0.5537 | 0.6436 | 0.3738 | 0.4801 | | 0.5478 | 266.0 | 179284 | 0.9123 | 0.3821 | 0.7381 | 0.3549 | 0.262 | 0.4349 | 0.5107 | 0.1266 | 0.4163 | 0.5281 | 0.389 | 0.573 | 0.6788 | 0.2271 | 0.4681 | 0.5497 | 0.6431 | 0.3693 | 0.4732 | | 0.5422 | 267.0 | 179958 | 0.9016 | 0.3846 | 0.7381 | 0.3615 | 0.2598 | 0.4407 | 0.5119 | 0.1274 | 0.4176 | 0.5319 | 0.3914 | 0.5759 | 0.6841 | 0.2309 | 0.4766 | 0.5538 | 0.6458 | 0.369 | 0.4735 | | 0.5456 | 268.0 | 180632 | 0.9068 | 0.3805 | 0.7432 | 0.3565 | 0.255 | 0.4402 | 0.5109 | 0.1249 | 0.4153 | 0.5283 | 0.3857 | 0.5772 | 0.6814 | 0.228 | 0.4686 | 0.5493 | 0.6435 | 0.3641 | 0.4728 | | 0.5437 | 269.0 | 181306 | 0.9086 | 0.3857 | 0.7369 | 0.3653 | 0.2638 | 0.4417 | 0.5143 | 0.127 | 0.4208 | 0.5346 | 0.399 | 0.5765 | 0.6837 | 0.2299 | 0.4749 | 0.555 | 0.6469 | 0.3722 | 0.4819 | | 0.5434 | 270.0 | 181980 | 0.9111 | 0.3857 | 0.7317 | 0.3581 | 0.2621 | 0.4418 | 0.514 | 0.1275 | 0.4184 | 0.533 | 0.3985 | 0.5762 | 0.6832 | 0.2273 | 0.4755 | 0.5591 | 0.6461 | 0.3707 | 0.4773 | | 0.5399 | 271.0 | 182654 | 0.9117 | 0.3852 | 0.7334 | 0.3725 | 0.2581 | 0.4434 | 0.5179 | 0.1261 | 0.4158 | 0.5297 | 0.3901 | 0.5793 | 0.6801 | 0.2278 | 0.4728 | 0.5549 | 0.6444 | 0.3729 | 0.4718 | | 0.541 | 272.0 | 183328 | 0.9128 | 0.3848 | 0.7407 | 0.3626 | 0.2629 | 0.4378 | 0.5165 | 0.1264 | 0.4152 | 0.529 | 0.3942 | 0.5711 | 0.6816 | 0.2277 | 0.4705 | 0.5499 | 0.6361 | 0.3769 | 0.4805 | | 0.541 | 273.0 | 184002 | 0.9104 | 0.3815 | 0.738 | 0.3528 | 0.2531 | 0.4414 | 0.515 | 0.1252 | 0.4136 | 0.5265 | 0.3873 | 0.5768 | 0.6756 | 0.2264 | 0.4657 | 0.5515 | 0.6446 | 0.3667 | 0.4693 | | 0.5458 | 274.0 | 184676 | 0.9146 | 0.3815 | 0.7338 | 0.3556 | 0.2569 | 0.4376 | 0.5106 | 0.1261 | 0.4162 | 0.5274 | 0.3958 | 0.572 | 0.6762 | 0.2288 | 0.47 | 0.5431 | 0.6381 | 0.3725 | 0.4741 | | 0.5398 | 275.0 | 185350 | 0.9026 | 0.384 | 0.7397 | 0.3512 | 0.2604 | 0.4374 | 0.5195 | 0.1272 | 0.4169 | 0.5298 | 0.3935 | 0.5711 | 0.6844 | 0.2327 | 0.473 | 0.5473 | 0.6399 | 0.372 | 0.4765 | | 0.5339 | 276.0 | 186024 | 0.9063 | 0.3833 | 0.738 | 0.3634 | 0.2556 | 0.4397 | 0.5105 | 0.1267 | 0.4158 | 0.5305 | 0.3922 | 0.574 | 0.6797 | 0.2265 | 0.4716 | 0.5486 | 0.6404 | 0.3748 | 0.4795 | | 0.5282 | 277.0 | 186698 | 0.9073 | 0.386 | 0.7391 | 0.3663 | 0.2609 | 0.4413 | 0.5116 | 0.1275 | 0.4172 | 0.5305 | 0.3942 | 0.5715 | 0.6795 | 0.2294 | 0.4692 | 0.554 | 0.6446 | 0.3746 | 0.4776 | | 0.5423 | 278.0 | 187372 | 0.9045 | 0.3867 | 0.7398 | 0.36 | 0.2636 | 0.4412 | 0.5124 | 0.127 | 0.4183 | 0.5323 | 0.3981 | 0.571 | 0.6795 | 0.2298 | 0.471 | 0.5531 | 0.644 | 0.3771 | 0.4819 | | 0.5296 | 279.0 | 188046 | 0.9049 | 0.3867 | 0.7468 | 0.3621 | 0.264 | 0.4407 | 0.5167 | 0.1266 | 0.4196 | 0.5333 | 0.3981 | 0.5736 | 0.6798 | 0.2331 | 0.4748 | 0.5506 | 0.6436 | 0.3763 | 0.4816 | | 0.5339 | 280.0 | 188720 | 0.9117 | 0.3857 | 0.7354 | 0.3608 | 0.2623 | 0.4415 | 0.5133 | 0.1276 | 0.4172 | 0.5311 | 0.3953 | 0.5725 | 0.6816 | 0.2287 | 0.4731 | 0.5517 | 0.6425 | 0.3766 | 0.4778 | | 0.5259 | 281.0 | 189394 | 0.9092 | 0.3833 | 0.7353 | 0.36 | 0.2564 | 0.4394 | 0.5153 | 0.1272 | 0.4161 | 0.5277 | 0.3877 | 0.5715 | 0.6781 | 0.2285 | 0.4688 | 0.5474 | 0.6382 | 0.3738 | 0.4759 | | 0.5275 | 282.0 | 190068 | 0.9104 | 0.3845 | 0.741 | 0.3689 | 0.2601 | 0.438 | 0.5141 | 0.1264 | 0.4168 | 0.5289 | 0.3933 | 0.5723 | 0.6767 | 0.2284 | 0.4676 | 0.5509 | 0.6425 | 0.3743 | 0.4766 | | 0.5268 | 283.0 | 190742 | 0.9081 | 0.3859 | 0.738 | 0.3678 | 0.2598 | 0.4444 | 0.5146 | 0.1266 | 0.4185 | 0.5302 | 0.3935 | 0.5754 | 0.6768 | 0.2289 | 0.472 | 0.5544 | 0.6441 | 0.3745 | 0.4745 | | 0.5237 | 284.0 | 191416 | 0.9125 | 0.3861 | 0.7325 | 0.3738 | 0.259 | 0.4446 | 0.5095 | 0.1272 | 0.417 | 0.5293 | 0.3919 | 0.5744 | 0.6754 | 0.2273 | 0.4677 | 0.5563 | 0.6438 | 0.3747 | 0.4764 | | 0.5253 | 285.0 | 192090 | 0.9077 | 0.3883 | 0.7366 | 0.3703 | 0.2622 | 0.4486 | 0.5082 | 0.127 | 0.4204 | 0.5323 | 0.3971 | 0.5762 | 0.6777 | 0.2318 | 0.473 | 0.5541 | 0.642 | 0.3791 | 0.482 | | 0.5268 | 286.0 | 192764 | 0.9117 | 0.3853 | 0.7372 | 0.3653 | 0.2577 | 0.4442 | 0.5156 | 0.1267 | 0.4189 | 0.528 | 0.3883 | 0.5715 | 0.6773 | 0.2298 | 0.4688 | 0.5514 | 0.6405 | 0.3747 | 0.4747 | | 0.5262 | 287.0 | 193438 | 0.9098 | 0.3876 | 0.74 | 0.3726 | 0.2628 | 0.4443 | 0.5129 | 0.1278 | 0.4219 | 0.5313 | 0.3981 | 0.5755 | 0.6766 | 0.2315 | 0.4691 | 0.5544 | 0.6448 | 0.3768 | 0.48 | | 0.5134 | 288.0 | 194112 | 0.9101 | 0.3876 | 0.738 | 0.3749 | 0.2624 | 0.4451 | 0.5097 | 0.1285 | 0.4203 | 0.5314 | 0.3971 | 0.5749 | 0.6759 | 0.2324 | 0.4716 | 0.5557 | 0.6462 | 0.3748 | 0.4763 | | 0.517 | 289.0 | 194786 | 0.9097 | 0.3868 | 0.7358 | 0.3683 | 0.259 | 0.4456 | 0.5121 | 0.1279 | 0.4201 | 0.5292 | 0.391 | 0.5737 | 0.6775 | 0.2303 | 0.4681 | 0.5537 | 0.6426 | 0.3764 | 0.477 | | 0.5329 | 290.0 | 195460 | 0.9063 | 0.3879 | 0.7427 | 0.3717 | 0.2626 | 0.4451 | 0.511 | 0.1285 | 0.4203 | 0.5308 | 0.396 | 0.5746 | 0.6749 | 0.2358 | 0.4705 | 0.5524 | 0.6422 | 0.3754 | 0.4798 | | 0.5242 | 291.0 | 196134 | 0.9133 | 0.3847 | 0.737 | 0.3624 | 0.2587 | 0.4437 | 0.5141 | 0.1263 | 0.4171 | 0.5293 | 0.3912 | 0.5744 | 0.6791 | 0.234 | 0.4726 | 0.5506 | 0.6426 | 0.3693 | 0.4726 | | 0.5197 | 292.0 | 196808 | 0.9090 | 0.3874 | 0.7402 | 0.37 | 0.2623 | 0.4446 | 0.5092 | 0.1279 | 0.4195 | 0.5322 | 0.3957 | 0.574 | 0.6805 | 0.2318 | 0.4712 | 0.5519 | 0.6436 | 0.3784 | 0.4817 | | 0.5233 | 293.0 | 197482 | 0.9104 | 0.3868 | 0.7368 | 0.3642 | 0.2606 | 0.4459 | 0.5108 | 0.1283 | 0.4192 | 0.5304 | 0.3957 | 0.5748 | 0.6757 | 0.2343 | 0.4736 | 0.5525 | 0.641 | 0.3735 | 0.4765 | | 0.5232 | 294.0 | 198156 | 0.9099 | 0.3854 | 0.7391 | 0.3618 | 0.2593 | 0.4432 | 0.5122 | 0.1274 | 0.4176 | 0.5297 | 0.3921 | 0.5732 | 0.6796 | 0.2334 | 0.4726 | 0.5501 | 0.6407 | 0.3728 | 0.4759 | | 0.5165 | 295.0 | 198830 | 0.9107 | 0.3861 | 0.7392 | 0.3663 | 0.2596 | 0.4431 | 0.5111 | 0.1277 | 0.4185 | 0.531 | 0.3934 | 0.574 | 0.6788 | 0.2312 | 0.4702 | 0.5519 | 0.6418 | 0.3753 | 0.4809 | | 0.5185 | 296.0 | 199504 | 0.9077 | 0.3881 | 0.7402 | 0.3758 | 0.2655 | 0.4413 | 0.5179 | 0.1282 | 0.4188 | 0.5318 | 0.3961 | 0.5731 | 0.6811 | 0.2326 | 0.4707 | 0.5531 | 0.6433 | 0.3785 | 0.4814 | | 0.5174 | 297.0 | 200178 | 0.9074 | 0.3878 | 0.7416 | 0.3717 | 0.2635 | 0.4442 | 0.5143 | 0.129 | 0.4187 | 0.5309 | 0.3949 | 0.5737 | 0.6793 | 0.2335 | 0.4702 | 0.552 | 0.6413 | 0.3779 | 0.4813 | | 0.5228 | 298.0 | 200852 | 0.9071 | 0.3875 | 0.7411 | 0.3668 | 0.2637 | 0.4431 | 0.5118 | 0.1283 | 0.4186 | 0.5321 | 0.398 | 0.5731 | 0.6765 | 0.2328 | 0.4717 | 0.5523 | 0.6425 | 0.3775 | 0.4821 | | 0.5159 | 299.0 | 201526 | 0.9071 | 0.3879 | 0.7416 | 0.3684 | 0.2634 | 0.4435 | 0.5135 | 0.1286 | 0.419 | 0.5322 | 0.3969 | 0.5741 | 0.6773 | 0.2325 | 0.4706 | 0.553 | 0.6415 | 0.3782 | 0.4844 | | 0.5213 | 300.0 | 202200 | 0.9075 | 0.3883 | 0.7422 | 0.3707 | 0.2639 | 0.4432 | 0.5149 | 0.1286 | 0.419 | 0.5325 | 0.3975 | 0.5729 | 0.68 | 0.2322 | 0.4708 | 0.5547 | 0.6431 | 0.3779 | 0.4834 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.20.1

[ "fire", "vehicle", "human" ]

hanskantoss/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.6645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.5801 | 0.4 | 50 | 7.2791 | | 3.004 | 0.8 | 100 | 5.6645 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

[ "accessories", "bags", "clothing", "shoes" ]

kike/table_structured_recognition_fito

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]

julianlec/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9542 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9905 | 1.0 | 5000 | 0.9542 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

SkowKyubu/detr-resnet-50_finetuned_cppe5

# detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.2

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

Max-Ploter/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.7762 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6869 | 0.16 | 100 | 5.9745 | | 2.3703 | 0.32 | 200 | 6.5070 | | 2.5309 | 0.48 | 300 | 7.8489 | | 2.3781 | 0.64 | 400 | 6.9163 | | 2.2011 | 0.8 | 500 | 5.8360 | | 1.9613 | 0.96 | 600 | 5.7762 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

AnnabelKaasik/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.1583 | 0.8 | 1000 | 1.0589 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

Khanmhmdi/detr-resnet-50_finetuned_fss1000

# detr-resnet-50_finetuned_fss1000 This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "trench_coat", "starfish", "nintendo_3ds", "maotai_bottle", "vending_machine", "black_grouse", "nintendo_sp", "yoga_pad", "chicken", "drumstick", "cream", "handcuff", "pistachio", "cristo_redentor", "water_ouzel", "olive", "sealion", "mite_predator", "croquet_ball", "television", "perfume", "indian_cobra", "thrush", "sparrow", "pool_table", "rock_snake", "sewing_machine", "curlew", "tank", "howler_monkey", "mouthpiece", "tape_player", "cougar", "mcdonald_sign", "dowitcher", "diver", "capuchin", "roller_coaster", "bradypod", "streetcar", "parthenon", "cradle", "chess_bishop", "stove", "magpie_bird", "warplane", "frying_pan", "rabbit", "kitchen_knife", "beam_bridge", "blenheim_spaniel", "cleaver", "wandering_albatross", "shih-tzu", "pickelhaube", "medical_kit", "bus", "cushion", "polo_shirt", "sundial", "sandwich_cookies", "transport_helicopter", "strawberry", "hami_melon", "excavator", "besom", "handshower", "mount_fuji", "toothpaste", "cosmetic_brush", "fish", "shower_cap", "egg_tart", "raven", "mango", "trolleybus", "otter", "brush_pen", "mouse", "jellyfish", "yawl", "cigarette", "pufferfish", "truss_bridge", "dingo", "australian_terrier", "leafhopper", "whale", "beet_root", "saltshaker", "carriage", "chalk_brush", "grey_whale", "hammerhead_shark", "shakuhachi", "pinecone", "rhinoceros", "barber_shaver", "face_powder", "sombrero", "soap", "afghan_hound", "hatchet", "rally_car", "moist_proof_pad", "prairie_chicken", "motor_scooter", "monkey", "garlic", "guinea_pig", "spark_plug", "tofu", "macaque", "flying_snakes", "pingpong_ball", "pyraminx", "broom", "jacko_lantern", "fire_engine", "proboscis", "macaw", "folding_chair", "ladle", "baseball", "golf_ball", "ocarina", "snowmobile", "tiger_shark", "airliner", "loguat", "groenendael", "knife", "net_surface_shoes", "bucket", "kappa_logo", "assult_rifle", "siamang", "rocking_chair", "iguana", "dragonfly", "vacuum_cup", "head_cabbage", "potted_plant", "spiderman", "submarine", "sea_cucumber", "bat", "spider_monkey", "fox_squirrel", "cactus_ball", "impala", "windsor_tie", "crash_helmet", "vestment", "gyromitra", "donkey", "balloon", "african_elephant", "giant_schnauzer", "mitten", "bra", "tow_truck", "pepitas", "drum", "kazoo", "ski_mask", "african_crocodile", "washer", "andean_condor", "peregine_falcon", "miniskirt", "gorilla", "harmonica", "cheese", "ashtray", "brick_card", "sungnyemun", "chainsaw", "seal", "skull", "kwanyin", "snowplow", "tulip", "refrigerator", "soap_dispenser", "cheetah", "brick_tea", "pteropus", "hartebeest", "gypsy_moth", "tiger_cat", "apple", "apron", "wallaby", "lion", "bee_house", "electronic_toothbrush", "table_lamp", "stool", "shower_curtain", "boston_bull", "petri_dish", "marmot", "typewriter", "mashed_potato", "vulture", "air_strip", "sweatshirt", "nintendo_switch", "warehouse_tray", "wooden_boat", "chimpanzee", "cantilever_bridge", "thatch", "polecat", "electric_fan", "combination_lock", "cactus", "spade", "forklift", "stole", "scarerow", "coyote", "skua", "carbonara", "ladyfinger", "cheese_burger", "cabbage", "fennel_bulb", "thimble", "ceiling_fan", "bullet_train", "carrot", "sports_car", "water_bike", "conversion_plug", "chinese_knot", "hover_board", "mountain_tent", "bouzouki", "gecko", "cornet", "pencil_sharpener1", "bloodhound", "chicken_leg", "battery", "turnstile", "schooner", "neck_brace", "briard", "goldfish", "woodpecker", "brambling", "helicopter", "victor_icon", "swimming_trunk", "speaker", "artichoke", "swim_ring", "quail_egg", "chinese_date", "dinosaur", "egret", "stretcher", "monocycle", "cucumber", "baboon", "shumai", "narcissus", "ocicat", "wine_bottle", "patas", "onion", "cabbage_butterfly", "pspgo", "water_buffalo", "kobe_logo", "hammer", "canoe", "eft_newt", "snowball", "lycaenid_butterfly", "dishwasher", "doughnut", "bell", "christmas_stocking", "church", "sulphur_butterfly", "housefinch", "bald_eagle", "white_shark", "soymilk_machine", "shopping_cart", "giant_panda", "lipstick", "snake", "tokyo_tower", "waffle_iron", "globe", "wheelchair", "wardrobe", "yorkshire_terrier", "coffeepot", "mooli", "walnut", "pidan", "raccoon", "french_ball", "carousel", "cpu", "cairn", "rose", "seagull", "panda", "windmill", "sandwich", "bomb", "wafer", "hyena", "swab", "diaper", "har_gow", "wash_basin", "beaker", "chopsticks", "lady_slipper", "leggings", "brain_coral", "earplug", "car_mirror", "okra", "pay_phone", "seatbelt", "fire_screen", "bell_pepper", "mooncake", "eletrical_switch", "fork", "one-armed_bandit", "statue_liberty", "guitar", "ladybug", "hare", "wig", "arrow", "speedboat", "owl", "lacewing", "laptop", "lampshade", "fire_hydrant", "turtle", "cd", "siamese_cat", "bracelet", "spatula", "fish_eagle", "pokermon_ball", "twin_tower", "border_terrier", "mario", "barbell", "ptarmigan", "ruler", "lifeboat", "adidas_logo1", "pineapple", "esport_chair", "pill_bottle", "banana_boat", "night_snake", "brasscica", "bamboo_slip", "glider_flyingfish", "wooden_spoon", "park_bench", "dhole", "acorn", "meatloaf", "joystick", "iron_man", "phonograph", "projector", "ferret", "crocodile", "nike_logo", "banded_gecko", "violin", "digital_watch", "birdhouse", "black_stork", "wok", "crt_screen", "indri", "convertible", "potato_chips", "spider", "spotted_salamander", "gas_tank", "ostrich", "microwave", "gym_ball", "ruffed_grouse", "swimming_glasses", "ballpoint", "buckingham_palace", "cumquat", "eagle", "fan", "villa_savoye", "tebby_cat", "leeks", "muscle_car", "brown_bear", "garfish", "three-toed_sloth", "chihuahua", "psp", "frog", "fire_balloon", "red_fox", "dutch_oven", "tile_roof", "earphone1", "captain_america_shield", "coho", "scabbard", "haddock", "broccoli", "razor", "bulbul_bird", "shovel", "pen", "persian_cat", "trimaran", "indian_elephant", "zucchini", "microscope", "beer_glass", "bolete", "armour", "sock", "hotel_slipper", "rosehip", "coin", "minicooper", "polar_bear", "corn", "drake", "nail_scissor", "crane", "jay_bird", "fox", "electronic_stove", "marshmallow", "white_wolf", "doublebus", "spinach", "bedlington_terrier", "paint_brush", "tomb", "adidas_logo2", "mud_turtle", "ox", "chess_king", "beaver", "peanut", "harp", "downy_pitch", "parallel_bars", "loafer", "cardoon", "camel", "teddy", "vase", "golden_plover", "measuring_cup", "waffle", "obelisk", "airedale", "plate", "space_heater", "aubergine", "comb", "revolver", "tray", "warthog", "leaf_egg", "feather_clothes", "jackfruit", "coconut", "almond", "timber_wolf", "water_snake", "lorikeet", "stinkhorn", "american_staffordshire", "water_heater", "grey_fox", "orang", "bamboo_dragonfly", "crab", "german_pointer", "cn_tower", "adhensive_tape", "african_grey", "pubg_airdrop", "reflex_camera", "ruddy_turnstone", "bee", "punching_bag", "necklace", "cello", "flowerpot", "wall_clock", "lotus", "jacamar", "dugong", "beacon", "lemon", "carambola", "conch", "whippet", "prayer_rug", "pyramid_cube", "lionfish", "beagle", "kart", "poker", "rock_beauty", "drilling_platform", "printer", "squirrel_monkey", "leaf_fan", "angora", "ipad", "egg", "upright_piano", "espresso_maker", "toaster", "consomme", "arabian_camel", "cowboy_hat", "bulb", "dart", "fly", "handkerchief", "croissant", "scissors", "screw", "redheart", "police_car", "weasel", "cauliflower", "balance_beam", "nintendo_gba", "hair_razor", "stone_lion", "cocktail_shaker", "cocacola", "umbrella", "astronaut", "mule", "sydney_opera_house", "poached_egg", "usb", "backpack", "flat-coated_retriever", "english_setter", "pickup", "bagel", "iceberg", "ice_lolly", "lemur_catta", "chicken_wings", "oriole", "wagtail", "flying_disc", "toilet_tissue", "pubg_lvl3backpack", "pinwheel", "icecream", "leather_shoes", "avocado", "boxing_gloves", "nagoya_castle", "coffee_mug", "scroll_brush", "sunscreen", "motorbike", "cottontail", "aircraft_carrier", "military_vest", "spoonbill", "melon_seed", "rugby_ball", "gourd", "tiltrotor", "sponge", "banjo", "trilobite", "wild_boar", "dwarf_beans", "matchstick", "strainer", "piano_keyboard", "butterfly", "cableways", "cigar", "titi_monkey", "computer_mouse", "baseball_player", "ipod", "children_slide", "kit_fox", "wreck", "solar_dish", "llama", "gazelle", "bluetick", "moon", "french_fries", "hippo", "lobster", "chalk", "saluki", "cornmeal", "toucan", "stupa", "panpipe", "radio_telescope", "manatee", "soup_bowl", "koala", "feeder", "hang_glider", "triumphal_arch", "taro", "snowman", "rocket", "bear", "stonechat", "daisy", "puma_logo", "egyptian_cat", "throne", "nintendo_wiiu", "pretzel", "black_bear", "banana", "sarong", "jinrikisha", "goblet", "stop_sign", "hook", "pumpkin", "rubber_eraser", "eel", "surfboard", "smoothing_iron", "apple_icon", "band-aid", "paper_plane", "red_breasted_merganser", "flying_frog", "hair_drier", "pubg_lvl3helmet", "cup", "balance_weight", "cloud", "badger", "radio", "saxophone", "hock", "dart_target", "whistle", "suitcase", "paper_crane", "calculator", "mushroom", "marimba", "lhasa_apso", "baby", "white_stork", "f1_racing", "burj_al", "staffordshire", "plaice", "chickadee_bird", "camomile", "lark", "carton", "wallet", "beer_bottle", "studio_couch", "ac_ground", "armadillo", "pheasant", "pizza", "dough", "scorpion", "wrench", "equestrian_helmet", "kremlin", "space_shuttle", "coucal", "oyster", "parachute", "cricketball", "panther", "cathedrale_paris", "little_blue_heron", "red_wolf", "car_wheel", "shuriken", "toilet_seat", "ferrari911", "pear", "carp", "lynx", "chiffon_cake", "tiger", "charge_battery", "tractor", "keyboard", "chest", "echidna", "breast_pump", "mailbox", "tredmill", "espresso", "flute", "window_screen", "accordion", "flamingo", "american_alligator", "abe's_flyingfish", "louvre_pyramid", "toothbrush", "common_newt", "terrapin_turtle", "spring_scroll", "kite", "bowtie", "doormat", "steering_wheel", "wolf", "big_ben", "jet_aircraft", "syringe", "abacus", "monitor", "golfcart", "igloo", "cannon", "bottle_cap", "taj_mahal", "tomato", "langur", "skunk", "tennis_racket", "leatherback_turtle", "sturgeon", "stealth_aircraft", "harvester", "pomegranate", "teapot", "shotgun", "triceratops", "volleyball", "lesser_panda", "pyramid", "lettuce", "stapler", "mortar", "ab_wheel", "hen_of_the_woods", "raft", "goldfinch", "zebra", "sea_urchin", "motarboard", "chicory", "pomelo", "stingray", "crayon", "watermelon", "tunnel", "tresher", "steam_locomotive", "paper_towel", "black_swan", "radiator", "totem_pole", "mcdonald_uncle", "fur_coat", "samarra_mosque", "toilet_brush", "sloth_bear", "delta_wing", "snail", "swan", "sushi", "cuckoo", "witch_hat", "gliding_lizard", "missile", "sandbar", "quail", "clam", "school_bus", "red_bayberry", "may_bug", "iphone", "osprey", "flatworm", "earphone2", "modem", "parking_meter", "ambulance", "memory_stick", "killer_whale", "barometer", "nematode", "bushtit", "bustard", "coffin", "shift_gear", "envelope", "pillow", "kangaroo", "ironing_board", "papaya", "bath_ball", "partridge", "guacamole", "torii", "pencil_box", "airship", "arch_bridge", "eggnog", "garbage_truck", "clearwing_flyingfish", "taxi", "ladder", "stopwatch", "hotdog", "vine_snake", "potato", "pig", "santa_sledge", "canton_tower", "gas_pump", "hornbill", "lapwing", "candle", "flying_geckos", "dandie_dinmont", "jordan_logo", "anise", "yurt", "ginger", "agama", "american_chamelon", "sunglasses", "plastic_bag", "window_shade", "peacock", "screwdriver", "baseball_bat", "litchi", "meerkat", "strongbox", "grasshopper", "paddle", "roller_skate", "cassette", "snow_leopard", "cablestayed_bridge", "oiltank_car", "buckler", "orange", "skateboard", "basset", "water_polo", "hard_disk", "monarch_butterfly", "pumpkin_pie", "spoon", "bighorn_sheep", "colubus", "pingpong_racket", "hawthorn", "trailer_truck", "collar", "golden_retriever", "letter_opener", "sled", "can_opener", "dumbbell", "microsd", "hawk", "porcupine", "diamond", "kunai", "brick", "basketball", "loggerhead_turtle", "sulphur_crested", "great_wall", "celery", "photocopier", "file_cabinet", "squirrel", "hornet", "briefcase", "oil_filter", "blossom_card", "flying_squirrel", "mink", "rice_cooker", "crepe", "steak", "bee_eater", "power_drill", "reel", "stork", "ringlet_butterfly", "condor", "english_foxhound", "mongoose", "bittern", "albatross", "single_log", "rubick_cube", "quill_pen", "rain_barrel", "conveyor", "thor's_hammer", "wombat", "chandelier", "ac_wall", "running_shoe", "chess_queen", "redshank", "fig", "toilet_plunger", "cherry", "manx", "box_turtle", "bolotie", "whiptail", "boa_constrictor", "cricket", "bison", "wasp", "soccer_ball", "light_tube", "maraca", "telescope", "vacuum", "relay_stick", "leopard", "gibbon", "kinguin", "pencil_sharpener2", "tobacco_pipe", "digital_clock", "traffic_light", "bathtub", "quad_drone", "remote_control", "key", "tower_pisa", "ganeva_chair", "green_mamba", "chess_knight", "fountain", "ibex", "sniper_rifle", "vinyl", "hummingbird", "microphone", "cicada", "yonex_icon", "sandal", "oscilloscope", "lawn_mower", "hamster", "sleeping_bag", "arctic_fox", "platypus", "bassoon", "sidewinder", "anemone_fish", "recreational_vehicle", "goose", "water_tower", "persimmon", "garbage_can" ]

SkowKyubu/yolo

# yolo This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/z4r0avog) # facebook-detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.1507 - Map: 0.3346 - Map 50: 0.5981 - Map 75: 0.3283 - Map Small: 0.1903 - Map Medium: 0.2508 - Map Large: 0.4752 - Mar 1: 0.3914 - Mar 10: 0.5305 - Mar 100: 0.5425 - Mar Small: 0.2821 - Mar Medium: 0.4111 - Mar Large: 0.705 - Map Coverall: 0.5884 - Mar 100 Coverall: 0.7253 - Map Face Shield: 0.3168 - Mar 100 Face Shield: 0.5818 - Map Gloves: 0.2028 - Mar 100 Gloves: 0.3934 - Map Goggles: 0.2102 - Mar 100 Goggles: 0.558 - Map Mask: 0.3549 - Mar 100 Mask: 0.454 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 3.616 | 1.0 | 107 | 2.9380 | 0.0046 | 0.0138 | 0.0018 | 0.0012 | 0.0002 | 0.0048 | 0.0166 | 0.0632 | 0.1049 | 0.0024 | 0.0187 | 0.1312 | 0.0223 | 0.4442 | 0.0 | 0.0 | 0.0002 | 0.0361 | 0.0 | 0.0 | 0.0007 | 0.0444 | | 2.9914 | 2.0 | 214 | 2.7489 | 0.0138 | 0.0444 | 0.0055 | 0.0018 | 0.0007 | 0.0142 | 0.0292 | 0.0888 | 0.1125 | 0.0024 | 0.041 | 0.1216 | 0.0664 | 0.4253 | 0.0 | 0.0 | 0.0004 | 0.0486 | 0.0 | 0.0 | 0.002 | 0.0884 | | 2.6785 | 3.0 | 321 | 2.5782 | 0.0167 | 0.0534 | 0.0071 | 0.0016 | 0.0005 | 0.0177 | 0.0367 | 0.1002 | 0.1182 | 0.0032 | 0.0382 | 0.1326 | 0.0808 | 0.4584 | 0.0 | 0.0 | 0.0003 | 0.0475 | 0.0 | 0.0 | 0.0022 | 0.0848 | | 2.4754 | 4.0 | 428 | 2.4978 | 0.0229 | 0.0639 | 0.0099 | 0.002 | 0.0042 | 0.0221 | 0.0417 | 0.1182 | 0.1475 | 0.0155 | 0.058 | 0.1507 | 0.1019 | 0.5318 | 0.0 | 0.0 | 0.001 | 0.0776 | 0.0 | 0.0 | 0.0118 | 0.1283 | | 2.2601 | 5.0 | 535 | 2.2584 | 0.0342 | 0.0846 | 0.0251 | 0.0055 | 0.0057 | 0.038 | 0.0602 | 0.1313 | 0.1686 | 0.0127 | 0.0815 | 0.1747 | 0.1557 | 0.5558 | 0.0068 | 0.0273 | 0.0016 | 0.0995 | 0.0 | 0.0 | 0.007 | 0.1606 | | 2.1746 | 6.0 | 642 | 2.0845 | 0.0536 | 0.1098 | 0.0468 | 0.006 | 0.0156 | 0.0545 | 0.0926 | 0.1654 | 0.204 | 0.0391 | 0.1055 | 0.2434 | 0.2312 | 0.5922 | 0.0181 | 0.0964 | 0.0032 | 0.1295 | 0.0 | 0.0 | 0.0157 | 0.202 | | 2.1412 | 7.0 | 749 | 2.1784 | 0.0398 | 0.1094 | 0.0225 | 0.0064 | 0.0174 | 0.0399 | 0.0913 | 0.1778 | 0.2043 | 0.0126 | 0.126 | 0.2373 | 0.1418 | 0.5214 | 0.0394 | 0.1836 | 0.0019 | 0.1246 | 0.0 | 0.0 | 0.0161 | 0.1919 | | 2.094 | 8.0 | 856 | 2.1212 | 0.0477 | 0.1209 | 0.027 | 0.0098 | 0.0087 | 0.0474 | 0.0939 | 0.18 | 0.2051 | 0.0503 | 0.1222 | 0.1987 | 0.2067 | 0.5435 | 0.0102 | 0.1382 | 0.0043 | 0.1355 | 0.0 | 0.0 | 0.0175 | 0.2081 | | 2.0231 | 9.0 | 963 | 1.9452 | 0.0592 | 0.1348 | 0.0455 | 0.0093 | 0.015 | 0.0622 | 0.1093 | 0.2226 | 0.2572 | 0.0603 | 0.1648 | 0.2804 | 0.2438 | 0.6117 | 0.013 | 0.2182 | 0.0085 | 0.1995 | 0.0007 | 0.002 | 0.0299 | 0.2545 | | 1.9703 | 10.0 | 1070 | 1.9528 | 0.0678 | 0.1508 | 0.0521 | 0.0077 | 0.0232 | 0.075 | 0.1116 | 0.2175 | 0.2571 | 0.0347 | 0.1752 | 0.2909 | 0.2741 | 0.6013 | 0.0274 | 0.2164 | 0.0052 | 0.1798 | 0.0 | 0.0 | 0.0326 | 0.2879 | | 1.9284 | 11.0 | 1177 | 1.8626 | 0.0843 | 0.1763 | 0.0776 | 0.0263 | 0.0162 | 0.1032 | 0.1378 | 0.2587 | 0.2928 | 0.0701 | 0.1837 | 0.3684 | 0.3527 | 0.6273 | 0.0321 | 0.3509 | 0.0057 | 0.1902 | 0.0001 | 0.004 | 0.0307 | 0.2914 | | 1.85 | 12.0 | 1284 | 1.8513 | 0.1011 | 0.207 | 0.0878 | 0.0073 | 0.0384 | 0.1079 | 0.1468 | 0.2539 | 0.2795 | 0.0377 | 0.1753 | 0.332 | 0.3676 | 0.6481 | 0.0796 | 0.2818 | 0.0085 | 0.1836 | 0.0005 | 0.012 | 0.0492 | 0.2722 | | 1.7521 | 13.0 | 1391 | 1.7764 | 0.105 | 0.2085 | 0.0924 | 0.0254 | 0.0396 | 0.1046 | 0.1551 | 0.2772 | 0.3019 | 0.0707 | 0.1945 | 0.3994 | 0.3992 | 0.6416 | 0.0502 | 0.3018 | 0.0142 | 0.2306 | 0.0008 | 0.028 | 0.0606 | 0.3076 | | 1.6934 | 14.0 | 1498 | 1.7405 | 0.1262 | 0.2607 | 0.1124 | 0.0365 | 0.0736 | 0.1244 | 0.1663 | 0.2984 | 0.3249 | 0.0764 | 0.2257 | 0.4122 | 0.4113 | 0.6506 | 0.1122 | 0.3527 | 0.0099 | 0.2224 | 0.0025 | 0.044 | 0.0952 | 0.3545 | | 1.6837 | 15.0 | 1605 | 1.6992 | 0.1192 | 0.246 | 0.1049 | 0.0455 | 0.063 | 0.1248 | 0.1809 | 0.3105 | 0.3314 | 0.0763 | 0.223 | 0.4305 | 0.3945 | 0.6435 | 0.1059 | 0.3982 | 0.0159 | 0.235 | 0.0062 | 0.046 | 0.0733 | 0.3343 | | 1.6609 | 16.0 | 1712 | 1.8307 | 0.09 | 0.2062 | 0.0676 | 0.0277 | 0.0295 | 0.107 | 0.1515 | 0.2788 | 0.3128 | 0.0831 | 0.2117 | 0.3951 | 0.3372 | 0.6084 | 0.0415 | 0.3091 | 0.0089 | 0.2426 | 0.007 | 0.08 | 0.0553 | 0.3237 | | 1.646 | 17.0 | 1819 | 1.6690 | 0.1319 | 0.275 | 0.1164 | 0.051 | 0.0747 | 0.1526 | 0.1989 | 0.3525 | 0.384 | 0.123 | 0.2814 | 0.5094 | 0.4002 | 0.6247 | 0.065 | 0.4164 | 0.0259 | 0.2973 | 0.0627 | 0.216 | 0.1058 | 0.3657 | | 1.6016 | 18.0 | 1926 | 1.6489 | 0.1374 | 0.3023 | 0.1079 | 0.0649 | 0.0727 | 0.171 | 0.1936 | 0.3339 | 0.3578 | 0.1163 | 0.2523 | 0.4989 | 0.4021 | 0.5877 | 0.0671 | 0.3782 | 0.0228 | 0.2798 | 0.0493 | 0.174 | 0.1458 | 0.3692 | | 1.5686 | 19.0 | 2033 | 1.6094 | 0.1497 | 0.3101 | 0.1236 | 0.0418 | 0.0841 | 0.1755 | 0.218 | 0.3731 | 0.4009 | 0.1232 | 0.2946 | 0.5219 | 0.4527 | 0.6468 | 0.0936 | 0.4182 | 0.0263 | 0.2934 | 0.0408 | 0.266 | 0.1349 | 0.3803 | | 1.5312 | 20.0 | 2140 | 1.5237 | 0.1732 | 0.3454 | 0.1548 | 0.0729 | 0.1081 | 0.2238 | 0.2383 | 0.3908 | 0.4182 | 0.2073 | 0.2878 | 0.5665 | 0.4815 | 0.6636 | 0.1266 | 0.44 | 0.037 | 0.3115 | 0.0266 | 0.288 | 0.1941 | 0.3879 | | 1.4907 | 21.0 | 2247 | 1.5332 | 0.1792 | 0.3615 | 0.152 | 0.0759 | 0.1167 | 0.2209 | 0.2473 | 0.3973 | 0.4221 | 0.1877 | 0.2963 | 0.5843 | 0.4678 | 0.661 | 0.104 | 0.4491 | 0.0496 | 0.3071 | 0.0722 | 0.314 | 0.2023 | 0.3793 | | 1.4154 | 22.0 | 2354 | 1.5248 | 0.1742 | 0.3551 | 0.1524 | 0.0867 | 0.0917 | 0.2499 | 0.2541 | 0.3974 | 0.4196 | 0.1728 | 0.2843 | 0.5959 | 0.4828 | 0.663 | 0.0991 | 0.4455 | 0.0502 | 0.294 | 0.057 | 0.342 | 0.182 | 0.3535 | | 1.4162 | 23.0 | 2461 | 1.4762 | 0.1931 | 0.3804 | 0.1741 | 0.0783 | 0.1263 | 0.2436 | 0.2567 | 0.3938 | 0.4137 | 0.15 | 0.279 | 0.5965 | 0.4868 | 0.6935 | 0.1134 | 0.4182 | 0.0542 | 0.2863 | 0.0953 | 0.312 | 0.2156 | 0.3586 | | 1.4624 | 24.0 | 2568 | 1.4757 | 0.173 | 0.3577 | 0.1468 | 0.0352 | 0.0993 | 0.2231 | 0.2258 | 0.3952 | 0.4155 | 0.1291 | 0.2987 | 0.5419 | 0.4726 | 0.6552 | 0.0822 | 0.42 | 0.0489 | 0.3186 | 0.0408 | 0.312 | 0.2207 | 0.3717 | | 1.4275 | 25.0 | 2675 | 1.5116 | 0.1759 | 0.3586 | 0.1602 | 0.0576 | 0.13 | 0.2456 | 0.24 | 0.3856 | 0.4077 | 0.1952 | 0.2959 | 0.5276 | 0.4527 | 0.6643 | 0.1087 | 0.4218 | 0.057 | 0.3169 | 0.0409 | 0.278 | 0.2202 | 0.3576 | | 1.4002 | 26.0 | 2782 | 1.4787 | 0.1954 | 0.3737 | 0.1821 | 0.0338 | 0.1296 | 0.2585 | 0.2523 | 0.4039 | 0.4237 | 0.1232 | 0.2934 | 0.6087 | 0.4823 | 0.6701 | 0.1562 | 0.4164 | 0.0361 | 0.2973 | 0.0473 | 0.37 | 0.2552 | 0.3646 | | 1.3991 | 27.0 | 2889 | 1.5002 | 0.1946 | 0.3787 | 0.1846 | 0.0501 | 0.1434 | 0.2684 | 0.2503 | 0.4031 | 0.4282 | 0.1418 | 0.3042 | 0.5904 | 0.4605 | 0.6675 | 0.1255 | 0.4236 | 0.0438 | 0.2907 | 0.0891 | 0.382 | 0.2543 | 0.3773 | | 1.3786 | 28.0 | 2996 | 1.4114 | 0.2167 | 0.4296 | 0.2011 | 0.055 | 0.1596 | 0.2997 | 0.2711 | 0.4271 | 0.4439 | 0.1325 | 0.3265 | 0.611 | 0.5044 | 0.6929 | 0.1611 | 0.4309 | 0.0871 | 0.3322 | 0.0671 | 0.382 | 0.2639 | 0.3813 | | 1.3221 | 29.0 | 3103 | 1.4392 | 0.2103 | 0.4118 | 0.19 | 0.0658 | 0.1463 | 0.2814 | 0.2744 | 0.4262 | 0.4473 | 0.1744 | 0.3121 | 0.6248 | 0.4909 | 0.6864 | 0.1448 | 0.4491 | 0.0675 | 0.2929 | 0.0811 | 0.432 | 0.267 | 0.3763 | | 1.3073 | 30.0 | 3210 | 1.3707 | 0.2243 | 0.4368 | 0.1973 | 0.0558 | 0.1679 | 0.3023 | 0.2749 | 0.4348 | 0.456 | 0.163 | 0.3312 | 0.6184 | 0.5199 | 0.6987 | 0.1462 | 0.4545 | 0.0965 | 0.3536 | 0.0996 | 0.394 | 0.2592 | 0.3793 | | 1.2889 | 31.0 | 3317 | 1.3956 | 0.2121 | 0.4279 | 0.1948 | 0.0479 | 0.1462 | 0.2967 | 0.2589 | 0.4308 | 0.4478 | 0.1559 | 0.3281 | 0.6031 | 0.4899 | 0.6779 | 0.1249 | 0.4309 | 0.0952 | 0.3372 | 0.111 | 0.43 | 0.2395 | 0.3631 | | 1.3164 | 32.0 | 3424 | 1.4315 | 0.2094 | 0.4238 | 0.1815 | 0.0341 | 0.1474 | 0.2662 | 0.2662 | 0.4129 | 0.4247 | 0.0857 | 0.3045 | 0.5793 | 0.4874 | 0.6766 | 0.1177 | 0.3927 | 0.0835 | 0.3027 | 0.1297 | 0.396 | 0.2288 | 0.3556 | | 1.3735 | 33.0 | 3531 | 1.3750 | 0.2322 | 0.4495 | 0.223 | 0.0762 | 0.1597 | 0.297 | 0.3006 | 0.4463 | 0.4602 | 0.1737 | 0.3294 | 0.6188 | 0.5351 | 0.7006 | 0.1626 | 0.4545 | 0.0956 | 0.3251 | 0.1178 | 0.452 | 0.2498 | 0.3687 | | 1.2856 | 34.0 | 3638 | 1.4287 | 0.2257 | 0.4418 | 0.2076 | 0.0646 | 0.1505 | 0.3169 | 0.2616 | 0.4221 | 0.4429 | 0.1341 | 0.3274 | 0.5719 | 0.5082 | 0.6747 | 0.1528 | 0.4509 | 0.11 | 0.3082 | 0.0935 | 0.414 | 0.264 | 0.3667 | | 1.2558 | 35.0 | 3745 | 1.3187 | 0.2401 | 0.4657 | 0.2202 | 0.0721 | 0.1688 | 0.3091 | 0.3008 | 0.4597 | 0.4756 | 0.1618 | 0.3272 | 0.6627 | 0.5188 | 0.7032 | 0.1495 | 0.4891 | 0.1232 | 0.3415 | 0.1313 | 0.45 | 0.2776 | 0.3939 | | 1.1862 | 36.0 | 3852 | 1.2866 | 0.2588 | 0.4753 | 0.2457 | 0.0634 | 0.1939 | 0.3323 | 0.3199 | 0.4643 | 0.4754 | 0.1514 | 0.342 | 0.6447 | 0.5416 | 0.7006 | 0.213 | 0.4745 | 0.13 | 0.3432 | 0.1414 | 0.474 | 0.2682 | 0.3848 | | 1.2361 | 37.0 | 3959 | 1.3003 | 0.2531 | 0.4754 | 0.2405 | 0.0619 | 0.191 | 0.3391 | 0.3006 | 0.4612 | 0.4814 | 0.1487 | 0.3592 | 0.6214 | 0.5226 | 0.7039 | 0.2095 | 0.4873 | 0.1069 | 0.3678 | 0.1434 | 0.46 | 0.2832 | 0.3879 | | 1.1919 | 38.0 | 4066 | 1.3157 | 0.2411 | 0.4507 | 0.2127 | 0.1031 | 0.1691 | 0.3298 | 0.3007 | 0.4654 | 0.4887 | 0.1863 | 0.3628 | 0.6209 | 0.5242 | 0.713 | 0.1632 | 0.4927 | 0.1206 | 0.3699 | 0.1164 | 0.478 | 0.2812 | 0.3899 | | 1.2089 | 39.0 | 4173 | 1.3253 | 0.2378 | 0.4618 | 0.2139 | 0.06 | 0.1662 | 0.3619 | 0.2932 | 0.456 | 0.4747 | 0.1647 | 0.3502 | 0.6456 | 0.5365 | 0.687 | 0.1477 | 0.44 | 0.1341 | 0.3612 | 0.1181 | 0.512 | 0.2526 | 0.3732 | | 1.1989 | 40.0 | 4280 | 1.3079 | 0.243 | 0.4632 | 0.2212 | 0.09 | 0.1745 | 0.3635 | 0.2888 | 0.4601 | 0.4815 | 0.1716 | 0.3643 | 0.6676 | 0.5474 | 0.711 | 0.1511 | 0.5055 | 0.1275 | 0.3596 | 0.1169 | 0.46 | 0.2721 | 0.3712 | | 1.123 | 41.0 | 4387 | 1.2717 | 0.2545 | 0.4648 | 0.2389 | 0.0865 | 0.1746 | 0.3768 | 0.3137 | 0.4805 | 0.5019 | 0.2341 | 0.3727 | 0.6742 | 0.5567 | 0.7182 | 0.1537 | 0.5127 | 0.1243 | 0.3738 | 0.1443 | 0.494 | 0.2933 | 0.4106 | | 1.1247 | 42.0 | 4494 | 1.3207 | 0.2547 | 0.4802 | 0.2359 | 0.0646 | 0.1709 | 0.3638 | 0.3091 | 0.4584 | 0.4749 | 0.1666 | 0.339 | 0.6682 | 0.5703 | 0.7162 | 0.2116 | 0.5055 | 0.1441 | 0.341 | 0.1132 | 0.474 | 0.2342 | 0.3379 | | 1.127 | 43.0 | 4601 | 1.2355 | 0.2651 | 0.5025 | 0.2454 | 0.0659 | 0.2016 | 0.3784 | 0.3272 | 0.4851 | 0.5009 | 0.1616 | 0.3671 | 0.691 | 0.5439 | 0.7188 | 0.2047 | 0.5164 | 0.1539 | 0.382 | 0.1291 | 0.49 | 0.2942 | 0.3975 | | 1.1019 | 44.0 | 4708 | 1.2395 | 0.2684 | 0.4895 | 0.2566 | 0.0892 | 0.1954 | 0.3819 | 0.3367 | 0.4887 | 0.5068 | 0.183 | 0.3718 | 0.6757 | 0.5447 | 0.7195 | 0.2254 | 0.52 | 0.1424 | 0.3781 | 0.1198 | 0.506 | 0.3096 | 0.4106 | | 1.1069 | 45.0 | 4815 | 1.2494 | 0.2673 | 0.4805 | 0.2646 | 0.0882 | 0.1876 | 0.3995 | 0.3261 | 0.4852 | 0.5081 | 0.2162 | 0.3564 | 0.7021 | 0.5524 | 0.7149 | 0.2068 | 0.5255 | 0.1756 | 0.3907 | 0.1071 | 0.498 | 0.2945 | 0.4116 | | 1.0929 | 46.0 | 4922 | 1.2631 | 0.2738 | 0.5088 | 0.2541 | 0.0801 | 0.1949 | 0.375 | 0.3189 | 0.4701 | 0.4968 | 0.2285 | 0.3514 | 0.6741 | 0.5683 | 0.7136 | 0.2407 | 0.5109 | 0.184 | 0.3852 | 0.1181 | 0.49 | 0.2578 | 0.3843 | | 1.1297 | 47.0 | 5029 | 1.2737 | 0.263 | 0.4986 | 0.2373 | 0.0855 | 0.1837 | 0.3832 | 0.315 | 0.4695 | 0.4879 | 0.2051 | 0.3548 | 0.6878 | 0.564 | 0.7052 | 0.2275 | 0.5182 | 0.1486 | 0.3481 | 0.0788 | 0.462 | 0.2959 | 0.4061 | | 1.1593 | 48.0 | 5136 | 1.2690 | 0.2644 | 0.5107 | 0.2612 | 0.0745 | 0.1985 | 0.3765 | 0.312 | 0.4634 | 0.4813 | 0.1852 | 0.3491 | 0.6642 | 0.5391 | 0.7019 | 0.2197 | 0.5073 | 0.1748 | 0.3601 | 0.1367 | 0.466 | 0.2516 | 0.3712 | | 1.1372 | 49.0 | 5243 | 1.2779 | 0.2707 | 0.4987 | 0.2736 | 0.0876 | 0.1936 | 0.4004 | 0.3236 | 0.4708 | 0.489 | 0.2231 | 0.3449 | 0.6683 | 0.5527 | 0.7182 | 0.2438 | 0.5 | 0.1566 | 0.3667 | 0.1353 | 0.466 | 0.2652 | 0.3939 | | 1.0796 | 50.0 | 5350 | 1.1901 | 0.2915 | 0.5427 | 0.2927 | 0.0752 | 0.2182 | 0.428 | 0.336 | 0.4896 | 0.5065 | 0.1738 | 0.3675 | 0.6844 | 0.5609 | 0.7253 | 0.2496 | 0.5073 | 0.1813 | 0.3923 | 0.1545 | 0.496 | 0.3109 | 0.4116 | | 1.0472 | 51.0 | 5457 | 1.2350 | 0.2861 | 0.5365 | 0.2647 | 0.0842 | 0.2116 | 0.4284 | 0.3274 | 0.4874 | 0.4988 | 0.1772 | 0.3617 | 0.6809 | 0.5501 | 0.7136 | 0.247 | 0.5018 | 0.1633 | 0.3339 | 0.1554 | 0.526 | 0.315 | 0.4187 | | 1.0605 | 52.0 | 5564 | 1.1940 | 0.2927 | 0.5423 | 0.2773 | 0.1478 | 0.2135 | 0.429 | 0.3472 | 0.49 | 0.5068 | 0.2497 | 0.3565 | 0.6921 | 0.5544 | 0.7097 | 0.2547 | 0.4818 | 0.1827 | 0.3913 | 0.1602 | 0.528 | 0.3113 | 0.4232 | | 1.0506 | 53.0 | 5671 | 1.2127 | 0.2751 | 0.5239 | 0.2555 | 0.101 | 0.1945 | 0.4041 | 0.3218 | 0.4813 | 0.4952 | 0.2365 | 0.3461 | 0.6708 | 0.5685 | 0.724 | 0.2394 | 0.4873 | 0.1758 | 0.3634 | 0.1136 | 0.52 | 0.2784 | 0.3813 | | 1.053 | 54.0 | 5778 | 1.1906 | 0.2994 | 0.5424 | 0.2941 | 0.1302 | 0.213 | 0.4167 | 0.3406 | 0.5033 | 0.5241 | 0.2372 | 0.3795 | 0.6998 | 0.5648 | 0.7266 | 0.2552 | 0.5273 | 0.1795 | 0.3732 | 0.176 | 0.564 | 0.3217 | 0.4293 | | 1.0142 | 55.0 | 5885 | 1.1860 | 0.2937 | 0.5326 | 0.28 | 0.1125 | 0.2158 | 0.4177 | 0.3366 | 0.4924 | 0.5081 | 0.2158 | 0.3796 | 0.6679 | 0.567 | 0.7195 | 0.2523 | 0.5018 | 0.1687 | 0.3765 | 0.1754 | 0.534 | 0.305 | 0.4086 | | 1.0391 | 56.0 | 5992 | 1.1970 | 0.2856 | 0.5244 | 0.2672 | 0.106 | 0.2012 | 0.4148 | 0.3396 | 0.4901 | 0.5089 | 0.2218 | 0.3697 | 0.6807 | 0.561 | 0.7065 | 0.2175 | 0.4982 | 0.1701 | 0.3661 | 0.1658 | 0.548 | 0.3133 | 0.4258 | | 1.0031 | 57.0 | 6099 | 1.1818 | 0.2964 | 0.5385 | 0.2752 | 0.1798 | 0.2196 | 0.4145 | 0.3537 | 0.5142 | 0.5287 | 0.3015 | 0.3943 | 0.6906 | 0.5677 | 0.7247 | 0.2248 | 0.5382 | 0.1842 | 0.3809 | 0.17 | 0.562 | 0.3353 | 0.4379 | | 0.9794 | 58.0 | 6206 | 1.1965 | 0.2903 | 0.5258 | 0.2852 | 0.1404 | 0.2064 | 0.422 | 0.3436 | 0.4984 | 0.5121 | 0.2596 | 0.3677 | 0.6908 | 0.5615 | 0.7149 | 0.2557 | 0.5091 | 0.155 | 0.3656 | 0.1543 | 0.536 | 0.3247 | 0.4348 | | 1.0123 | 59.0 | 6313 | 1.1943 | 0.2831 | 0.5389 | 0.2672 | 0.1388 | 0.213 | 0.4055 | 0.3503 | 0.5034 | 0.5191 | 0.2761 | 0.3874 | 0.6878 | 0.5536 | 0.711 | 0.2217 | 0.52 | 0.1557 | 0.3672 | 0.1714 | 0.562 | 0.3133 | 0.4354 | | 0.9814 | 60.0 | 6420 | 1.2012 | 0.303 | 0.5553 | 0.2868 | 0.1351 | 0.2209 | 0.4293 | 0.3507 | 0.5019 | 0.5164 | 0.2826 | 0.3747 | 0.6823 | 0.5686 | 0.7104 | 0.2642 | 0.5036 | 0.1664 | 0.3732 | 0.198 | 0.56 | 0.3177 | 0.4348 | | 0.9583 | 61.0 | 6527 | 1.1838 | 0.3041 | 0.5548 | 0.305 | 0.1338 | 0.2199 | 0.4376 | 0.3457 | 0.4977 | 0.5123 | 0.2561 | 0.3739 | 0.6789 | 0.5853 | 0.724 | 0.2694 | 0.5164 | 0.193 | 0.3836 | 0.1766 | 0.522 | 0.2962 | 0.4157 | | 0.9506 | 62.0 | 6634 | 1.1634 | 0.3013 | 0.5503 | 0.2856 | 0.1514 | 0.2189 | 0.4223 | 0.3473 | 0.5123 | 0.5251 | 0.2774 | 0.3852 | 0.6884 | 0.5762 | 0.7117 | 0.2389 | 0.5345 | 0.1946 | 0.3896 | 0.1656 | 0.542 | 0.3309 | 0.4475 | | 0.9914 | 63.0 | 6741 | 1.1681 | 0.3051 | 0.5439 | 0.2905 | 0.1401 | 0.2514 | 0.4296 | 0.3476 | 0.5 | 0.5137 | 0.2761 | 0.4054 | 0.6728 | 0.5816 | 0.7143 | 0.2752 | 0.5436 | 0.1746 | 0.3656 | 0.1677 | 0.518 | 0.3264 | 0.4268 | | 0.9516 | 64.0 | 6848 | 1.1811 | 0.2933 | 0.532 | 0.2921 | 0.1145 | 0.2285 | 0.4111 | 0.3384 | 0.4869 | 0.5034 | 0.2024 | 0.38 | 0.6794 | 0.5576 | 0.7019 | 0.2721 | 0.5291 | 0.1595 | 0.3579 | 0.1524 | 0.502 | 0.3251 | 0.4263 | | 0.9232 | 65.0 | 6955 | 1.1625 | 0.304 | 0.551 | 0.2776 | 0.1706 | 0.2403 | 0.4162 | 0.3477 | 0.5064 | 0.5193 | 0.2514 | 0.3986 | 0.6958 | 0.5644 | 0.7188 | 0.2713 | 0.5327 | 0.1806 | 0.388 | 0.1681 | 0.526 | 0.3357 | 0.4308 | | 0.9479 | 66.0 | 7062 | 1.1899 | 0.3017 | 0.5539 | 0.2705 | 0.157 | 0.2135 | 0.4417 | 0.36 | 0.5122 | 0.5277 | 0.2622 | 0.3857 | 0.7181 | 0.5725 | 0.7136 | 0.2637 | 0.5636 | 0.173 | 0.3814 | 0.1876 | 0.56 | 0.3117 | 0.4197 | | 0.9041 | 67.0 | 7169 | 1.1758 | 0.3142 | 0.5585 | 0.3089 | 0.1431 | 0.2326 | 0.4294 | 0.3619 | 0.5049 | 0.5161 | 0.2519 | 0.3782 | 0.6862 | 0.5954 | 0.7266 | 0.2947 | 0.5182 | 0.1781 | 0.3617 | 0.1817 | 0.558 | 0.3213 | 0.4162 | | 0.9385 | 68.0 | 7276 | 1.1483 | 0.3169 | 0.5782 | 0.2985 | 0.1935 | 0.232 | 0.4517 | 0.3714 | 0.5241 | 0.5357 | 0.2918 | 0.3976 | 0.7021 | 0.5802 | 0.7312 | 0.2987 | 0.5636 | 0.1982 | 0.3896 | 0.1854 | 0.566 | 0.3218 | 0.4283 | | 0.9177 | 69.0 | 7383 | 1.1799 | 0.3144 | 0.5645 | 0.3005 | 0.2049 | 0.2287 | 0.456 | 0.3629 | 0.5138 | 0.5294 | 0.3176 | 0.3806 | 0.6951 | 0.5789 | 0.7227 | 0.2856 | 0.5655 | 0.1928 | 0.3852 | 0.1902 | 0.544 | 0.3246 | 0.4298 | | 0.8781 | 70.0 | 7490 | 1.1637 | 0.3114 | 0.5637 | 0.2894 | 0.1957 | 0.2345 | 0.4385 | 0.3655 | 0.5149 | 0.5293 | 0.3021 | 0.3919 | 0.6927 | 0.58 | 0.7331 | 0.2764 | 0.5473 | 0.1943 | 0.3858 | 0.191 | 0.55 | 0.3151 | 0.4303 | | 0.9337 | 71.0 | 7597 | 1.1627 | 0.3156 | 0.5643 | 0.3023 | 0.1742 | 0.229 | 0.4506 | 0.3683 | 0.517 | 0.5322 | 0.302 | 0.3825 | 0.7129 | 0.5811 | 0.7357 | 0.2999 | 0.54 | 0.1897 | 0.3831 | 0.1859 | 0.578 | 0.3212 | 0.4242 | | 0.8986 | 72.0 | 7704 | 1.1679 | 0.3119 | 0.5658 | 0.2888 | 0.1704 | 0.24 | 0.4547 | 0.3554 | 0.5196 | 0.5338 | 0.2748 | 0.3949 | 0.7075 | 0.5799 | 0.7286 | 0.3 | 0.5855 | 0.1882 | 0.3896 | 0.1744 | 0.534 | 0.317 | 0.4313 | | 0.8766 | 73.0 | 7811 | 1.1545 | 0.319 | 0.5724 | 0.2952 | 0.1869 | 0.2428 | 0.4541 | 0.3684 | 0.5239 | 0.5388 | 0.2905 | 0.4015 | 0.7063 | 0.5834 | 0.7325 | 0.2992 | 0.5782 | 0.1877 | 0.3913 | 0.2001 | 0.564 | 0.3247 | 0.4283 | | 0.8668 | 74.0 | 7918 | 1.1662 | 0.3178 | 0.5811 | 0.3096 | 0.1839 | 0.2384 | 0.4517 | 0.3609 | 0.5171 | 0.5335 | 0.2927 | 0.3982 | 0.6937 | 0.5824 | 0.7344 | 0.3004 | 0.5582 | 0.1927 | 0.3863 | 0.1833 | 0.552 | 0.3304 | 0.4364 | | 0.9248 | 75.0 | 8025 | 1.1544 | 0.3152 | 0.5751 | 0.2907 | 0.1729 | 0.2337 | 0.444 | 0.3545 | 0.5134 | 0.5281 | 0.2933 | 0.3852 | 0.705 | 0.5802 | 0.726 | 0.302 | 0.5473 | 0.1915 | 0.3891 | 0.1634 | 0.528 | 0.3389 | 0.45 | | 0.8511 | 76.0 | 8132 | 1.1887 | 0.3263 | 0.5907 | 0.3062 | 0.1854 | 0.2392 | 0.4552 | 0.3585 | 0.5155 | 0.5295 | 0.2971 | 0.3832 | 0.7044 | 0.5832 | 0.7227 | 0.3305 | 0.5691 | 0.1884 | 0.3678 | 0.1977 | 0.542 | 0.3315 | 0.446 | | 0.8675 | 77.0 | 8239 | 1.1739 | 0.3276 | 0.5954 | 0.3103 | 0.1956 | 0.2426 | 0.4537 | 0.361 | 0.5207 | 0.5333 | 0.2866 | 0.3944 | 0.6991 | 0.5889 | 0.7286 | 0.322 | 0.5655 | 0.2014 | 0.3825 | 0.1879 | 0.54 | 0.3379 | 0.45 | | 0.821 | 78.0 | 8346 | 1.1699 | 0.3277 | 0.5932 | 0.313 | 0.2021 | 0.2523 | 0.4642 | 0.3718 | 0.5263 | 0.5403 | 0.283 | 0.4125 | 0.7088 | 0.5832 | 0.7299 | 0.3294 | 0.5745 | 0.2013 | 0.4011 | 0.1897 | 0.552 | 0.3347 | 0.4439 | | 0.8467 | 79.0 | 8453 | 1.1643 | 0.332 | 0.5979 | 0.3249 | 0.2042 | 0.2473 | 0.4766 | 0.3875 | 0.5246 | 0.5332 | 0.3188 | 0.4013 | 0.6999 | 0.5873 | 0.7318 | 0.3269 | 0.5655 | 0.2038 | 0.3836 | 0.2126 | 0.544 | 0.3296 | 0.4409 | | 0.8951 | 80.0 | 8560 | 1.1533 | 0.3359 | 0.5938 | 0.3165 | 0.2187 | 0.2461 | 0.4588 | 0.3917 | 0.5416 | 0.5544 | 0.3381 | 0.4132 | 0.7085 | 0.5918 | 0.737 | 0.3356 | 0.5836 | 0.1981 | 0.3967 | 0.2108 | 0.604 | 0.3433 | 0.4505 | | 0.8303 | 81.0 | 8667 | 1.1637 | 0.3298 | 0.5901 | 0.306 | 0.1872 | 0.244 | 0.4758 | 0.3891 | 0.5293 | 0.5387 | 0.279 | 0.405 | 0.7137 | 0.5887 | 0.7312 | 0.3384 | 0.5727 | 0.1918 | 0.3776 | 0.1974 | 0.57 | 0.3327 | 0.4419 | | 0.8386 | 82.0 | 8774 | 1.1539 | 0.3293 | 0.5917 | 0.3013 | 0.1962 | 0.2435 | 0.4732 | 0.3916 | 0.5291 | 0.5404 | 0.2927 | 0.4116 | 0.7092 | 0.5858 | 0.7299 | 0.3162 | 0.5764 | 0.1995 | 0.3891 | 0.2015 | 0.556 | 0.3432 | 0.4505 | | 0.809 | 83.0 | 8881 | 1.1587 | 0.3284 | 0.5927 | 0.3001 | 0.1922 | 0.2476 | 0.4659 | 0.3861 | 0.5219 | 0.5347 | 0.2976 | 0.4068 | 0.6911 | 0.5829 | 0.726 | 0.3299 | 0.5818 | 0.1993 | 0.3918 | 0.1948 | 0.54 | 0.3351 | 0.4338 | | 0.8205 | 84.0 | 8988 | 1.1508 | 0.3333 | 0.5942 | 0.3156 | 0.2093 | 0.2456 | 0.4866 | 0.3853 | 0.5314 | 0.5429 | 0.3255 | 0.4043 | 0.7107 | 0.5863 | 0.7279 | 0.3185 | 0.5691 | 0.203 | 0.3885 | 0.2253 | 0.592 | 0.3335 | 0.4369 | | 0.8459 | 85.0 | 9095 | 1.1513 | 0.335 | 0.5963 | 0.3219 | 0.2008 | 0.2476 | 0.4894 | 0.3915 | 0.527 | 0.537 | 0.3072 | 0.3987 | 0.7064 | 0.5833 | 0.7234 | 0.3246 | 0.5709 | 0.2059 | 0.3814 | 0.2144 | 0.562 | 0.3465 | 0.4475 | | 0.8008 | 86.0 | 9202 | 1.1530 | 0.3245 | 0.5927 | 0.3062 | 0.1832 | 0.2477 | 0.4705 | 0.3825 | 0.5248 | 0.5359 | 0.2794 | 0.41 | 0.6952 | 0.5836 | 0.7299 | 0.3002 | 0.5709 | 0.1856 | 0.3749 | 0.2223 | 0.57 | 0.3308 | 0.4338 | | 0.7936 | 87.0 | 9309 | 1.1558 | 0.3266 | 0.5861 | 0.3079 | 0.1893 | 0.2374 | 0.4833 | 0.3848 | 0.5226 | 0.5357 | 0.2929 | 0.4 | 0.7015 | 0.5788 | 0.7299 | 0.3111 | 0.5745 | 0.1929 | 0.3814 | 0.2219 | 0.56 | 0.3281 | 0.4328 | | 0.7991 | 88.0 | 9416 | 1.1667 | 0.3246 | 0.5969 | 0.2974 | 0.167 | 0.2393 | 0.49 | 0.379 | 0.5165 | 0.53 | 0.2649 | 0.4023 | 0.6997 | 0.5824 | 0.726 | 0.3114 | 0.5636 | 0.1853 | 0.3721 | 0.2107 | 0.55 | 0.3335 | 0.4384 | | 0.7914 | 89.0 | 9523 | 1.1521 | 0.3281 | 0.5926 | 0.3144 | 0.1756 | 0.2404 | 0.4755 | 0.3851 | 0.5305 | 0.5402 | 0.2902 | 0.4018 | 0.7063 | 0.5845 | 0.7338 | 0.313 | 0.5636 | 0.2014 | 0.3863 | 0.2031 | 0.576 | 0.3386 | 0.4414 | | 0.7722 | 90.0 | 9630 | 1.1521 | 0.3292 | 0.5978 | 0.3104 | 0.1804 | 0.2419 | 0.4764 | 0.3893 | 0.5308 | 0.5407 | 0.2913 | 0.4016 | 0.7088 | 0.5845 | 0.7253 | 0.3133 | 0.5727 | 0.1944 | 0.382 | 0.2064 | 0.57 | 0.3475 | 0.4535 | | 0.7563 | 91.0 | 9737 | 1.1742 | 0.331 | 0.5967 | 0.3157 | 0.1783 | 0.2413 | 0.4789 | 0.393 | 0.5273 | 0.5397 | 0.302 | 0.403 | 0.7119 | 0.5886 | 0.724 | 0.3014 | 0.5673 | 0.2036 | 0.3874 | 0.2167 | 0.574 | 0.3445 | 0.446 | | 0.7714 | 92.0 | 9844 | 1.1554 | 0.3274 | 0.5924 | 0.3159 | 0.1808 | 0.2393 | 0.4769 | 0.387 | 0.5304 | 0.5426 | 0.2972 | 0.4029 | 0.712 | 0.5857 | 0.7266 | 0.3051 | 0.5855 | 0.1966 | 0.3874 | 0.2062 | 0.568 | 0.3435 | 0.4455 | | 0.7669 | 93.0 | 9951 | 1.1550 | 0.325 | 0.5913 | 0.2998 | 0.1885 | 0.2429 | 0.4674 | 0.3818 | 0.5262 | 0.5388 | 0.3039 | 0.4037 | 0.7082 | 0.5781 | 0.7227 | 0.301 | 0.5673 | 0.1997 | 0.3995 | 0.205 | 0.56 | 0.3414 | 0.4444 | | 0.7811 | 94.0 | 10058 | 1.1591 | 0.3263 | 0.5903 | 0.31 | 0.1742 | 0.2371 | 0.4802 | 0.3841 | 0.5252 | 0.5356 | 0.2761 | 0.3988 | 0.7018 | 0.5788 | 0.7234 | 0.2981 | 0.5582 | 0.1989 | 0.3885 | 0.2056 | 0.558 | 0.3498 | 0.45 | | 0.7932 | 95.0 | 10165 | 1.1517 | 0.3301 | 0.5926 | 0.3201 | 0.1803 | 0.2432 | 0.4713 | 0.3859 | 0.5256 | 0.5361 | 0.2813 | 0.402 | 0.7011 | 0.583 | 0.7292 | 0.3034 | 0.5673 | 0.2052 | 0.3918 | 0.2131 | 0.546 | 0.346 | 0.446 | | 0.77 | 96.0 | 10272 | 1.1538 | 0.3319 | 0.5967 | 0.3222 | 0.1813 | 0.2434 | 0.4755 | 0.3891 | 0.5203 | 0.5319 | 0.2707 | 0.3965 | 0.694 | 0.5833 | 0.7299 | 0.3101 | 0.56 | 0.2049 | 0.3907 | 0.223 | 0.538 | 0.3384 | 0.4409 | | 0.764 | 97.0 | 10379 | 1.1521 | 0.3323 | 0.6031 | 0.3131 | 0.1855 | 0.2427 | 0.4726 | 0.3908 | 0.5256 | 0.5369 | 0.2745 | 0.4053 | 0.6897 | 0.5893 | 0.7266 | 0.31 | 0.5764 | 0.1998 | 0.3913 | 0.2162 | 0.546 | 0.3464 | 0.4444 | | 0.7517 | 98.0 | 10486 | 1.1509 | 0.3328 | 0.601 | 0.3287 | 0.1866 | 0.2448 | 0.478 | 0.3888 | 0.5252 | 0.5395 | 0.2761 | 0.4053 | 0.7042 | 0.5868 | 0.724 | 0.3141 | 0.5782 | 0.1991 | 0.3907 | 0.2159 | 0.554 | 0.3482 | 0.4505 | | 0.7519 | 99.0 | 10593 | 1.1516 | 0.3334 | 0.597 | 0.3165 | 0.1921 | 0.2488 | 0.4761 | 0.3897 | 0.5306 | 0.5431 | 0.2851 | 0.4112 | 0.7061 | 0.5871 | 0.726 | 0.3091 | 0.58 | 0.2027 | 0.3951 | 0.2161 | 0.562 | 0.3522 | 0.4525 | | 0.7373 | 100.0 | 10700 | 1.1507 | 0.3346 | 0.5981 | 0.3283 | 0.1903 | 0.2508 | 0.4752 | 0.3914 | 0.5305 | 0.5425 | 0.2821 | 0.4111 | 0.705 | 0.5884 | 0.7253 | 0.3168 | 0.5818 | 0.2028 | 0.3934 | 0.2102 | 0.558 | 0.3549 | 0.454 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7

# detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]

JuudasMooses/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.6795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.9973 | 0.3195 | 100 | 3.0595 | | 2.5938 | 0.6390 | 200 | 5.8527 | | 2.0334 | 0.9585 | 300 | 5.6795 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

HannesKuslap/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

qubvel-hf/sbchoi-rtdetr_r50vd-finetuned-10k-cppe5

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/koo968ri) # sbchoi-rtdetr_r50vd-finetuned-10k-cppe5 This model is a fine-tuned version of [sbchoi/rtdetr_r50vd](https://huggingface.co/sbchoi/rtdetr_r50vd) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 5.4784 - Map: 0.2239 - Map 50: 0.4506 - Map 75: 0.2138 - Map Small: 0.0183 - Map Medium: 0.1151 - Map Large: 0.2987 - Mar 1: 0.218 - Mar 10: 0.3079 - Mar 100: 0.3163 - Mar Small: 0.0568 - Mar Medium: 0.1656 - Mar Large: 0.402 - Map Coverall: 0.4629 - Mar 100 Coverall: 0.5994 - Map Face Shield: 0.0649 - Mar 100 Face Shield: 0.1417 - Map Gloves: 0.1978 - Mar 100 Gloves: 0.3059 - Map Goggles: 0.0656 - Mar 100 Goggles: 0.1187 - Map Mask: 0.3282 - Mar 100 Mask: 0.4157 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 19.8959 | 1.0 | 107 | 10.4382 | 0.015 | 0.0518 | 0.006 | 0.0 | 0.0032 | 0.0154 | 0.0312 | 0.08 | 0.1101 | 0.0 | 0.0511 | 0.1487 | 0.0705 | 0.2114 | 0.0027 | 0.15 | 0.0001 | 0.0211 | 0.0003 | 0.1083 | 0.0011 | 0.0595 | | 8.7775 | 2.0 | 214 | 9.1554 | 0.0523 | 0.1237 | 0.0395 | 0.0092 | 0.0289 | 0.0704 | 0.0938 | 0.163 | 0.1773 | 0.0122 | 0.1071 | 0.2541 | 0.123 | 0.1861 | 0.0219 | 0.175 | 0.0423 | 0.1573 | 0.0113 | 0.1813 | 0.0632 | 0.187 | | 7.7422 | 3.0 | 321 | 7.4197 | 0.1 | 0.2581 | 0.0614 | 0.0425 | 0.0738 | 0.1211 | 0.1424 | 0.2413 | 0.2591 | 0.1086 | 0.2103 | 0.3067 | 0.16 | 0.2199 | 0.0337 | 0.1933 | 0.1186 | 0.2746 | 0.0085 | 0.225 | 0.179 | 0.3827 | | 7.4523 | 4.0 | 428 | 7.4193 | 0.1107 | 0.2562 | 0.0744 | 0.0116 | 0.066 | 0.1631 | 0.1469 | 0.2205 | 0.2294 | 0.0744 | 0.1243 | 0.3197 | 0.1811 | 0.3301 | 0.0302 | 0.1467 | 0.1121 | 0.2735 | 0.018 | 0.0875 | 0.2122 | 0.3092 | | 7.5623 | 5.0 | 535 | 7.8406 | 0.1154 | 0.2796 | 0.0821 | 0.0134 | 0.0782 | 0.1731 | 0.1308 | 0.1992 | 0.2013 | 0.0254 | 0.1302 | 0.2818 | 0.1452 | 0.2343 | 0.0315 | 0.0767 | 0.1517 | 0.2643 | 0.0161 | 0.1042 | 0.2326 | 0.327 | | 7.6045 | 6.0 | 642 | 7.1243 | 0.1411 | 0.3101 | 0.1111 | 0.0138 | 0.0868 | 0.1868 | 0.1533 | 0.2187 | 0.2257 | 0.0628 | 0.1473 | 0.275 | 0.2607 | 0.3584 | 0.027 | 0.0917 | 0.1651 | 0.3043 | 0.0253 | 0.0562 | 0.2272 | 0.3178 | | 6.8108 | 7.0 | 749 | 7.6319 | 0.1252 | 0.2857 | 0.1 | 0.0103 | 0.0904 | 0.1878 | 0.1441 | 0.2117 | 0.2132 | 0.0609 | 0.151 | 0.2848 | 0.1204 | 0.2229 | 0.0357 | 0.0983 | 0.1194 | 0.2086 | 0.037 | 0.1271 | 0.3136 | 0.4092 | | 6.7824 | 8.0 | 856 | 6.3509 | 0.1761 | 0.3743 | 0.1561 | 0.0176 | 0.1056 | 0.2354 | 0.1887 | 0.2682 | 0.2772 | 0.0262 | 0.1671 | 0.3683 | 0.2966 | 0.3831 | 0.0392 | 0.1517 | 0.1887 | 0.2957 | 0.0216 | 0.1312 | 0.3346 | 0.4243 | | 6.5661 | 9.0 | 963 | 6.9362 | 0.1619 | 0.3614 | 0.1367 | 0.0094 | 0.0878 | 0.2252 | 0.1837 | 0.2428 | 0.2459 | 0.0145 | 0.1376 | 0.334 | 0.2901 | 0.3747 | 0.0753 | 0.1717 | 0.1239 | 0.1876 | 0.0409 | 0.1229 | 0.2793 | 0.3724 | | 6.8621 | 10.0 | 1070 | 7.7606 | 0.1142 | 0.2565 | 0.089 | 0.0125 | 0.0667 | 0.1606 | 0.1302 | 0.1857 | 0.1876 | 0.0286 | 0.1134 | 0.2369 | 0.2217 | 0.3422 | 0.0061 | 0.04 | 0.0766 | 0.1584 | 0.0141 | 0.075 | 0.2525 | 0.3227 | | 7.0258 | 11.0 | 1177 | 6.9053 | 0.1446 | 0.3325 | 0.1254 | 0.0156 | 0.0929 | 0.1954 | 0.1755 | 0.2389 | 0.2431 | 0.0649 | 0.1647 | 0.3001 | 0.243 | 0.3488 | 0.0663 | 0.1583 | 0.1163 | 0.2319 | 0.0351 | 0.1104 | 0.2625 | 0.3659 | | 6.8975 | 12.0 | 1284 | 7.1437 | 0.1348 | 0.318 | 0.101 | 0.0171 | 0.0922 | 0.1828 | 0.1579 | 0.2284 | 0.2338 | 0.0616 | 0.1612 | 0.307 | 0.1919 | 0.2639 | 0.0259 | 0.1017 | 0.1215 | 0.2362 | 0.0229 | 0.15 | 0.3118 | 0.4173 | | 7.1849 | 13.0 | 1391 | 7.5812 | 0.1296 | 0.3093 | 0.0909 | 0.0195 | 0.0742 | 0.1821 | 0.1485 | 0.1964 | 0.2003 | 0.0412 | 0.1191 | 0.2639 | 0.2199 | 0.3114 | 0.0389 | 0.0733 | 0.0835 | 0.173 | 0.0367 | 0.0875 | 0.2691 | 0.3562 | | 7.2413 | 14.0 | 1498 | 6.7032 | 0.1493 | 0.3214 | 0.1337 | 0.0103 | 0.0977 | 0.2105 | 0.1766 | 0.2424 | 0.2465 | 0.0217 | 0.1591 | 0.3171 | 0.2244 | 0.3699 | 0.0346 | 0.1267 | 0.145 | 0.2443 | 0.037 | 0.0979 | 0.3058 | 0.3935 | | 6.2961 | 15.0 | 1605 | 5.8884 | 0.1614 | 0.3412 | 0.137 | 0.0104 | 0.0897 | 0.2213 | 0.1664 | 0.2609 | 0.2766 | 0.0353 | 0.139 | 0.3778 | 0.332 | 0.5386 | 0.0262 | 0.0867 | 0.1593 | 0.2632 | 0.0226 | 0.1167 | 0.2669 | 0.3778 | | 6.1921 | 16.0 | 1712 | 6.0759 | 0.1672 | 0.366 | 0.1332 | 0.0078 | 0.087 | 0.2378 | 0.1742 | 0.2685 | 0.2809 | 0.013 | 0.1333 | 0.3916 | 0.3246 | 0.5416 | 0.0425 | 0.1067 | 0.1582 | 0.2676 | 0.0158 | 0.1021 | 0.2949 | 0.3865 | | 5.901 | 17.0 | 1819 | 5.9836 | 0.1719 | 0.349 | 0.1598 | 0.0146 | 0.0836 | 0.2536 | 0.1752 | 0.2609 | 0.274 | 0.0411 | 0.1335 | 0.3793 | 0.3581 | 0.5265 | 0.0362 | 0.1067 | 0.1555 | 0.2714 | 0.0168 | 0.0917 | 0.293 | 0.3741 | | 5.8931 | 18.0 | 1926 | 5.6273 | 0.1864 | 0.39 | 0.169 | 0.0145 | 0.0894 | 0.2542 | 0.1882 | 0.2834 | 0.2926 | 0.0618 | 0.1544 | 0.3746 | 0.3912 | 0.5651 | 0.0448 | 0.1117 | 0.1629 | 0.2757 | 0.0385 | 0.1167 | 0.2945 | 0.3941 | | 5.7067 | 19.0 | 2033 | 5.8492 | 0.1802 | 0.3771 | 0.1605 | 0.0152 | 0.0957 | 0.244 | 0.193 | 0.2793 | 0.2898 | 0.0396 | 0.1489 | 0.3767 | 0.3808 | 0.5633 | 0.0394 | 0.1217 | 0.1812 | 0.2984 | 0.0279 | 0.1125 | 0.2717 | 0.353 | | 5.8838 | 20.0 | 2140 | 5.5158 | 0.197 | 0.4056 | 0.1799 | 0.0082 | 0.1051 | 0.2671 | 0.2033 | 0.3007 | 0.3178 | 0.0316 | 0.1809 | 0.3955 | 0.4057 | 0.5952 | 0.0475 | 0.1317 | 0.1568 | 0.293 | 0.0655 | 0.1771 | 0.3096 | 0.3919 | | 5.757 | 21.0 | 2247 | 5.8292 | 0.1895 | 0.3919 | 0.1651 | 0.0138 | 0.0887 | 0.2658 | 0.1937 | 0.2791 | 0.2896 | 0.0232 | 0.1351 | 0.3858 | 0.4212 | 0.5994 | 0.0476 | 0.1467 | 0.1219 | 0.2297 | 0.0719 | 0.1146 | 0.2847 | 0.3578 | | 5.5671 | 22.0 | 2354 | 5.5429 | 0.1919 | 0.4088 | 0.1584 | 0.0189 | 0.0879 | 0.268 | 0.1975 | 0.2975 | 0.3105 | 0.0382 | 0.1482 | 0.4233 | 0.4063 | 0.5729 | 0.0606 | 0.165 | 0.1521 | 0.2773 | 0.0788 | 0.1667 | 0.2616 | 0.3708 | | 5.9776 | 23.0 | 2461 | 6.0600 | 0.1805 | 0.3854 | 0.147 | 0.0113 | 0.08 | 0.2629 | 0.1977 | 0.2919 | 0.2998 | 0.0174 | 0.1355 | 0.4161 | 0.3742 | 0.547 | 0.0702 | 0.19 | 0.1298 | 0.2238 | 0.0594 | 0.1833 | 0.2688 | 0.3551 | | 6.0912 | 24.0 | 2568 | 6.2436 | 0.178 | 0.3731 | 0.1579 | 0.0107 | 0.0704 | 0.2571 | 0.1842 | 0.2619 | 0.2687 | 0.0182 | 0.1068 | 0.3908 | 0.4095 | 0.5476 | 0.0585 | 0.1283 | 0.157 | 0.2276 | 0.0474 | 0.1396 | 0.2177 | 0.3005 | | 6.3446 | 25.0 | 2675 | 6.2047 | 0.1718 | 0.3749 | 0.1428 | 0.0138 | 0.0834 | 0.2428 | 0.1772 | 0.2644 | 0.2761 | 0.0477 | 0.1334 | 0.3698 | 0.3639 | 0.5205 | 0.0449 | 0.1267 | 0.1729 | 0.2903 | 0.0472 | 0.1354 | 0.2301 | 0.3076 | | 5.8688 | 26.0 | 2782 | 5.5055 | 0.2074 | 0.4247 | 0.1647 | 0.0089 | 0.0972 | 0.3015 | 0.218 | 0.3273 | 0.3416 | 0.0243 | 0.1589 | 0.4928 | 0.4014 | 0.5753 | 0.0992 | 0.215 | 0.1658 | 0.3027 | 0.0643 | 0.2 | 0.3063 | 0.4151 | | 5.8882 | 27.0 | 2889 | 6.0958 | 0.1899 | 0.3901 | 0.1635 | 0.0066 | 0.0841 | 0.2864 | 0.1968 | 0.281 | 0.2909 | 0.0058 | 0.1351 | 0.4151 | 0.367 | 0.5223 | 0.0766 | 0.1617 | 0.1599 | 0.2557 | 0.0717 | 0.1583 | 0.2745 | 0.3568 | | 5.9026 | 28.0 | 2996 | 5.8641 | 0.1894 | 0.4096 | 0.1672 | 0.0186 | 0.085 | 0.2829 | 0.1837 | 0.2797 | 0.2936 | 0.0456 | 0.1392 | 0.4058 | 0.3684 | 0.5349 | 0.0602 | 0.1417 | 0.1774 | 0.3216 | 0.0601 | 0.1167 | 0.281 | 0.353 | | 5.7844 | 29.0 | 3103 | 5.9831 | 0.1829 | 0.3867 | 0.1519 | 0.0236 | 0.0753 | 0.2618 | 0.1825 | 0.2804 | 0.3 | 0.0797 | 0.1264 | 0.4228 | 0.4015 | 0.5867 | 0.0746 | 0.17 | 0.1316 | 0.2935 | 0.0529 | 0.1167 | 0.254 | 0.333 | | 5.4624 | 30.0 | 3210 | 5.5451 | 0.2103 | 0.4341 | 0.1763 | 0.007 | 0.104 | 0.3013 | 0.2181 | 0.3252 | 0.3413 | 0.0332 | 0.1706 | 0.4648 | 0.4311 | 0.6181 | 0.1039 | 0.21 | 0.1703 | 0.2995 | 0.0464 | 0.1729 | 0.3 | 0.4059 | | 5.3751 | 31.0 | 3317 | 5.6696 | 0.2164 | 0.4465 | 0.1884 | 0.0175 | 0.0944 | 0.3133 | 0.2159 | 0.3192 | 0.3365 | 0.053 | 0.1452 | 0.4768 | 0.4498 | 0.647 | 0.0963 | 0.1783 | 0.1693 | 0.3162 | 0.0427 | 0.1312 | 0.3238 | 0.4097 | | 5.3834 | 32.0 | 3424 | 5.5642 | 0.2084 | 0.4319 | 0.1891 | 0.0167 | 0.1065 | 0.2875 | 0.2009 | 0.3046 | 0.3166 | 0.0536 | 0.1732 | 0.4105 | 0.4126 | 0.5687 | 0.0453 | 0.1433 | 0.2076 | 0.34 | 0.0713 | 0.1396 | 0.3052 | 0.3914 | | 5.6258 | 33.0 | 3531 | 6.1584 | 0.1752 | 0.3825 | 0.142 | 0.0104 | 0.0937 | 0.2504 | 0.1836 | 0.2604 | 0.2714 | 0.0332 | 0.1404 | 0.3709 | 0.3399 | 0.497 | 0.0343 | 0.105 | 0.1651 | 0.2838 | 0.0363 | 0.0917 | 0.3006 | 0.3795 | | 5.891 | 34.0 | 3638 | 5.7572 | 0.1819 | 0.387 | 0.1466 | 0.0139 | 0.0838 | 0.2645 | 0.1921 | 0.2855 | 0.295 | 0.0256 | 0.143 | 0.4095 | 0.3692 | 0.544 | 0.0404 | 0.1233 | 0.1534 | 0.2935 | 0.0656 | 0.1333 | 0.281 | 0.3811 | | 5.2963 | 35.0 | 3745 | 5.5806 | 0.2119 | 0.4322 | 0.1983 | 0.0247 | 0.0959 | 0.3041 | 0.2154 | 0.3071 | 0.3219 | 0.1019 | 0.1576 | 0.4346 | 0.4475 | 0.5916 | 0.0559 | 0.1617 | 0.1749 | 0.2978 | 0.0765 | 0.15 | 0.3049 | 0.4086 | | 5.4486 | 36.0 | 3852 | 5.4363 | 0.2144 | 0.4363 | 0.1795 | 0.0246 | 0.102 | 0.3068 | 0.2103 | 0.304 | 0.3157 | 0.0818 | 0.1577 | 0.4234 | 0.4336 | 0.5819 | 0.0811 | 0.175 | 0.1744 | 0.2903 | 0.061 | 0.1208 | 0.3217 | 0.4103 | | 5.3556 | 37.0 | 3959 | 5.4515 | 0.224 | 0.4305 | 0.2072 | 0.0146 | 0.1068 | 0.3151 | 0.2236 | 0.3178 | 0.3311 | 0.0367 | 0.1779 | 0.4321 | 0.4554 | 0.6102 | 0.0784 | 0.1633 | 0.1903 | 0.3184 | 0.0758 | 0.1542 | 0.3203 | 0.4092 | | 5.5322 | 38.0 | 4066 | 5.7703 | 0.209 | 0.4153 | 0.1905 | 0.0095 | 0.0996 | 0.2975 | 0.2088 | 0.2973 | 0.307 | 0.0441 | 0.1532 | 0.4187 | 0.4287 | 0.5614 | 0.0493 | 0.145 | 0.1987 | 0.3341 | 0.0656 | 0.1333 | 0.3028 | 0.3611 | | 5.6029 | 39.0 | 4173 | 5.5674 | 0.2185 | 0.4515 | 0.1913 | 0.012 | 0.1012 | 0.3171 | 0.2243 | 0.3148 | 0.3294 | 0.0188 | 0.169 | 0.4454 | 0.4315 | 0.5819 | 0.0739 | 0.175 | 0.1828 | 0.3184 | 0.0715 | 0.1542 | 0.3327 | 0.4173 | | 5.5652 | 40.0 | 4280 | 6.3700 | 0.1913 | 0.3917 | 0.1778 | 0.0159 | 0.0829 | 0.2774 | 0.1936 | 0.2656 | 0.2758 | 0.044 | 0.1229 | 0.3773 | 0.3794 | 0.5355 | 0.095 | 0.1383 | 0.1359 | 0.247 | 0.0617 | 0.1104 | 0.2845 | 0.3476 | | 6.0757 | 41.0 | 4387 | 6.1865 | 0.1762 | 0.3632 | 0.1617 | 0.0235 | 0.0823 | 0.2449 | 0.1806 | 0.2524 | 0.2599 | 0.0794 | 0.1258 | 0.3441 | 0.3949 | 0.5205 | 0.0347 | 0.0883 | 0.1377 | 0.2454 | 0.0237 | 0.0792 | 0.2898 | 0.3659 | | 5.7012 | 42.0 | 4494 | 6.3980 | 0.1822 | 0.3586 | 0.1738 | 0.0154 | 0.0854 | 0.2402 | 0.1903 | 0.2665 | 0.2732 | 0.0311 | 0.1329 | 0.3519 | 0.4061 | 0.5518 | 0.0653 | 0.1283 | 0.1038 | 0.1989 | 0.0516 | 0.1146 | 0.2842 | 0.3724 | | 5.3802 | 43.0 | 4601 | 5.3665 | 0.2106 | 0.4207 | 0.1825 | 0.0225 | 0.1026 | 0.2906 | 0.2157 | 0.3075 | 0.3177 | 0.0327 | 0.1608 | 0.4207 | 0.4352 | 0.6054 | 0.0439 | 0.115 | 0.1684 | 0.3151 | 0.0719 | 0.1208 | 0.3338 | 0.4319 | | 5.4719 | 44.0 | 4708 | 5.7122 | 0.2072 | 0.4086 | 0.1859 | 0.0155 | 0.1034 | 0.2908 | 0.2095 | 0.2866 | 0.2949 | 0.0432 | 0.1589 | 0.3876 | 0.4153 | 0.5416 | 0.0485 | 0.1217 | 0.1916 | 0.2968 | 0.0724 | 0.1167 | 0.3084 | 0.3978 | | 5.4061 | 45.0 | 4815 | 5.3479 | 0.2171 | 0.4339 | 0.1925 | 0.0335 | 0.1027 | 0.3095 | 0.2117 | 0.3016 | 0.3108 | 0.1145 | 0.1625 | 0.4053 | 0.4328 | 0.5681 | 0.0406 | 0.1033 | 0.177 | 0.3049 | 0.098 | 0.1396 | 0.3369 | 0.4384 | | 5.3701 | 46.0 | 4922 | 5.7335 | 0.1938 | 0.3969 | 0.1733 | 0.0204 | 0.093 | 0.2671 | 0.1995 | 0.2872 | 0.3008 | 0.0615 | 0.1581 | 0.3877 | 0.4239 | 0.5747 | 0.0468 | 0.125 | 0.1603 | 0.28 | 0.0505 | 0.125 | 0.2874 | 0.3995 | | 5.4386 | 47.0 | 5029 | 5.5070 | 0.2167 | 0.4648 | 0.1839 | 0.0397 | 0.1143 | 0.297 | 0.2241 | 0.3128 | 0.327 | 0.0784 | 0.1872 | 0.4106 | 0.4129 | 0.5711 | 0.0934 | 0.1833 | 0.183 | 0.3119 | 0.0985 | 0.1604 | 0.2956 | 0.4081 | | 5.538 | 48.0 | 5136 | 5.9021 | 0.196 | 0.4063 | 0.1583 | 0.0386 | 0.08 | 0.2826 | 0.2034 | 0.2765 | 0.2853 | 0.0862 | 0.1286 | 0.3832 | 0.4243 | 0.5506 | 0.0823 | 0.16 | 0.1432 | 0.2492 | 0.0531 | 0.0917 | 0.2771 | 0.3751 | | 5.4485 | 49.0 | 5243 | 5.4971 | 0.2043 | 0.4362 | 0.1812 | 0.0161 | 0.097 | 0.277 | 0.2091 | 0.3035 | 0.3142 | 0.0304 | 0.1567 | 0.4055 | 0.4557 | 0.603 | 0.0814 | 0.16 | 0.136 | 0.2568 | 0.0484 | 0.1312 | 0.3 | 0.42 | | 5.3201 | 50.0 | 5350 | 5.6181 | 0.2187 | 0.4415 | 0.1978 | 0.0231 | 0.0955 | 0.3126 | 0.2133 | 0.3034 | 0.3125 | 0.0407 | 0.1525 | 0.4199 | 0.4325 | 0.5669 | 0.1138 | 0.16 | 0.1449 | 0.26 | 0.1097 | 0.175 | 0.2925 | 0.4005 | | 5.6202 | 51.0 | 5457 | 5.6736 | 0.2082 | 0.415 | 0.1912 | 0.0194 | 0.0929 | 0.3019 | 0.2147 | 0.2935 | 0.3021 | 0.0498 | 0.1481 | 0.4087 | 0.432 | 0.556 | 0.0478 | 0.11 | 0.1599 | 0.2627 | 0.11 | 0.1979 | 0.2913 | 0.3838 | | 5.5043 | 52.0 | 5564 | 5.7540 | 0.2152 | 0.4244 | 0.198 | 0.024 | 0.0907 | 0.309 | 0.2191 | 0.303 | 0.3098 | 0.0667 | 0.1542 | 0.4141 | 0.4192 | 0.5398 | 0.0972 | 0.16 | 0.1706 | 0.2762 | 0.0898 | 0.1708 | 0.2991 | 0.4022 | | 5.4446 | 53.0 | 5671 | 5.3788 | 0.2273 | 0.4656 | 0.193 | 0.0183 | 0.1133 | 0.3178 | 0.2377 | 0.3129 | 0.3266 | 0.0609 | 0.1723 | 0.4317 | 0.4162 | 0.5608 | 0.1212 | 0.1783 | 0.1715 | 0.2886 | 0.1082 | 0.1854 | 0.3193 | 0.42 | | 5.4776 | 54.0 | 5778 | 5.6180 | 0.2073 | 0.4264 | 0.1724 | 0.0176 | 0.1079 | 0.2764 | 0.2074 | 0.2949 | 0.3064 | 0.0399 | 0.1632 | 0.3915 | 0.4111 | 0.5554 | 0.0909 | 0.1683 | 0.1553 | 0.2649 | 0.0574 | 0.1167 | 0.3217 | 0.4265 | | 5.4939 | 55.0 | 5885 | 5.5716 | 0.2042 | 0.4311 | 0.1723 | 0.016 | 0.0937 | 0.2869 | 0.2085 | 0.3014 | 0.3129 | 0.0594 | 0.1605 | 0.4079 | 0.4217 | 0.5753 | 0.0589 | 0.1467 | 0.1548 | 0.2827 | 0.0679 | 0.1396 | 0.318 | 0.42 | | 5.2805 | 56.0 | 5992 | 5.3773 | 0.2041 | 0.4212 | 0.1711 | 0.0343 | 0.1026 | 0.278 | 0.2126 | 0.3049 | 0.316 | 0.0873 | 0.1654 | 0.4012 | 0.3985 | 0.5566 | 0.073 | 0.17 | 0.159 | 0.2811 | 0.0496 | 0.1229 | 0.3405 | 0.4492 | | 5.435 | 57.0 | 6099 | 5.7054 | 0.2086 | 0.4337 | 0.1867 | 0.0223 | 0.0939 | 0.2906 | 0.2077 | 0.292 | 0.3008 | 0.0866 | 0.1452 | 0.3944 | 0.4274 | 0.559 | 0.0861 | 0.16 | 0.1584 | 0.2643 | 0.0732 | 0.1271 | 0.2978 | 0.3935 | | 5.4074 | 58.0 | 6206 | 5.7383 | 0.2143 | 0.4488 | 0.1947 | 0.012 | 0.1032 | 0.2983 | 0.2141 | 0.2961 | 0.3071 | 0.0654 | 0.1567 | 0.3987 | 0.4173 | 0.5711 | 0.1133 | 0.1817 | 0.1558 | 0.2557 | 0.075 | 0.1292 | 0.31 | 0.3978 | | 5.4203 | 59.0 | 6313 | 5.8282 | 0.2027 | 0.4063 | 0.1735 | 0.0219 | 0.097 | 0.2848 | 0.2034 | 0.2812 | 0.2893 | 0.0807 | 0.1433 | 0.3785 | 0.3967 | 0.5241 | 0.0692 | 0.16 | 0.1734 | 0.2692 | 0.065 | 0.0958 | 0.3093 | 0.3973 | | 5.429 | 60.0 | 6420 | 5.5048 | 0.2013 | 0.4125 | 0.1838 | 0.0339 | 0.103 | 0.2693 | 0.2001 | 0.2909 | 0.2978 | 0.1268 | 0.1539 | 0.3725 | 0.4207 | 0.5633 | 0.0551 | 0.1367 | 0.167 | 0.2805 | 0.0468 | 0.1042 | 0.317 | 0.4043 | | 5.4682 | 61.0 | 6527 | 5.8043 | 0.2072 | 0.4226 | 0.197 | 0.0257 | 0.1029 | 0.2771 | 0.2042 | 0.2843 | 0.2924 | 0.0821 | 0.1599 | 0.3601 | 0.413 | 0.5404 | 0.0596 | 0.12 | 0.1735 | 0.2832 | 0.0768 | 0.1167 | 0.313 | 0.4016 | | 5.3648 | 62.0 | 6634 | 5.6528 | 0.2191 | 0.4496 | 0.2036 | 0.0395 | 0.1227 | 0.2863 | 0.2181 | 0.3029 | 0.3087 | 0.11 | 0.1759 | 0.3789 | 0.4085 | 0.5301 | 0.0834 | 0.16 | 0.1924 | 0.2908 | 0.06 | 0.1146 | 0.3511 | 0.4481 | | 5.4239 | 63.0 | 6741 | 5.6781 | 0.2203 | 0.4493 | 0.1915 | 0.0484 | 0.1114 | 0.3007 | 0.2141 | 0.2983 | 0.3046 | 0.0873 | 0.1715 | 0.3796 | 0.4097 | 0.5253 | 0.0762 | 0.13 | 0.2113 | 0.3259 | 0.0678 | 0.1187 | 0.3367 | 0.4232 | | 5.462 | 64.0 | 6848 | 5.6454 | 0.2113 | 0.4239 | 0.1881 | 0.0304 | 0.1122 | 0.2978 | 0.2107 | 0.2981 | 0.3086 | 0.0568 | 0.1716 | 0.3965 | 0.393 | 0.5265 | 0.0634 | 0.1417 | 0.1984 | 0.3324 | 0.0619 | 0.1125 | 0.3399 | 0.4297 | | 5.5407 | 65.0 | 6955 | 6.0103 | 0.2027 | 0.4166 | 0.1891 | 0.0212 | 0.1034 | 0.2701 | 0.2019 | 0.2762 | 0.2824 | 0.0628 | 0.1506 | 0.3556 | 0.3955 | 0.5048 | 0.0522 | 0.1217 | 0.1849 | 0.293 | 0.0558 | 0.0771 | 0.3253 | 0.4157 | | 5.8151 | 66.0 | 7062 | 5.6046 | 0.2131 | 0.4179 | 0.1946 | 0.0203 | 0.1078 | 0.289 | 0.2069 | 0.2969 | 0.3064 | 0.0667 | 0.1587 | 0.3923 | 0.4381 | 0.5807 | 0.0814 | 0.1583 | 0.1628 | 0.2681 | 0.0567 | 0.1 | 0.3264 | 0.4249 | | 5.7518 | 67.0 | 7169 | 6.1826 | 0.1905 | 0.3764 | 0.18 | 0.018 | 0.1042 | 0.2507 | 0.1928 | 0.2613 | 0.2665 | 0.0844 | 0.1446 | 0.3312 | 0.4 | 0.506 | 0.0291 | 0.0917 | 0.1851 | 0.2914 | 0.0344 | 0.0688 | 0.304 | 0.3746 | | 5.7898 | 68.0 | 7276 | 5.6343 | 0.2043 | 0.4141 | 0.1788 | 0.0188 | 0.1111 | 0.2593 | 0.207 | 0.2905 | 0.2985 | 0.0947 | 0.1705 | 0.3525 | 0.4386 | 0.5717 | 0.0603 | 0.14 | 0.167 | 0.2778 | 0.0362 | 0.0833 | 0.3194 | 0.4195 | | 5.4899 | 69.0 | 7383 | 5.5064 | 0.2105 | 0.4178 | 0.1815 | 0.0536 | 0.1057 | 0.2802 | 0.2087 | 0.3034 | 0.3124 | 0.1259 | 0.1579 | 0.3948 | 0.4457 | 0.5759 | 0.0721 | 0.1567 | 0.1767 | 0.2908 | 0.0264 | 0.0979 | 0.3317 | 0.4405 | | 5.5614 | 70.0 | 7490 | 5.9147 | 0.1879 | 0.3858 | 0.1698 | 0.0411 | 0.1004 | 0.246 | 0.1963 | 0.2823 | 0.2887 | 0.1108 | 0.1583 | 0.3513 | 0.4046 | 0.5301 | 0.0437 | 0.1183 | 0.1606 | 0.2897 | 0.0223 | 0.1042 | 0.3086 | 0.4011 | | 5.502 | 71.0 | 7597 | 5.7527 | 0.1992 | 0.4014 | 0.1893 | 0.0326 | 0.1013 | 0.2601 | 0.1973 | 0.2851 | 0.2937 | 0.0768 | 0.1534 | 0.3678 | 0.4346 | 0.5542 | 0.054 | 0.135 | 0.1512 | 0.2686 | 0.0376 | 0.0771 | 0.3187 | 0.4335 | | 5.3745 | 72.0 | 7704 | 5.6854 | 0.2101 | 0.4397 | 0.1789 | 0.0181 | 0.0985 | 0.2954 | 0.2087 | 0.2997 | 0.308 | 0.0494 | 0.1565 | 0.3996 | 0.4275 | 0.5578 | 0.0993 | 0.2033 | 0.1524 | 0.2719 | 0.0587 | 0.1063 | 0.3124 | 0.4005 | | 5.478 | 73.0 | 7811 | 5.5914 | 0.2143 | 0.4404 | 0.1894 | 0.0361 | 0.1078 | 0.2929 | 0.2177 | 0.2978 | 0.3066 | 0.0948 | 0.1641 | 0.3871 | 0.4374 | 0.5524 | 0.0901 | 0.1683 | 0.1761 | 0.313 | 0.0519 | 0.1 | 0.3162 | 0.3995 | | 5.4078 | 74.0 | 7918 | 5.4932 | 0.2274 | 0.4827 | 0.1945 | 0.0366 | 0.1166 | 0.311 | 0.2232 | 0.3153 | 0.3248 | 0.0961 | 0.1764 | 0.4136 | 0.44 | 0.5633 | 0.0936 | 0.1933 | 0.2148 | 0.3259 | 0.0631 | 0.1125 | 0.3257 | 0.4292 | | 5.2262 | 75.0 | 8025 | 5.3089 | 0.2239 | 0.4499 | 0.2093 | 0.0182 | 0.1143 | 0.3137 | 0.2196 | 0.3143 | 0.3221 | 0.0651 | 0.1718 | 0.4182 | 0.4429 | 0.5747 | 0.0946 | 0.17 | 0.1882 | 0.3216 | 0.0451 | 0.1104 | 0.3488 | 0.4335 | | 5.2505 | 76.0 | 8132 | 5.5950 | 0.2166 | 0.4315 | 0.1951 | 0.0185 | 0.1073 | 0.309 | 0.2177 | 0.3029 | 0.3129 | 0.0718 | 0.164 | 0.4127 | 0.4407 | 0.5512 | 0.08 | 0.1667 | 0.1763 | 0.2978 | 0.0558 | 0.1271 | 0.3301 | 0.4216 | | 5.2589 | 77.0 | 8239 | 5.3893 | 0.2289 | 0.4676 | 0.2032 | 0.0268 | 0.1122 | 0.3243 | 0.2232 | 0.3189 | 0.3279 | 0.0791 | 0.1659 | 0.4289 | 0.4389 | 0.5771 | 0.0804 | 0.1717 | 0.1938 | 0.3211 | 0.0831 | 0.1333 | 0.3481 | 0.4362 | | 5.158 | 78.0 | 8346 | 5.4600 | 0.2209 | 0.4324 | 0.2044 | 0.0174 | 0.1087 | 0.3026 | 0.2175 | 0.3024 | 0.3113 | 0.0747 | 0.1596 | 0.4028 | 0.4444 | 0.5771 | 0.0736 | 0.1483 | 0.1923 | 0.3032 | 0.0642 | 0.1083 | 0.33 | 0.4195 | | 5.2881 | 79.0 | 8453 | 5.5639 | 0.2199 | 0.4295 | 0.1993 | 0.0205 | 0.1089 | 0.3022 | 0.2114 | 0.2986 | 0.307 | 0.0784 | 0.1555 | 0.4008 | 0.4604 | 0.5801 | 0.0794 | 0.1483 | 0.1871 | 0.2962 | 0.049 | 0.0979 | 0.3237 | 0.4124 | | 5.2688 | 80.0 | 8560 | 5.4073 | 0.221 | 0.4583 | 0.2045 | 0.0442 | 0.1057 | 0.3 | 0.215 | 0.3064 | 0.3151 | 0.0879 | 0.1617 | 0.4029 | 0.4573 | 0.5801 | 0.096 | 0.1783 | 0.1794 | 0.3054 | 0.0463 | 0.0875 | 0.3261 | 0.4243 | | 5.2374 | 81.0 | 8667 | 5.4014 | 0.2221 | 0.4509 | 0.2 | 0.0324 | 0.1089 | 0.3074 | 0.2204 | 0.3108 | 0.319 | 0.0774 | 0.1632 | 0.4133 | 0.4389 | 0.5723 | 0.085 | 0.18 | 0.2015 | 0.3097 | 0.052 | 0.1042 | 0.3333 | 0.4286 | | 5.2208 | 82.0 | 8774 | 5.4175 | 0.2289 | 0.449 | 0.2124 | 0.0471 | 0.1066 | 0.3136 | 0.2269 | 0.3162 | 0.3255 | 0.0902 | 0.1611 | 0.4251 | 0.4557 | 0.5898 | 0.1118 | 0.2017 | 0.2025 | 0.3076 | 0.0472 | 0.1063 | 0.3273 | 0.4222 | | 5.3092 | 83.0 | 8881 | 5.5742 | 0.2091 | 0.4243 | 0.1928 | 0.0163 | 0.1006 | 0.2874 | 0.2142 | 0.2966 | 0.3081 | 0.0533 | 0.1518 | 0.3977 | 0.4426 | 0.5813 | 0.0731 | 0.175 | 0.1763 | 0.2935 | 0.0386 | 0.0833 | 0.3148 | 0.4076 | | 5.3841 | 84.0 | 8988 | 5.5962 | 0.2158 | 0.4373 | 0.1891 | 0.0239 | 0.1024 | 0.2958 | 0.2167 | 0.3028 | 0.3121 | 0.0576 | 0.1562 | 0.4005 | 0.4528 | 0.5759 | 0.0885 | 0.1817 | 0.1699 | 0.2751 | 0.0405 | 0.1021 | 0.3273 | 0.4259 | | 5.3724 | 85.0 | 9095 | 5.6336 | 0.2162 | 0.4289 | 0.1967 | 0.0148 | 0.1008 | 0.2989 | 0.2186 | 0.2993 | 0.3096 | 0.0576 | 0.1572 | 0.3935 | 0.4352 | 0.5735 | 0.0742 | 0.1617 | 0.1661 | 0.2746 | 0.0894 | 0.1208 | 0.3164 | 0.4173 | | 5.3174 | 86.0 | 9202 | 5.4311 | 0.2202 | 0.4377 | 0.1949 | 0.0118 | 0.1035 | 0.3026 | 0.2138 | 0.3086 | 0.3187 | 0.0519 | 0.1592 | 0.4123 | 0.4611 | 0.597 | 0.075 | 0.1683 | 0.1778 | 0.2914 | 0.0653 | 0.1125 | 0.3217 | 0.4243 | | 5.3588 | 87.0 | 9309 | 5.6099 | 0.2102 | 0.4264 | 0.2016 | 0.0098 | 0.101 | 0.2872 | 0.2037 | 0.2961 | 0.3042 | 0.0246 | 0.1528 | 0.3895 | 0.457 | 0.5982 | 0.0576 | 0.15 | 0.1728 | 0.2876 | 0.0494 | 0.0771 | 0.3144 | 0.4081 | | 5.3652 | 88.0 | 9416 | 5.5130 | 0.214 | 0.4258 | 0.1935 | 0.0173 | 0.1086 | 0.2806 | 0.2091 | 0.302 | 0.313 | 0.052 | 0.1613 | 0.3917 | 0.4535 | 0.6084 | 0.0677 | 0.1583 | 0.1857 | 0.2989 | 0.0423 | 0.0771 | 0.3211 | 0.4222 | | 5.3507 | 89.0 | 9523 | 5.5487 | 0.2165 | 0.4273 | 0.2017 | 0.0147 | 0.1075 | 0.2891 | 0.2102 | 0.3003 | 0.3078 | 0.0527 | 0.1573 | 0.3945 | 0.4663 | 0.5982 | 0.0668 | 0.1417 | 0.1759 | 0.2859 | 0.0473 | 0.0958 | 0.3261 | 0.4173 | | 5.3079 | 90.0 | 9630 | 5.5233 | 0.2173 | 0.4314 | 0.1982 | 0.0199 | 0.1091 | 0.2885 | 0.2187 | 0.3076 | 0.317 | 0.0697 | 0.1633 | 0.401 | 0.4609 | 0.6102 | 0.0677 | 0.16 | 0.171 | 0.287 | 0.065 | 0.1125 | 0.3221 | 0.4151 | | 5.3084 | 91.0 | 9737 | 5.5679 | 0.221 | 0.429 | 0.2074 | 0.0271 | 0.1074 | 0.2907 | 0.219 | 0.3044 | 0.313 | 0.0771 | 0.1587 | 0.3927 | 0.4744 | 0.6157 | 0.076 | 0.1467 | 0.181 | 0.2903 | 0.0489 | 0.1 | 0.3248 | 0.4124 | | 5.3048 | 92.0 | 9844 | 5.7454 | 0.209 | 0.4153 | 0.1905 | 0.0224 | 0.1041 | 0.2769 | 0.2082 | 0.2927 | 0.3032 | 0.0488 | 0.1552 | 0.3825 | 0.4488 | 0.603 | 0.0529 | 0.1167 | 0.1875 | 0.2876 | 0.0395 | 0.1 | 0.3161 | 0.4086 | | 5.3667 | 93.0 | 9951 | 5.5465 | 0.2198 | 0.4462 | 0.2006 | 0.0237 | 0.1071 | 0.2946 | 0.217 | 0.3033 | 0.313 | 0.0739 | 0.1637 | 0.3937 | 0.4632 | 0.6054 | 0.0673 | 0.13 | 0.1884 | 0.3 | 0.0578 | 0.1063 | 0.3224 | 0.4232 | | 5.3391 | 94.0 | 10058 | 5.5115 | 0.2158 | 0.4391 | 0.2025 | 0.0185 | 0.1086 | 0.2846 | 0.2157 | 0.3061 | 0.3152 | 0.0549 | 0.164 | 0.3952 | 0.4752 | 0.6157 | 0.0758 | 0.1583 | 0.1781 | 0.287 | 0.0371 | 0.1042 | 0.3127 | 0.4108 | | 5.2343 | 95.0 | 10165 | 5.5251 | 0.2204 | 0.4441 | 0.2043 | 0.0164 | 0.1093 | 0.2925 | 0.2163 | 0.3073 | 0.3172 | 0.0586 | 0.1601 | 0.4069 | 0.4783 | 0.612 | 0.0685 | 0.1583 | 0.1884 | 0.2951 | 0.0477 | 0.1104 | 0.3191 | 0.4103 | | 5.2467 | 96.0 | 10272 | 5.3810 | 0.2182 | 0.4426 | 0.2023 | 0.0206 | 0.11 | 0.2947 | 0.2212 | 0.3112 | 0.3212 | 0.0655 | 0.1642 | 0.4137 | 0.4659 | 0.612 | 0.0601 | 0.145 | 0.1971 | 0.3151 | 0.0488 | 0.1167 | 0.3191 | 0.4173 | | 5.2167 | 97.0 | 10379 | 5.3852 | 0.2288 | 0.4657 | 0.2075 | 0.0193 | 0.1121 | 0.3084 | 0.2222 | 0.3144 | 0.3237 | 0.0576 | 0.165 | 0.4169 | 0.48 | 0.6108 | 0.0767 | 0.1617 | 0.2061 | 0.3173 | 0.0553 | 0.1146 | 0.3259 | 0.4141 | | 5.2097 | 98.0 | 10486 | 5.4532 | 0.2189 | 0.4447 | 0.2072 | 0.0186 | 0.1176 | 0.2845 | 0.2229 | 0.3122 | 0.3196 | 0.0517 | 0.1717 | 0.4046 | 0.4699 | 0.603 | 0.058 | 0.15 | 0.1928 | 0.3027 | 0.0519 | 0.1271 | 0.3221 | 0.4151 | | 5.276 | 99.0 | 10593 | 5.4841 | 0.2195 | 0.4472 | 0.2109 | 0.0152 | 0.1101 | 0.2944 | 0.2192 | 0.3053 | 0.3125 | 0.0535 | 0.1658 | 0.3939 | 0.4666 | 0.5982 | 0.0653 | 0.1417 | 0.1999 | 0.313 | 0.0452 | 0.1 | 0.3207 | 0.4097 | | 5.2471 | 100.0 | 10700 | 5.4784 | 0.2239 | 0.4506 | 0.2138 | 0.0183 | 0.1151 | 0.2987 | 0.218 | 0.3079 | 0.3163 | 0.0568 | 0.1656 | 0.402 | 0.4629 | 0.5994 | 0.0649 | 0.1417 | 0.1978 | 0.3059 | 0.0656 | 0.1187 | 0.3282 | 0.4157 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

aisak-ai/aisak-detect

# AISAK-Detect ## Overview: AISAK-Detect is an integral component of the AISAK-Visual system, specializing in object detection tasks. Leveraging an encoder-decoder transformer architecture with a convolutional backbone, AISAK-Detect excels in accurately and efficiently detecting objects within images. This model enhances the image understanding capabilities of AISAK-Visual, contributing to comprehensive visual analysis. Trained and fine-tuned by the AISAK team, AISAK-Detect is designed to seamlessly integrate into the broader AISAK system, ensuring cohesive performance in image analysis tasks. ## Model Information: - **Model Name**: AISAK-Detect - **Version**: 1.0 - **Model Architecture**: Transformer with convolutional backbone - **Specialization**: AISAK-Detect is a specialized model within the AISAK-Visual system, focusing on object detection tasks. It employs an encoder-decoder transformer architecture with a convolutional backbone, enabling it to effectively analyze images and generate precise object detection results. AISAK-Visual is part of the broader AISAK system and is specialized in image captioning tasks. ## Intended Use: The model demonstrates high accuracy in object detection tasks, leveraging the synergy between its transformer-based encoder-decoder architecture and the convolutional backbone. When utilized in conjunction with AISAK-Visual, it enhances overall performance in image analysis tasks. ## Performance: AISAK-Visual, based on the BLIP framework, achieves state-of-the-art results on image captioning tasks, including image-text retrieval, image captioning, and VQA. Its generalization ability is demonstrated by its strong performance on video-language tasks in a zero-shot manner. ## Ethical Considerations: - **Bias Mitigation**: Efforts have been made to mitigate bias during training; however, users are encouraged to remain vigilant about potential biases in the model's output. - **Fair Use**: Users should exercise caution when using AISAK-Visual in sensitive contexts and ensure fair and ethical use of the generated image captions. ## Limitations: - While proficient in general object detection, AISAK-Detect may encounter challenges in scenarios requiring specialized object recognition or highly cluttered images. - Users should be aware of these limitations and consider them when interpreting the model's outputs. ## Deployment: AISAK-Detect's inferencing capabilities will be seamlessly integrated into the deployment of the AISAK-Visual system. This integration ensures smooth operation and maximizes the synergy between the two models, providing comprehensive image understanding and analysis. ## Caveats: - Users should verify critical decisions based on AISAK-Detect's object detection results, particularly in high-stakes scenarios. Considering the broader context provided by AISAK-Visual is essential for a comprehensive understanding of visual content and informed decision-making. ## Model Card Information: - **Model Card Created**: April 25, 2024 - **Last Updated**: April 25, 2024 - **Contact Information**: For any inquiries or communication regarding AISAK, please contact me at mandelakorilogan@gmail.com. **© 2024 Mandela Logan. All rights reserved.** No part of this model may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the copyright holder. Users are expressly prohibited from creating replications or spaces derived from this model, whether in whole or in part, without the explicit authorization of the copyright holder. Unauthorized use or reproduction of this model is strictly prohibited by copyright law.

[ "n/a", "person", "traffic light", "fire hydrant", "street sign", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "bicycle", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "hat", "backpack", "umbrella", "shoe", "car", "eye glasses", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "motorcycle", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "plate", "wine glass", "cup", "fork", "knife", "airplane", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "bus", "donut", "cake", "chair", "couch", "potted plant", "bed", "mirror", "dining table", "window", "desk", "train", "toilet", "door", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "truck", "toaster", "sink", "refrigerator", "blender", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "boat", "toothbrush" ]

qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/hgyy7wjo) # facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

MarkoLillemagi/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4503 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5641 | 0.8 | 1000 | 1.4503 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

YaroslavPrytula/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9698 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.237 | 0.2 | 500 | 1.0990 | | 1.0403 | 0.4 | 1000 | 0.9698 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "accessories", "bags", "clothing", "shoes" ]

firefiruses/detr-resnet-50_finetuned_cppe5

# detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "dog", "dog", "dogs" ]

qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/y96s266q) # facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.3283 - Map: 0.2947 - Map 50: 0.5809 - Map 75: 0.2681 - Map Small: 0.1583 - Map Medium: 0.23 - Map Large: 0.4971 - Mar 1: 0.2972 - Mar 10: 0.4633 - Mar 100: 0.4771 - Mar Small: 0.2237 - Mar Medium: 0.4317 - Mar Large: 0.7008 - Map Coverall: 0.5445 - Mar 100 Coverall: 0.6829 - Map Face Shield: 0.2753 - Mar 100 Face Shield: 0.4937 - Map Gloves: 0.2028 - Mar 100 Gloves: 0.4098 - Map Goggles: 0.151 - Mar 100 Goggles: 0.3938 - Map Mask: 0.3002 - Mar 100 Mask: 0.4053 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 2.7248 | 1.0 | 107 | 2.6074 | 0.0142 | 0.0396 | 0.0076 | 0.0039 | 0.0015 | 0.0139 | 0.0281 | 0.085 | 0.1226 | 0.0401 | 0.0831 | 0.1324 | 0.0625 | 0.3068 | 0.0 | 0.0 | 0.0024 | 0.1589 | 0.0 | 0.0 | 0.0061 | 0.1476 | | 2.3311 | 2.0 | 214 | 2.3317 | 0.0296 | 0.0744 | 0.019 | 0.007 | 0.0084 | 0.0291 | 0.0548 | 0.1307 | 0.1676 | 0.0627 | 0.1019 | 0.1817 | 0.1282 | 0.4536 | 0.0 | 0.0 | 0.0076 | 0.1777 | 0.0 | 0.0 | 0.0119 | 0.2067 | | 2.1268 | 3.0 | 321 | 2.2497 | 0.0259 | 0.0601 | 0.0189 | 0.0075 | 0.0188 | 0.0236 | 0.0516 | 0.1358 | 0.183 | 0.053 | 0.1058 | 0.2174 | 0.1028 | 0.5477 | 0.0 | 0.0 | 0.0056 | 0.175 | 0.0 | 0.0 | 0.0209 | 0.1924 | | 1.9337 | 4.0 | 428 | 2.0126 | 0.0464 | 0.1088 | 0.0338 | 0.0173 | 0.0202 | 0.0392 | 0.0779 | 0.1704 | 0.2113 | 0.0536 | 0.1312 | 0.2662 | 0.166 | 0.5896 | 0.0 | 0.0 | 0.0117 | 0.2121 | 0.0 | 0.0 | 0.0543 | 0.2547 | | 1.7712 | 5.0 | 535 | 1.9681 | 0.0654 | 0.1458 | 0.0466 | 0.0205 | 0.037 | 0.0593 | 0.0942 | 0.1996 | 0.236 | 0.0773 | 0.1439 | 0.3188 | 0.2289 | 0.6279 | 0.0026 | 0.0367 | 0.0318 | 0.2286 | 0.0 | 0.0 | 0.0636 | 0.2867 | | 1.6867 | 6.0 | 642 | 1.9592 | 0.0736 | 0.1771 | 0.0487 | 0.0041 | 0.0415 | 0.1024 | 0.094 | 0.1794 | 0.2144 | 0.0524 | 0.1234 | 0.3006 | 0.2756 | 0.5964 | 0.0057 | 0.0342 | 0.0178 | 0.2353 | 0.0 | 0.0 | 0.0688 | 0.2062 | | 1.6342 | 7.0 | 749 | 1.8956 | 0.0829 | 0.172 | 0.0727 | 0.0108 | 0.0426 | 0.131 | 0.1135 | 0.2153 | 0.2315 | 0.0463 | 0.1354 | 0.3658 | 0.2921 | 0.5892 | 0.0143 | 0.0873 | 0.0306 | 0.1817 | 0.002 | 0.0077 | 0.0758 | 0.2916 | | 1.6003 | 8.0 | 856 | 1.7564 | 0.1105 | 0.2502 | 0.0854 | 0.0256 | 0.0815 | 0.1467 | 0.1344 | 0.2559 | 0.2844 | 0.074 | 0.2137 | 0.4119 | 0.3628 | 0.6252 | 0.0492 | 0.1987 | 0.036 | 0.2688 | 0.0009 | 0.0262 | 0.1035 | 0.3031 | | 1.5536 | 9.0 | 963 | 1.8190 | 0.1043 | 0.2518 | 0.0728 | 0.0168 | 0.0669 | 0.1254 | 0.1301 | 0.2492 | 0.268 | 0.0637 | 0.1812 | 0.4043 | 0.3236 | 0.5775 | 0.0381 | 0.1823 | 0.043 | 0.2424 | 0.0011 | 0.0569 | 0.1158 | 0.2809 | | 1.5082 | 10.0 | 1070 | 1.7315 | 0.1314 | 0.2986 | 0.1032 | 0.0233 | 0.0862 | 0.1886 | 0.1654 | 0.2975 | 0.3201 | 0.062 | 0.2453 | 0.4825 | 0.3749 | 0.5991 | 0.0633 | 0.2696 | 0.0546 | 0.2728 | 0.0197 | 0.1569 | 0.1446 | 0.3022 | | 1.4375 | 11.0 | 1177 | 1.6288 | 0.1535 | 0.3396 | 0.1235 | 0.0308 | 0.1048 | 0.2523 | 0.1912 | 0.3504 | 0.3762 | 0.1132 | 0.281 | 0.5931 | 0.4365 | 0.65 | 0.0788 | 0.3456 | 0.0635 | 0.2848 | 0.02 | 0.2708 | 0.1687 | 0.3298 | | 1.4056 | 12.0 | 1284 | 1.6457 | 0.1394 | 0.3148 | 0.0984 | 0.0325 | 0.0954 | 0.2416 | 0.1685 | 0.3278 | 0.3516 | 0.0839 | 0.2689 | 0.5579 | 0.3979 | 0.6477 | 0.0657 | 0.2861 | 0.0692 | 0.2688 | 0.0096 | 0.2615 | 0.1546 | 0.2938 | | 1.424 | 13.0 | 1391 | 1.6102 | 0.1626 | 0.3626 | 0.1216 | 0.0531 | 0.1099 | 0.2685 | 0.1902 | 0.3443 | 0.3722 | 0.1117 | 0.299 | 0.5444 | 0.4262 | 0.6423 | 0.0721 | 0.3519 | 0.0749 | 0.283 | 0.0412 | 0.2585 | 0.1984 | 0.3253 | | 1.3553 | 14.0 | 1498 | 1.5945 | 0.1601 | 0.3462 | 0.1318 | 0.0314 | 0.1071 | 0.278 | 0.1977 | 0.3453 | 0.3659 | 0.1093 | 0.2915 | 0.5759 | 0.4558 | 0.6329 | 0.0692 | 0.3291 | 0.0696 | 0.2937 | 0.0229 | 0.2554 | 0.183 | 0.3182 | | 1.3127 | 15.0 | 1605 | 1.6288 | 0.165 | 0.3566 | 0.1315 | 0.0444 | 0.1033 | 0.287 | 0.1937 | 0.3491 | 0.3718 | 0.0778 | 0.2843 | 0.6119 | 0.4463 | 0.6383 | 0.0903 | 0.343 | 0.0818 | 0.3049 | 0.0277 | 0.2569 | 0.1791 | 0.316 | | 1.2941 | 16.0 | 1712 | 1.5854 | 0.1643 | 0.3635 | 0.1281 | 0.0533 | 0.1198 | 0.2617 | 0.2003 | 0.3668 | 0.3896 | 0.1619 | 0.3128 | 0.5948 | 0.439 | 0.641 | 0.1011 | 0.3886 | 0.0718 | 0.2973 | 0.0277 | 0.3123 | 0.1819 | 0.3089 | | 1.271 | 17.0 | 1819 | 1.5453 | 0.1645 | 0.3585 | 0.1352 | 0.069 | 0.1089 | 0.2721 | 0.2053 | 0.3563 | 0.3835 | 0.1498 | 0.3023 | 0.5795 | 0.4413 | 0.65 | 0.0907 | 0.3405 | 0.0652 | 0.2969 | 0.0292 | 0.3092 | 0.1963 | 0.3209 | | 1.2797 | 18.0 | 1926 | 1.4980 | 0.1828 | 0.3932 | 0.1529 | 0.0898 | 0.1312 | 0.3023 | 0.2189 | 0.3885 | 0.4139 | 0.2014 | 0.3349 | 0.6208 | 0.4426 | 0.6338 | 0.1163 | 0.4 | 0.085 | 0.3286 | 0.05 | 0.3585 | 0.2203 | 0.3489 | | 1.2202 | 19.0 | 2033 | 1.5525 | 0.1768 | 0.3837 | 0.1496 | 0.0654 | 0.1163 | 0.3329 | 0.2189 | 0.3765 | 0.4027 | 0.1528 | 0.3314 | 0.6205 | 0.4413 | 0.6171 | 0.1071 | 0.3975 | 0.0922 | 0.329 | 0.0324 | 0.3277 | 0.211 | 0.3422 | | 1.2601 | 20.0 | 2140 | 1.5374 | 0.1806 | 0.3936 | 0.1454 | 0.0636 | 0.1232 | 0.2955 | 0.2168 | 0.373 | 0.401 | 0.1507 | 0.3136 | 0.6096 | 0.4367 | 0.6324 | 0.1267 | 0.4101 | 0.0865 | 0.2737 | 0.0448 | 0.3446 | 0.2084 | 0.344 | | 1.2382 | 21.0 | 2247 | 1.5249 | 0.1687 | 0.3792 | 0.1313 | 0.0512 | 0.1189 | 0.3014 | 0.2056 | 0.3703 | 0.394 | 0.1416 | 0.3245 | 0.5803 | 0.4207 | 0.6266 | 0.0906 | 0.3886 | 0.073 | 0.3022 | 0.0526 | 0.3231 | 0.2066 | 0.3293 | | 1.1701 | 22.0 | 2354 | 1.5312 | 0.1891 | 0.4048 | 0.1572 | 0.0608 | 0.1315 | 0.3261 | 0.2291 | 0.3843 | 0.4069 | 0.135 | 0.3326 | 0.6264 | 0.4435 | 0.6234 | 0.1407 | 0.3684 | 0.0997 | 0.35 | 0.0451 | 0.3492 | 0.2168 | 0.3436 | | 1.1604 | 23.0 | 2461 | 1.4588 | 0.1924 | 0.4116 | 0.1591 | 0.0521 | 0.1244 | 0.3417 | 0.2199 | 0.3947 | 0.4099 | 0.1433 | 0.3346 | 0.6199 | 0.4795 | 0.6293 | 0.1222 | 0.381 | 0.0932 | 0.3335 | 0.0501 | 0.3508 | 0.2167 | 0.3551 | | 1.1605 | 24.0 | 2568 | 1.4838 | 0.1938 | 0.4038 | 0.159 | 0.0595 | 0.1262 | 0.3412 | 0.2126 | 0.3878 | 0.4032 | 0.153 | 0.3075 | 0.6215 | 0.4689 | 0.6419 | 0.1383 | 0.3848 | 0.1099 | 0.354 | 0.0458 | 0.3092 | 0.2061 | 0.3262 | | 1.1148 | 25.0 | 2675 | 1.4525 | 0.19 | 0.3952 | 0.1518 | 0.0459 | 0.1462 | 0.3308 | 0.2158 | 0.3855 | 0.4048 | 0.1365 | 0.3526 | 0.591 | 0.4618 | 0.6446 | 0.1184 | 0.4063 | 0.0995 | 0.35 | 0.0523 | 0.2862 | 0.218 | 0.3369 | | 1.1126 | 26.0 | 2782 | 1.4628 | 0.2043 | 0.4292 | 0.1697 | 0.0581 | 0.1509 | 0.3362 | 0.234 | 0.403 | 0.4236 | 0.1471 | 0.3592 | 0.6312 | 0.4892 | 0.6635 | 0.1204 | 0.3924 | 0.1185 | 0.3442 | 0.0491 | 0.3692 | 0.2444 | 0.3489 | | 1.1128 | 27.0 | 2889 | 1.4258 | 0.2041 | 0.4284 | 0.1715 | 0.0714 | 0.1429 | 0.3312 | 0.232 | 0.4125 | 0.4299 | 0.1504 | 0.3629 | 0.6398 | 0.4813 | 0.6635 | 0.1375 | 0.4392 | 0.1243 | 0.3491 | 0.0419 | 0.3292 | 0.2358 | 0.3684 | | 1.0908 | 28.0 | 2996 | 1.4615 | 0.2072 | 0.425 | 0.1828 | 0.0839 | 0.1465 | 0.3402 | 0.2404 | 0.3906 | 0.4027 | 0.1514 | 0.3253 | 0.6275 | 0.4933 | 0.6482 | 0.1083 | 0.3886 | 0.1146 | 0.3339 | 0.0681 | 0.2908 | 0.2517 | 0.352 | | 1.0785 | 29.0 | 3103 | 1.4452 | 0.195 | 0.4186 | 0.1581 | 0.0527 | 0.1553 | 0.3474 | 0.2285 | 0.3948 | 0.4135 | 0.1668 | 0.3468 | 0.6451 | 0.4759 | 0.6572 | 0.108 | 0.4152 | 0.1055 | 0.3357 | 0.0649 | 0.32 | 0.2207 | 0.3396 | | 1.0677 | 30.0 | 3210 | 1.4368 | 0.2105 | 0.4321 | 0.187 | 0.0744 | 0.1641 | 0.3346 | 0.243 | 0.4065 | 0.4272 | 0.1789 | 0.3693 | 0.6408 | 0.4924 | 0.6662 | 0.1364 | 0.4152 | 0.1271 | 0.3388 | 0.0754 | 0.3785 | 0.2213 | 0.3373 | | 1.0448 | 31.0 | 3317 | 1.4151 | 0.2115 | 0.436 | 0.1687 | 0.063 | 0.1549 | 0.3449 | 0.2306 | 0.4104 | 0.4299 | 0.1689 | 0.3668 | 0.6302 | 0.4999 | 0.6423 | 0.1308 | 0.4304 | 0.1373 | 0.3554 | 0.0478 | 0.3615 | 0.2418 | 0.36 | | 1.0656 | 32.0 | 3424 | 1.4272 | 0.2218 | 0.449 | 0.1816 | 0.0807 | 0.1638 | 0.3717 | 0.2564 | 0.4207 | 0.4405 | 0.1786 | 0.3802 | 0.6742 | 0.4992 | 0.6563 | 0.1638 | 0.4633 | 0.1285 | 0.35 | 0.0649 | 0.3631 | 0.2528 | 0.3698 | | 1.0345 | 33.0 | 3531 | 1.4501 | 0.2144 | 0.4477 | 0.174 | 0.0843 | 0.1471 | 0.3437 | 0.242 | 0.4054 | 0.43 | 0.1869 | 0.3499 | 0.6493 | 0.5021 | 0.645 | 0.1414 | 0.438 | 0.1352 | 0.3585 | 0.0702 | 0.3585 | 0.2233 | 0.3502 | | 1.0243 | 34.0 | 3638 | 1.3969 | 0.2248 | 0.4842 | 0.1808 | 0.0848 | 0.1805 | 0.371 | 0.2491 | 0.4226 | 0.4434 | 0.1466 | 0.405 | 0.6755 | 0.4944 | 0.6721 | 0.1569 | 0.4443 | 0.1482 | 0.3634 | 0.0832 | 0.3754 | 0.2412 | 0.3618 | | 1.0221 | 35.0 | 3745 | 1.4094 | 0.2203 | 0.4537 | 0.1956 | 0.0682 | 0.1655 | 0.3699 | 0.2469 | 0.4174 | 0.4359 | 0.1675 | 0.3785 | 0.6632 | 0.5107 | 0.6613 | 0.1535 | 0.4405 | 0.1419 | 0.3705 | 0.0566 | 0.3462 | 0.239 | 0.3609 | | 0.99 | 36.0 | 3852 | 1.3827 | 0.2092 | 0.4456 | 0.1798 | 0.0706 | 0.1619 | 0.3708 | 0.2594 | 0.4185 | 0.4385 | 0.1516 | 0.39 | 0.6637 | 0.4897 | 0.6536 | 0.1332 | 0.4165 | 0.154 | 0.3674 | 0.057 | 0.4031 | 0.2119 | 0.352 | | 0.9819 | 37.0 | 3959 | 1.4144 | 0.2298 | 0.4652 | 0.1873 | 0.0827 | 0.1817 | 0.3784 | 0.2504 | 0.4264 | 0.4448 | 0.1796 | 0.3827 | 0.68 | 0.5135 | 0.6743 | 0.1888 | 0.4392 | 0.1466 | 0.3714 | 0.0676 | 0.3769 | 0.2325 | 0.3622 | | 0.9652 | 38.0 | 4066 | 1.3730 | 0.2336 | 0.487 | 0.2047 | 0.0886 | 0.1914 | 0.3765 | 0.2472 | 0.4256 | 0.4448 | 0.1851 | 0.3925 | 0.6692 | 0.5115 | 0.6743 | 0.1967 | 0.4544 | 0.1575 | 0.3759 | 0.0675 | 0.3554 | 0.235 | 0.364 | | 0.9397 | 39.0 | 4173 | 1.3323 | 0.2396 | 0.4804 | 0.215 | 0.1072 | 0.1857 | 0.4298 | 0.2672 | 0.4452 | 0.4634 | 0.1984 | 0.3967 | 0.7004 | 0.5192 | 0.6806 | 0.1547 | 0.4785 | 0.1653 | 0.3848 | 0.1059 | 0.3908 | 0.2531 | 0.3822 | | 0.9346 | 40.0 | 4280 | 1.3810 | 0.2354 | 0.488 | 0.2112 | 0.1037 | 0.1849 | 0.4009 | 0.2623 | 0.4279 | 0.4404 | 0.1807 | 0.3652 | 0.6944 | 0.51 | 0.6788 | 0.1698 | 0.4456 | 0.1541 | 0.3509 | 0.098 | 0.3569 | 0.245 | 0.3698 | | 0.9575 | 41.0 | 4387 | 1.3592 | 0.2396 | 0.4808 | 0.2072 | 0.1173 | 0.2005 | 0.373 | 0.2488 | 0.4339 | 0.4533 | 0.2073 | 0.4023 | 0.6733 | 0.5255 | 0.6878 | 0.1735 | 0.4443 | 0.1591 | 0.383 | 0.1032 | 0.3862 | 0.2368 | 0.3653 | | 0.948 | 42.0 | 4494 | 1.3716 | 0.2295 | 0.4945 | 0.1711 | 0.0793 | 0.1755 | 0.3977 | 0.2508 | 0.4201 | 0.4391 | 0.1373 | 0.3812 | 0.6851 | 0.5015 | 0.6644 | 0.1825 | 0.4443 | 0.1456 | 0.3696 | 0.094 | 0.3662 | 0.2238 | 0.3511 | | 0.9254 | 43.0 | 4601 | 1.3677 | 0.238 | 0.4902 | 0.2091 | 0.1018 | 0.1861 | 0.4061 | 0.2651 | 0.4344 | 0.4542 | 0.1866 | 0.389 | 0.6789 | 0.5221 | 0.6968 | 0.1681 | 0.4481 | 0.1749 | 0.4013 | 0.0885 | 0.3677 | 0.2363 | 0.3569 | | 0.9162 | 44.0 | 4708 | 1.4004 | 0.2363 | 0.491 | 0.2079 | 0.0949 | 0.1897 | 0.394 | 0.2511 | 0.4205 | 0.4403 | 0.1717 | 0.391 | 0.6663 | 0.5106 | 0.6919 | 0.1837 | 0.4241 | 0.1573 | 0.3732 | 0.0733 | 0.3385 | 0.2566 | 0.3738 | | 0.9186 | 45.0 | 4815 | 1.3953 | 0.2378 | 0.4922 | 0.2211 | 0.1115 | 0.1843 | 0.3661 | 0.2527 | 0.4224 | 0.4439 | 0.2003 | 0.3923 | 0.6378 | 0.4989 | 0.6761 | 0.2 | 0.4418 | 0.1678 | 0.3821 | 0.0843 | 0.3677 | 0.238 | 0.3516 | | 0.9225 | 46.0 | 4922 | 1.3936 | 0.2395 | 0.5114 | 0.1973 | 0.1111 | 0.1805 | 0.405 | 0.2557 | 0.4384 | 0.4586 | 0.2039 | 0.3929 | 0.6808 | 0.4977 | 0.6716 | 0.1804 | 0.4595 | 0.1716 | 0.3933 | 0.0927 | 0.4062 | 0.2553 | 0.3622 | | 0.9011 | 47.0 | 5029 | 1.3632 | 0.2437 | 0.4962 | 0.2148 | 0.0766 | 0.1902 | 0.4155 | 0.2687 | 0.4318 | 0.452 | 0.1849 | 0.4063 | 0.6736 | 0.528 | 0.6703 | 0.1841 | 0.4696 | 0.1692 | 0.3772 | 0.0796 | 0.3754 | 0.2579 | 0.3676 | | 0.8909 | 48.0 | 5136 | 1.3843 | 0.2513 | 0.5148 | 0.211 | 0.1097 | 0.1896 | 0.4229 | 0.2602 | 0.427 | 0.4438 | 0.1835 | 0.3919 | 0.6837 | 0.5279 | 0.6649 | 0.1892 | 0.4165 | 0.168 | 0.3862 | 0.1023 | 0.3677 | 0.2692 | 0.384 | | 0.9073 | 49.0 | 5243 | 1.3763 | 0.2411 | 0.4936 | 0.208 | 0.1217 | 0.1832 | 0.4036 | 0.2703 | 0.4364 | 0.4507 | 0.2241 | 0.3758 | 0.6765 | 0.5104 | 0.6468 | 0.18 | 0.4747 | 0.1656 | 0.379 | 0.0975 | 0.3646 | 0.2522 | 0.3884 | | 0.8877 | 50.0 | 5350 | 1.3689 | 0.251 | 0.5232 | 0.2096 | 0.1109 | 0.1933 | 0.4366 | 0.2773 | 0.4407 | 0.4563 | 0.208 | 0.3948 | 0.7056 | 0.526 | 0.6712 | 0.1895 | 0.4532 | 0.1796 | 0.392 | 0.106 | 0.3831 | 0.254 | 0.3822 | | 0.8917 | 51.0 | 5457 | 1.3656 | 0.2506 | 0.51 | 0.202 | 0.1155 | 0.1989 | 0.4294 | 0.2728 | 0.4417 | 0.4633 | 0.2124 | 0.4163 | 0.6978 | 0.524 | 0.6797 | 0.1921 | 0.4519 | 0.1789 | 0.3924 | 0.0954 | 0.3954 | 0.2627 | 0.3969 | | 0.8844 | 52.0 | 5564 | 1.3813 | 0.249 | 0.5001 | 0.2201 | 0.0869 | 0.1916 | 0.4365 | 0.253 | 0.4423 | 0.4577 | 0.2158 | 0.3852 | 0.696 | 0.5307 | 0.6829 | 0.1786 | 0.4595 | 0.1711 | 0.3607 | 0.0967 | 0.3892 | 0.2677 | 0.396 | | 0.8548 | 53.0 | 5671 | 1.3952 | 0.2509 | 0.5076 | 0.2131 | 0.0846 | 0.1989 | 0.4249 | 0.2738 | 0.4475 | 0.4634 | 0.1904 | 0.4007 | 0.708 | 0.5228 | 0.6694 | 0.2054 | 0.4785 | 0.1833 | 0.3817 | 0.0732 | 0.3908 | 0.27 | 0.3964 | | 0.8677 | 54.0 | 5778 | 1.4126 | 0.2542 | 0.5102 | 0.2243 | 0.1042 | 0.1943 | 0.4266 | 0.2703 | 0.4432 | 0.464 | 0.2001 | 0.4123 | 0.7115 | 0.5149 | 0.6689 | 0.2095 | 0.4797 | 0.1891 | 0.3973 | 0.0973 | 0.3908 | 0.2605 | 0.3831 | | 0.8411 | 55.0 | 5885 | 1.3719 | 0.2622 | 0.5302 | 0.2162 | 0.1064 | 0.1973 | 0.4546 | 0.2722 | 0.4387 | 0.4582 | 0.2055 | 0.4015 | 0.6793 | 0.5294 | 0.6721 | 0.2128 | 0.4658 | 0.1936 | 0.3853 | 0.1067 | 0.38 | 0.2684 | 0.388 | | 0.8304 | 56.0 | 5992 | 1.3720 | 0.2574 | 0.5284 | 0.2123 | 0.111 | 0.2098 | 0.4288 | 0.2714 | 0.4422 | 0.4608 | 0.2192 | 0.4254 | 0.678 | 0.513 | 0.673 | 0.2099 | 0.457 | 0.1897 | 0.3884 | 0.1077 | 0.3908 | 0.2668 | 0.3947 | | 0.8494 | 57.0 | 6099 | 1.3436 | 0.2688 | 0.5468 | 0.2296 | 0.1174 | 0.2014 | 0.487 | 0.2786 | 0.4496 | 0.4712 | 0.2191 | 0.4218 | 0.7035 | 0.5323 | 0.6797 | 0.2373 | 0.4949 | 0.1761 | 0.3746 | 0.1257 | 0.4169 | 0.2728 | 0.3898 | | 0.8505 | 58.0 | 6206 | 1.3279 | 0.2665 | 0.522 | 0.237 | 0.1173 | 0.2102 | 0.4605 | 0.2776 | 0.4479 | 0.4679 | 0.2063 | 0.4194 | 0.6983 | 0.5201 | 0.6833 | 0.2246 | 0.4772 | 0.1797 | 0.3857 | 0.1267 | 0.3954 | 0.2814 | 0.3978 | | 0.8227 | 59.0 | 6313 | 1.3279 | 0.2668 | 0.5267 | 0.2222 | 0.1304 | 0.208 | 0.4523 | 0.2823 | 0.4514 | 0.4696 | 0.223 | 0.4233 | 0.6969 | 0.5244 | 0.6757 | 0.2493 | 0.5089 | 0.1784 | 0.3982 | 0.1102 | 0.3723 | 0.2717 | 0.3929 | | 0.8129 | 60.0 | 6420 | 1.3400 | 0.2673 | 0.5348 | 0.2388 | 0.1185 | 0.2189 | 0.4501 | 0.2807 | 0.4554 | 0.4708 | 0.2163 | 0.4207 | 0.7048 | 0.5286 | 0.6761 | 0.2222 | 0.4646 | 0.1809 | 0.3969 | 0.1413 | 0.4231 | 0.2635 | 0.3933 | | 0.8054 | 61.0 | 6527 | 1.3815 | 0.2734 | 0.548 | 0.2313 | 0.1199 | 0.2321 | 0.4526 | 0.282 | 0.4534 | 0.4687 | 0.2029 | 0.427 | 0.6887 | 0.5306 | 0.6797 | 0.2432 | 0.4734 | 0.1897 | 0.3942 | 0.1207 | 0.4 | 0.2825 | 0.396 | | 0.7911 | 62.0 | 6634 | 1.3294 | 0.2704 | 0.5431 | 0.2301 | 0.1107 | 0.2143 | 0.4745 | 0.2791 | 0.4557 | 0.4704 | 0.2158 | 0.4193 | 0.7055 | 0.5363 | 0.691 | 0.2281 | 0.4532 | 0.1879 | 0.3929 | 0.1242 | 0.4138 | 0.2756 | 0.4013 | | 0.7883 | 63.0 | 6741 | 1.3769 | 0.2605 | 0.5374 | 0.2202 | 0.1197 | 0.2211 | 0.4287 | 0.2717 | 0.4422 | 0.459 | 0.2065 | 0.4214 | 0.7033 | 0.5196 | 0.6698 | 0.2206 | 0.4646 | 0.176 | 0.3696 | 0.1092 | 0.4108 | 0.2772 | 0.3804 | | 0.786 | 64.0 | 6848 | 1.3379 | 0.2666 | 0.5441 | 0.2257 | 0.1268 | 0.2197 | 0.468 | 0.2779 | 0.4595 | 0.479 | 0.2326 | 0.4344 | 0.7088 | 0.5226 | 0.6779 | 0.2319 | 0.5076 | 0.172 | 0.3933 | 0.1309 | 0.4169 | 0.2755 | 0.3991 | | 0.7776 | 65.0 | 6955 | 1.3192 | 0.2708 | 0.5498 | 0.2165 | 0.1242 | 0.2149 | 0.4638 | 0.2795 | 0.4604 | 0.4736 | 0.2291 | 0.4245 | 0.6993 | 0.5326 | 0.6761 | 0.2312 | 0.4823 | 0.194 | 0.3951 | 0.1255 | 0.4138 | 0.2704 | 0.4004 | | 0.7615 | 66.0 | 7062 | 1.3282 | 0.2745 | 0.5488 | 0.2276 | 0.1299 | 0.2271 | 0.459 | 0.2828 | 0.458 | 0.4734 | 0.2233 | 0.4263 | 0.695 | 0.5327 | 0.6725 | 0.2393 | 0.4949 | 0.1862 | 0.3946 | 0.1283 | 0.4015 | 0.2862 | 0.4036 | | 0.7625 | 67.0 | 7169 | 1.3395 | 0.2778 | 0.5506 | 0.2384 | 0.1129 | 0.2216 | 0.4549 | 0.2754 | 0.4565 | 0.4698 | 0.2099 | 0.4264 | 0.7016 | 0.5473 | 0.6829 | 0.2375 | 0.4835 | 0.1937 | 0.3866 | 0.1235 | 0.3938 | 0.2872 | 0.4022 | | 0.7495 | 68.0 | 7276 | 1.3261 | 0.2763 | 0.5487 | 0.2482 | 0.1388 | 0.2211 | 0.4822 | 0.2841 | 0.4541 | 0.4678 | 0.2334 | 0.4186 | 0.6913 | 0.5344 | 0.6752 | 0.2438 | 0.4633 | 0.1956 | 0.4027 | 0.12 | 0.3954 | 0.2876 | 0.4027 | | 0.752 | 69.0 | 7383 | 1.3089 | 0.2816 | 0.5614 | 0.2464 | 0.1287 | 0.2309 | 0.4715 | 0.2833 | 0.4535 | 0.4704 | 0.2142 | 0.4297 | 0.6813 | 0.5332 | 0.6775 | 0.2508 | 0.4785 | 0.199 | 0.4062 | 0.1332 | 0.3831 | 0.2915 | 0.4067 | | 0.7329 | 70.0 | 7490 | 1.3402 | 0.2703 | 0.5482 | 0.2299 | 0.1397 | 0.2188 | 0.459 | 0.2748 | 0.4504 | 0.4671 | 0.2478 | 0.4077 | 0.6824 | 0.5322 | 0.6788 | 0.2376 | 0.4544 | 0.181 | 0.3955 | 0.1111 | 0.3985 | 0.2895 | 0.4084 | | 0.7383 | 71.0 | 7597 | 1.3367 | 0.2789 | 0.559 | 0.2514 | 0.1478 | 0.2189 | 0.4587 | 0.2852 | 0.462 | 0.4785 | 0.2426 | 0.4202 | 0.6802 | 0.5409 | 0.6928 | 0.2435 | 0.4595 | 0.1959 | 0.4 | 0.1287 | 0.4262 | 0.2855 | 0.4142 | | 0.7223 | 72.0 | 7704 | 1.3356 | 0.2774 | 0.5589 | 0.2312 | 0.1569 | 0.2185 | 0.4519 | 0.2852 | 0.4558 | 0.4692 | 0.2374 | 0.4173 | 0.6811 | 0.5452 | 0.6883 | 0.2422 | 0.4646 | 0.1951 | 0.3888 | 0.1131 | 0.3923 | 0.2915 | 0.412 | | 0.7155 | 73.0 | 7811 | 1.3295 | 0.2745 | 0.5553 | 0.2355 | 0.1444 | 0.2142 | 0.4732 | 0.2876 | 0.4614 | 0.4763 | 0.2288 | 0.4301 | 0.6877 | 0.5413 | 0.6851 | 0.2356 | 0.4835 | 0.1922 | 0.4013 | 0.1264 | 0.4077 | 0.2768 | 0.404 | | 0.7123 | 74.0 | 7918 | 1.3261 | 0.2725 | 0.546 | 0.2292 | 0.1447 | 0.2054 | 0.464 | 0.2856 | 0.4561 | 0.4731 | 0.2266 | 0.4088 | 0.695 | 0.542 | 0.6779 | 0.2295 | 0.4696 | 0.186 | 0.4138 | 0.121 | 0.4062 | 0.2842 | 0.3978 | | 0.7044 | 75.0 | 8025 | 1.3641 | 0.274 | 0.5524 | 0.2362 | 0.1446 | 0.2029 | 0.4664 | 0.29 | 0.4518 | 0.4683 | 0.2221 | 0.416 | 0.6934 | 0.5351 | 0.6761 | 0.2387 | 0.481 | 0.1923 | 0.3933 | 0.1144 | 0.3969 | 0.2895 | 0.3942 | | 0.6946 | 76.0 | 8132 | 1.3353 | 0.2769 | 0.5585 | 0.2302 | 0.1549 | 0.2175 | 0.457 | 0.2855 | 0.4546 | 0.4677 | 0.221 | 0.4138 | 0.6847 | 0.5438 | 0.6833 | 0.2514 | 0.4709 | 0.1899 | 0.392 | 0.1209 | 0.4 | 0.2785 | 0.3924 | | 0.692 | 77.0 | 8239 | 1.3378 | 0.2818 | 0.5631 | 0.2419 | 0.1771 | 0.2198 | 0.4555 | 0.2929 | 0.4592 | 0.4761 | 0.2936 | 0.4141 | 0.6873 | 0.5437 | 0.6815 | 0.2651 | 0.4899 | 0.2011 | 0.4062 | 0.1198 | 0.4108 | 0.2795 | 0.392 | | 0.6912 | 78.0 | 8346 | 1.3104 | 0.2801 | 0.5626 | 0.2337 | 0.1692 | 0.2139 | 0.4725 | 0.2856 | 0.4588 | 0.476 | 0.2346 | 0.4302 | 0.6936 | 0.5434 | 0.6919 | 0.2599 | 0.462 | 0.1845 | 0.4071 | 0.1339 | 0.42 | 0.2787 | 0.3991 | | 0.6841 | 79.0 | 8453 | 1.3479 | 0.2835 | 0.568 | 0.2334 | 0.1536 | 0.2137 | 0.483 | 0.2937 | 0.4524 | 0.4712 | 0.2171 | 0.4099 | 0.7032 | 0.5365 | 0.6811 | 0.2659 | 0.4709 | 0.1912 | 0.4027 | 0.1389 | 0.4108 | 0.2852 | 0.3907 | | 0.6772 | 80.0 | 8560 | 1.3513 | 0.2853 | 0.5623 | 0.2438 | 0.1542 | 0.2224 | 0.4758 | 0.2944 | 0.4557 | 0.4709 | 0.2264 | 0.4131 | 0.7013 | 0.5309 | 0.6784 | 0.266 | 0.4671 | 0.1991 | 0.4103 | 0.1429 | 0.4 | 0.2877 | 0.3987 | | 0.6748 | 81.0 | 8667 | 1.3345 | 0.2853 | 0.5691 | 0.2438 | 0.1607 | 0.2242 | 0.4818 | 0.2931 | 0.4543 | 0.469 | 0.2335 | 0.421 | 0.6924 | 0.5383 | 0.6851 | 0.2584 | 0.4519 | 0.2001 | 0.4031 | 0.1403 | 0.4 | 0.2894 | 0.4049 | | 0.6601 | 82.0 | 8774 | 1.3315 | 0.291 | 0.574 | 0.2597 | 0.1627 | 0.2333 | 0.4837 | 0.2899 | 0.4605 | 0.4754 | 0.2354 | 0.4348 | 0.6868 | 0.5364 | 0.6797 | 0.2761 | 0.4797 | 0.1991 | 0.404 | 0.143 | 0.4031 | 0.3003 | 0.4102 | | 0.6598 | 83.0 | 8881 | 1.3230 | 0.2917 | 0.5794 | 0.2537 | 0.1385 | 0.2287 | 0.4916 | 0.2912 | 0.4621 | 0.477 | 0.2305 | 0.4339 | 0.6913 | 0.5473 | 0.6892 | 0.2669 | 0.4747 | 0.1975 | 0.4205 | 0.1506 | 0.3938 | 0.296 | 0.4067 | | 0.6577 | 84.0 | 8988 | 1.3283 | 0.2947 | 0.5809 | 0.2681 | 0.1583 | 0.23 | 0.4971 | 0.2972 | 0.4633 | 0.4771 | 0.2237 | 0.4317 | 0.7008 | 0.5445 | 0.6829 | 0.2753 | 0.4937 | 0.2028 | 0.4098 | 0.151 | 0.3938 | 0.3002 | 0.4053 | | 0.6657 | 85.0 | 9095 | 1.3270 | 0.292 | 0.5805 | 0.2547 | 0.1669 | 0.224 | 0.4878 | 0.2968 | 0.46 | 0.4757 | 0.2361 | 0.4245 | 0.699 | 0.5422 | 0.6865 | 0.2658 | 0.4696 | 0.2014 | 0.4107 | 0.1552 | 0.4 | 0.2954 | 0.4116 | | 0.6451 | 86.0 | 9202 | 1.3250 | 0.2898 | 0.5695 | 0.2528 | 0.1706 | 0.2256 | 0.4978 | 0.2932 | 0.4548 | 0.4709 | 0.225 | 0.4229 | 0.6988 | 0.5352 | 0.6766 | 0.2675 | 0.4797 | 0.1967 | 0.396 | 0.1485 | 0.3938 | 0.3011 | 0.4084 | | 0.6501 | 87.0 | 9309 | 1.3576 | 0.2934 | 0.5824 | 0.2498 | 0.1556 | 0.2243 | 0.4893 | 0.2938 | 0.4633 | 0.4791 | 0.2446 | 0.4291 | 0.702 | 0.5461 | 0.6847 | 0.2651 | 0.4899 | 0.2 | 0.3969 | 0.1569 | 0.4108 | 0.2989 | 0.4133 | | 0.6444 | 88.0 | 9416 | 1.3638 | 0.2911 | 0.5818 | 0.2514 | 0.1715 | 0.2217 | 0.4913 | 0.2945 | 0.4576 | 0.4745 | 0.2477 | 0.4226 | 0.6918 | 0.5451 | 0.6788 | 0.2577 | 0.4835 | 0.2045 | 0.4049 | 0.1554 | 0.3969 | 0.2929 | 0.4084 | | 0.6275 | 89.0 | 9523 | 1.3529 | 0.2908 | 0.5777 | 0.2417 | 0.1722 | 0.2221 | 0.4779 | 0.2948 | 0.4575 | 0.4722 | 0.2504 | 0.4186 | 0.6857 | 0.5422 | 0.6734 | 0.2696 | 0.481 | 0.2097 | 0.4036 | 0.1463 | 0.3938 | 0.2861 | 0.4093 | | 0.6394 | 90.0 | 9630 | 1.3503 | 0.2913 | 0.5775 | 0.2445 | 0.159 | 0.2181 | 0.4954 | 0.2935 | 0.4579 | 0.4713 | 0.2362 | 0.4156 | 0.6884 | 0.5438 | 0.6784 | 0.2697 | 0.4835 | 0.2041 | 0.4045 | 0.1519 | 0.3862 | 0.2869 | 0.404 | | 0.6301 | 91.0 | 9737 | 1.3381 | 0.2914 | 0.5775 | 0.2397 | 0.1611 | 0.2246 | 0.4956 | 0.2963 | 0.4591 | 0.4737 | 0.241 | 0.426 | 0.6906 | 0.5477 | 0.6802 | 0.262 | 0.4772 | 0.2076 | 0.408 | 0.1554 | 0.4 | 0.2843 | 0.4031 | | 0.632 | 92.0 | 9844 | 1.3426 | 0.2911 | 0.573 | 0.2416 | 0.1699 | 0.2282 | 0.4914 | 0.2991 | 0.4619 | 0.4751 | 0.2436 | 0.4282 | 0.6894 | 0.5428 | 0.6833 | 0.2679 | 0.4886 | 0.2091 | 0.3955 | 0.1519 | 0.4077 | 0.2836 | 0.4004 | | 0.6231 | 93.0 | 9951 | 1.3458 | 0.294 | 0.5787 | 0.2491 | 0.1695 | 0.2263 | 0.4898 | 0.298 | 0.4621 | 0.4764 | 0.2407 | 0.4302 | 0.6841 | 0.546 | 0.6784 | 0.2697 | 0.4797 | 0.2138 | 0.404 | 0.1485 | 0.4062 | 0.292 | 0.4138 | | 0.6162 | 94.0 | 10058 | 1.3339 | 0.2915 | 0.5819 | 0.2501 | 0.1638 | 0.2224 | 0.491 | 0.2978 | 0.4607 | 0.477 | 0.2425 | 0.4217 | 0.697 | 0.5445 | 0.6793 | 0.2713 | 0.4759 | 0.2092 | 0.404 | 0.1482 | 0.4215 | 0.2844 | 0.404 | | 0.6249 | 95.0 | 10165 | 1.3501 | 0.2905 | 0.583 | 0.2494 | 0.1697 | 0.2233 | 0.4881 | 0.2969 | 0.4602 | 0.4746 | 0.243 | 0.4209 | 0.6892 | 0.5373 | 0.6739 | 0.2717 | 0.4937 | 0.2109 | 0.4022 | 0.1501 | 0.4046 | 0.2826 | 0.3987 | | 0.6189 | 96.0 | 10272 | 1.3529 | 0.2904 | 0.5798 | 0.2422 | 0.1658 | 0.2242 | 0.4877 | 0.2938 | 0.4631 | 0.4772 | 0.2482 | 0.4268 | 0.6855 | 0.5395 | 0.6788 | 0.279 | 0.4924 | 0.2083 | 0.3973 | 0.1388 | 0.4154 | 0.2865 | 0.4022 | | 0.6135 | 97.0 | 10379 | 1.3553 | 0.2929 | 0.5853 | 0.248 | 0.167 | 0.2251 | 0.4901 | 0.2972 | 0.4593 | 0.4765 | 0.2379 | 0.4222 | 0.6929 | 0.5369 | 0.6788 | 0.2785 | 0.4861 | 0.2083 | 0.3987 | 0.1524 | 0.4138 | 0.2887 | 0.4049 | | 0.613 | 98.0 | 10486 | 1.3622 | 0.2938 | 0.5851 | 0.2432 | 0.1642 | 0.2285 | 0.4894 | 0.2958 | 0.4603 | 0.4747 | 0.2408 | 0.4212 | 0.6916 | 0.5406 | 0.6766 | 0.2774 | 0.4848 | 0.2088 | 0.3938 | 0.1525 | 0.4092 | 0.2899 | 0.4089 | | 0.6144 | 99.0 | 10593 | 1.3536 | 0.2933 | 0.5833 | 0.245 | 0.1634 | 0.2282 | 0.4935 | 0.2962 | 0.4589 | 0.4745 | 0.2419 | 0.4225 | 0.6914 | 0.5414 | 0.6793 | 0.277 | 0.4873 | 0.2061 | 0.3942 | 0.1554 | 0.4092 | 0.2869 | 0.4027 | | 0.613 | 100.0 | 10700 | 1.3550 | 0.2936 | 0.5835 | 0.2461 | 0.1635 | 0.2284 | 0.4956 | 0.2943 | 0.4587 | 0.4737 | 0.2424 | 0.4204 | 0.6907 | 0.5418 | 0.6788 | 0.2801 | 0.481 | 0.2075 | 0.3955 | 0.1534 | 0.4108 | 0.2852 | 0.4022 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-manual-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/feu7hgwm) # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5-manual-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/3q9cy9us) # jozhang97-deta-resnet-50-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-manual-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/nzag6oys) # sensetime-deformable-detr-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-manual-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/lgrnumaa) # hustvl-yolos-small-finetuned-10k-cppe5-manual-pad This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

Spatiallysaying/detr-finetuned-rwymarkings-horizontal-v1

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6" ]

nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session8

# detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session8 This model is a fine-tuned version of [nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7](https://huggingface.co/nsugianto/detr-resnet50_finetuned_lstabledetv1s9_lsdocelementdetv1type3_session7) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "table", "companylogo", "doctype", "text_1line", "text_multilines", "textgroup", "table_notproduct" ]

tooltest/detr

# detr This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "clothing", "shoes", "bags", "accessories" ]

qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-auto-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/gno0q6ox) # sensetime-deformable-detr-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/vn7jioeq) # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

Spatiallysaying/detr-finetuned-runwaymarkings-Horizontal-v1

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6" ]

qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/6eb20ojg) # facebook-detr-resnet-50-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5-auto-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/lcafrooz) # jozhang97-deta-resnet-50-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/hustvl-yolos-small-finetuned-10k-cppe5-auto-pad

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/d58yz1w7) # hustvl-yolos-small-finetuned-10k-cppe5-auto-pad This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

schoonhovenra/detr-resnet-50_finetuned_cppe5

# detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

schoonhovenra/transformer-OD

# transformer-OD This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

schoonhovenra/20240429_115939

# 20240429_115939 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-reproduce

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/tuning-sota-cppe5/runs/w975f3w4) # sensetime-deformable-detr-finetuned-10k-cppe5-reproduce This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0727 - Map: 0.3471 - Map 50: 0.6376 - Map 75: 0.3269 - Map Small: 0.2239 - Map Medium: 0.2687 - Map Large: 0.5542 - Mar 1: 0.3146 - Mar 10: 0.485 - Mar 100: 0.5045 - Mar Small: 0.3033 - Mar Medium: 0.4315 - Mar Large: 0.7048 - Map Coverall: 0.556 - Mar 100 Coverall: 0.6671 - Map Face Shield: 0.3346 - Mar 100 Face Shield: 0.4873 - Map Gloves: 0.2879 - Mar 100 Gloves: 0.4625 - Map Goggles: 0.2348 - Mar 100 Goggles: 0.44 - Map Mask: 0.3221 - Mar 100 Mask: 0.4653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 11.6745 | 0.9953 | 106 | 1.7743 | 0.0167 | 0.0443 | 0.0098 | 0.0113 | 0.0253 | 0.0122 | 0.0331 | 0.1258 | 0.1673 | 0.0575 | 0.155 | 0.178 | 0.0056 | 0.2622 | 0.008 | 0.1329 | 0.0044 | 0.1192 | 0.0001 | 0.0292 | 0.0654 | 0.2929 | | 1.5556 | 2.0 | 213 | 1.5655 | 0.0293 | 0.0676 | 0.0249 | 0.0265 | 0.0367 | 0.0521 | 0.042 | 0.1789 | 0.2423 | 0.1235 | 0.2148 | 0.3336 | 0.0138 | 0.3158 | 0.0073 | 0.2038 | 0.0123 | 0.2844 | 0.0001 | 0.0185 | 0.1131 | 0.3889 | | 1.4143 | 2.9953 | 319 | 1.4570 | 0.074 | 0.1565 | 0.0634 | 0.0315 | 0.0468 | 0.0894 | 0.1088 | 0.2778 | 0.3228 | 0.1222 | 0.2836 | 0.3768 | 0.2084 | 0.5955 | 0.013 | 0.2203 | 0.0184 | 0.3112 | 0.0033 | 0.1246 | 0.1267 | 0.3627 | | 1.2557 | 4.0 | 426 | 1.3799 | 0.1076 | 0.212 | 0.099 | 0.0439 | 0.0618 | 0.1269 | 0.1323 | 0.3206 | 0.3609 | 0.1514 | 0.3238 | 0.4669 | 0.3272 | 0.6257 | 0.0165 | 0.257 | 0.0337 | 0.3634 | 0.0069 | 0.16 | 0.1537 | 0.3987 | | 1.1941 | 4.9953 | 532 | 1.3209 | 0.1144 | 0.2176 | 0.1087 | 0.0438 | 0.0713 | 0.151 | 0.1434 | 0.3609 | 0.4018 | 0.2001 | 0.3457 | 0.564 | 0.3425 | 0.6644 | 0.0144 | 0.2949 | 0.0407 | 0.3665 | 0.0154 | 0.2585 | 0.1591 | 0.4244 | | 1.155 | 6.0 | 639 | 1.2990 | 0.1515 | 0.2906 | 0.1425 | 0.0672 | 0.1129 | 0.1945 | 0.1753 | 0.3935 | 0.4237 | 0.1877 | 0.368 | 0.598 | 0.4158 | 0.6707 | 0.0406 | 0.3203 | 0.0925 | 0.3951 | 0.0189 | 0.3138 | 0.1894 | 0.4187 | | 1.1215 | 6.9953 | 745 | 1.2747 | 0.1571 | 0.309 | 0.143 | 0.0696 | 0.125 | 0.1889 | 0.1724 | 0.3909 | 0.43 | 0.2033 | 0.3851 | 0.604 | 0.4395 | 0.6743 | 0.0498 | 0.381 | 0.0871 | 0.4036 | 0.018 | 0.2692 | 0.1911 | 0.4218 | | 1.0717 | 8.0 | 852 | 1.2178 | 0.1571 | 0.3227 | 0.1375 | 0.0901 | 0.1195 | 0.2164 | 0.198 | 0.4041 | 0.4433 | 0.2476 | 0.4 | 0.5814 | 0.4163 | 0.6811 | 0.0407 | 0.3582 | 0.0893 | 0.4116 | 0.049 | 0.3246 | 0.1903 | 0.4409 | | 1.0565 | 8.9953 | 958 | 1.2809 | 0.1655 | 0.3272 | 0.1587 | 0.091 | 0.1352 | 0.2086 | 0.2214 | 0.4057 | 0.4395 | 0.2224 | 0.3794 | 0.6548 | 0.3741 | 0.6495 | 0.0995 | 0.4316 | 0.098 | 0.3714 | 0.0485 | 0.3277 | 0.2076 | 0.4173 | | 1.0112 | 10.0 | 1065 | 1.1774 | 0.2008 | 0.3844 | 0.2 | 0.0948 | 0.1963 | 0.2942 | 0.2258 | 0.4368 | 0.4664 | 0.273 | 0.4293 | 0.6294 | 0.4777 | 0.6856 | 0.0681 | 0.343 | 0.1224 | 0.4321 | 0.09 | 0.3938 | 0.246 | 0.4773 | | 0.9978 | 10.9953 | 1171 | 1.1756 | 0.2299 | 0.4406 | 0.2158 | 0.0987 | 0.208 | 0.3558 | 0.2403 | 0.4529 | 0.4784 | 0.2767 | 0.4338 | 0.6759 | 0.474 | 0.6856 | 0.1359 | 0.4127 | 0.1528 | 0.4388 | 0.1218 | 0.3923 | 0.2652 | 0.4627 | | 0.9543 | 12.0 | 1278 | 1.1263 | 0.2416 | 0.459 | 0.226 | 0.129 | 0.2064 | 0.3378 | 0.2497 | 0.4603 | 0.4893 | 0.3098 | 0.439 | 0.6639 | 0.51 | 0.6892 | 0.133 | 0.4228 | 0.1824 | 0.4393 | 0.116 | 0.4385 | 0.2665 | 0.4569 | | 0.9578 | 12.9953 | 1384 | 1.1217 | 0.2622 | 0.4835 | 0.2467 | 0.1248 | 0.2321 | 0.4226 | 0.2676 | 0.4575 | 0.4884 | 0.3023 | 0.4572 | 0.661 | 0.5304 | 0.6977 | 0.172 | 0.4139 | 0.1784 | 0.4603 | 0.1386 | 0.4062 | 0.2917 | 0.464 | | 0.9196 | 14.0 | 1491 | 1.1083 | 0.26 | 0.4885 | 0.2469 | 0.1338 | 0.2185 | 0.3915 | 0.2684 | 0.4657 | 0.4912 | 0.2804 | 0.4423 | 0.6744 | 0.5478 | 0.7005 | 0.1698 | 0.4684 | 0.1808 | 0.4246 | 0.1119 | 0.4 | 0.2895 | 0.4627 | | 0.8938 | 14.9953 | 1597 | 1.1487 | 0.2506 | 0.4849 | 0.2305 | 0.1269 | 0.2174 | 0.3739 | 0.2597 | 0.4527 | 0.4777 | 0.2813 | 0.4375 | 0.6726 | 0.5043 | 0.6748 | 0.1642 | 0.462 | 0.1753 | 0.4321 | 0.125 | 0.3785 | 0.2842 | 0.4413 | | 0.8907 | 16.0 | 1704 | 1.1091 | 0.2545 | 0.4905 | 0.2477 | 0.1267 | 0.2276 | 0.392 | 0.2634 | 0.4574 | 0.4881 | 0.3075 | 0.4439 | 0.6852 | 0.524 | 0.695 | 0.1588 | 0.4443 | 0.188 | 0.4634 | 0.1298 | 0.3815 | 0.2717 | 0.4564 | | 0.8669 | 16.9953 | 1810 | 1.0938 | 0.2838 | 0.5311 | 0.274 | 0.1446 | 0.2359 | 0.4327 | 0.2853 | 0.4676 | 0.49 | 0.3005 | 0.444 | 0.6873 | 0.5439 | 0.6905 | 0.2054 | 0.457 | 0.2177 | 0.4487 | 0.1542 | 0.4046 | 0.2976 | 0.4493 | | 0.8495 | 18.0 | 1917 | 1.0866 | 0.2755 | 0.5134 | 0.2554 | 0.1362 | 0.2355 | 0.4541 | 0.2681 | 0.4548 | 0.4799 | 0.2886 | 0.4362 | 0.6823 | 0.5533 | 0.6995 | 0.1514 | 0.4418 | 0.2165 | 0.4598 | 0.159 | 0.3492 | 0.2974 | 0.4493 | | 0.8421 | 18.9953 | 2023 | 1.0856 | 0.2719 | 0.5259 | 0.2519 | 0.1366 | 0.2341 | 0.4521 | 0.28 | 0.4539 | 0.4769 | 0.3123 | 0.4265 | 0.6567 | 0.5057 | 0.6459 | 0.1693 | 0.4418 | 0.2233 | 0.4629 | 0.1574 | 0.3677 | 0.304 | 0.4662 | | 0.813 | 20.0 | 2130 | 1.0726 | 0.2846 | 0.5348 | 0.2736 | 0.1497 | 0.2304 | 0.4655 | 0.2851 | 0.4696 | 0.4932 | 0.3084 | 0.4391 | 0.6959 | 0.5259 | 0.6586 | 0.1864 | 0.5051 | 0.2468 | 0.4406 | 0.1622 | 0.3969 | 0.3018 | 0.4649 | | 0.8215 | 20.9953 | 2236 | 1.0397 | 0.2966 | 0.5453 | 0.2992 | 0.1696 | 0.2338 | 0.4806 | 0.2887 | 0.4835 | 0.5056 | 0.3419 | 0.4607 | 0.694 | 0.5695 | 0.6973 | 0.1967 | 0.5051 | 0.2348 | 0.4647 | 0.1719 | 0.3815 | 0.3103 | 0.4796 | | 0.7876 | 22.0 | 2343 | 1.0477 | 0.2908 | 0.5476 | 0.2759 | 0.1466 | 0.2439 | 0.4577 | 0.287 | 0.4747 | 0.5011 | 0.3174 | 0.4614 | 0.6895 | 0.5722 | 0.6959 | 0.1964 | 0.4848 | 0.2358 | 0.4656 | 0.1462 | 0.3892 | 0.3034 | 0.4698 | | 0.8012 | 22.9953 | 2449 | 1.0780 | 0.2829 | 0.5255 | 0.2685 | 0.137 | 0.2255 | 0.4389 | 0.2786 | 0.4624 | 0.4898 | 0.2655 | 0.44 | 0.689 | 0.5429 | 0.6676 | 0.2043 | 0.4772 | 0.2418 | 0.4616 | 0.1156 | 0.3646 | 0.3101 | 0.4782 | | 0.7738 | 24.0 | 2556 | 1.0447 | 0.3086 | 0.5717 | 0.296 | 0.18 | 0.2464 | 0.4829 | 0.286 | 0.4777 | 0.5067 | 0.3497 | 0.4605 | 0.6955 | 0.5613 | 0.6815 | 0.2118 | 0.4797 | 0.2567 | 0.4679 | 0.1977 | 0.4123 | 0.3156 | 0.492 | | 0.7738 | 24.9953 | 2662 | 1.0471 | 0.3099 | 0.5827 | 0.2977 | 0.1699 | 0.2539 | 0.4816 | 0.2908 | 0.465 | 0.4846 | 0.3136 | 0.4363 | 0.6683 | 0.5595 | 0.6761 | 0.2114 | 0.4468 | 0.2579 | 0.4571 | 0.2039 | 0.3846 | 0.3166 | 0.4582 | | 0.7549 | 26.0 | 2769 | 1.0618 | 0.3113 | 0.5754 | 0.289 | 0.1962 | 0.2495 | 0.4786 | 0.2938 | 0.4705 | 0.4954 | 0.2923 | 0.4491 | 0.6872 | 0.5627 | 0.6802 | 0.2508 | 0.4899 | 0.2579 | 0.45 | 0.1777 | 0.3954 | 0.3074 | 0.4613 | | 0.7715 | 26.9953 | 2875 | 1.0328 | 0.313 | 0.5867 | 0.2898 | 0.1915 | 0.2419 | 0.4874 | 0.2935 | 0.4789 | 0.5051 | 0.3092 | 0.4417 | 0.7034 | 0.5449 | 0.6815 | 0.2329 | 0.4861 | 0.26 | 0.4741 | 0.1915 | 0.4 | 0.3356 | 0.484 | | 0.7386 | 28.0 | 2982 | 1.0318 | 0.3182 | 0.5894 | 0.2969 | 0.2087 | 0.2584 | 0.4936 | 0.3043 | 0.4905 | 0.5203 | 0.3489 | 0.463 | 0.6977 | 0.5581 | 0.6761 | 0.2404 | 0.5 | 0.2663 | 0.4871 | 0.1996 | 0.4431 | 0.3264 | 0.4951 | | 0.7388 | 28.9953 | 3088 | 1.0309 | 0.3151 | 0.5908 | 0.2953 | 0.1714 | 0.2506 | 0.4845 | 0.299 | 0.4833 | 0.507 | 0.3136 | 0.4511 | 0.7004 | 0.5507 | 0.6874 | 0.2508 | 0.4937 | 0.2607 | 0.4696 | 0.191 | 0.4138 | 0.3224 | 0.4707 | | 0.7092 | 30.0 | 3195 | 1.0388 | 0.3165 | 0.5937 | 0.2909 | 0.1496 | 0.2446 | 0.5126 | 0.2976 | 0.4785 | 0.5024 | 0.2835 | 0.4463 | 0.6904 | 0.5549 | 0.6838 | 0.2498 | 0.4962 | 0.2718 | 0.467 | 0.2004 | 0.4062 | 0.3053 | 0.4591 | | 0.7114 | 30.9953 | 3301 | 1.0427 | 0.3102 | 0.5886 | 0.286 | 0.1976 | 0.2561 | 0.4823 | 0.2985 | 0.4676 | 0.4946 | 0.2994 | 0.445 | 0.7112 | 0.5535 | 0.6806 | 0.2505 | 0.4975 | 0.2679 | 0.4621 | 0.167 | 0.3769 | 0.3123 | 0.456 | | 0.7032 | 32.0 | 3408 | 1.0404 | 0.3168 | 0.5916 | 0.3 | 0.2018 | 0.2554 | 0.5038 | 0.2933 | 0.4776 | 0.5102 | 0.3669 | 0.4527 | 0.7007 | 0.5593 | 0.6887 | 0.244 | 0.4823 | 0.2684 | 0.4853 | 0.1801 | 0.4123 | 0.332 | 0.4822 | | 0.7006 | 32.9953 | 3514 | 1.0246 | 0.3287 | 0.5948 | 0.3158 | 0.21 | 0.2597 | 0.5103 | 0.304 | 0.4967 | 0.522 | 0.3417 | 0.4715 | 0.7227 | 0.5832 | 0.7014 | 0.2496 | 0.519 | 0.2767 | 0.475 | 0.1936 | 0.4308 | 0.3405 | 0.484 | | 0.6886 | 34.0 | 3621 | 1.0414 | 0.3186 | 0.5938 | 0.2917 | 0.1831 | 0.2501 | 0.5168 | 0.2984 | 0.4753 | 0.5011 | 0.2782 | 0.4546 | 0.689 | 0.5766 | 0.7086 | 0.2695 | 0.5089 | 0.2707 | 0.4629 | 0.1621 | 0.3738 | 0.3141 | 0.4511 | | 0.7005 | 34.9953 | 3727 | 1.0315 | 0.3403 | 0.6141 | 0.3297 | 0.1953 | 0.264 | 0.5513 | 0.3007 | 0.4799 | 0.5053 | 0.2818 | 0.4534 | 0.7013 | 0.5757 | 0.6896 | 0.3081 | 0.5063 | 0.268 | 0.4621 | 0.2108 | 0.3908 | 0.339 | 0.4778 | | 0.6736 | 36.0 | 3834 | 1.0174 | 0.3302 | 0.5993 | 0.3115 | 0.2041 | 0.263 | 0.5133 | 0.3003 | 0.4861 | 0.5088 | 0.2864 | 0.4674 | 0.69 | 0.5769 | 0.6973 | 0.2593 | 0.4684 | 0.278 | 0.492 | 0.2139 | 0.4231 | 0.323 | 0.4636 | | 0.6713 | 36.9953 | 3940 | 1.0260 | 0.3312 | 0.5975 | 0.3118 | 0.2167 | 0.2652 | 0.5174 | 0.3112 | 0.4891 | 0.5127 | 0.3276 | 0.4565 | 0.7097 | 0.5684 | 0.6914 | 0.2784 | 0.5063 | 0.2671 | 0.4603 | 0.2226 | 0.4354 | 0.3192 | 0.4702 | | 0.6572 | 38.0 | 4047 | 1.0167 | 0.3431 | 0.6228 | 0.3377 | 0.2027 | 0.2728 | 0.5576 | 0.3079 | 0.4877 | 0.5142 | 0.3312 | 0.4538 | 0.7063 | 0.5705 | 0.6811 | 0.2904 | 0.5063 | 0.2847 | 0.4817 | 0.2318 | 0.4169 | 0.3381 | 0.4849 | | 0.6608 | 38.9953 | 4153 | 1.0307 | 0.3428 | 0.6126 | 0.3359 | 0.21 | 0.2775 | 0.5477 | 0.3043 | 0.4884 | 0.515 | 0.314 | 0.4668 | 0.7124 | 0.5706 | 0.691 | 0.3056 | 0.5367 | 0.2919 | 0.479 | 0.2123 | 0.3923 | 0.3336 | 0.476 | | 0.63 | 40.0 | 4260 | 1.0264 | 0.3443 | 0.6177 | 0.3276 | 0.2114 | 0.2806 | 0.5421 | 0.3121 | 0.4916 | 0.5202 | 0.3188 | 0.4705 | 0.7166 | 0.5783 | 0.691 | 0.3117 | 0.5291 | 0.2732 | 0.4817 | 0.2299 | 0.4308 | 0.3282 | 0.4684 | | 0.6396 | 40.9953 | 4366 | 1.0215 | 0.3425 | 0.621 | 0.3171 | 0.2082 | 0.2728 | 0.5362 | 0.3092 | 0.4871 | 0.5079 | 0.2951 | 0.4554 | 0.7131 | 0.5877 | 0.7027 | 0.3052 | 0.4937 | 0.288 | 0.475 | 0.1991 | 0.4 | 0.3324 | 0.468 | | 0.625 | 42.0 | 4473 | 1.0178 | 0.3484 | 0.6218 | 0.3461 | 0.2131 | 0.2829 | 0.5369 | 0.3012 | 0.4949 | 0.5168 | 0.3244 | 0.4631 | 0.7096 | 0.5735 | 0.6932 | 0.3088 | 0.5127 | 0.2923 | 0.479 | 0.2422 | 0.4246 | 0.3251 | 0.4742 | | 0.6251 | 42.9953 | 4579 | 1.0301 | 0.3461 | 0.6247 | 0.3439 | 0.2131 | 0.2702 | 0.5491 | 0.306 | 0.4934 | 0.5121 | 0.2848 | 0.4461 | 0.7165 | 0.5833 | 0.6901 | 0.301 | 0.5051 | 0.2873 | 0.4835 | 0.2335 | 0.4123 | 0.3255 | 0.4693 | | 0.6135 | 44.0 | 4686 | 1.0139 | 0.3493 | 0.6317 | 0.3372 | 0.2248 | 0.2862 | 0.5572 | 0.3072 | 0.4894 | 0.5117 | 0.3177 | 0.4626 | 0.7082 | 0.5812 | 0.6914 | 0.3109 | 0.5152 | 0.2954 | 0.4839 | 0.223 | 0.3846 | 0.3361 | 0.4836 | | 0.6144 | 44.9953 | 4792 | 1.0285 | 0.3509 | 0.6356 | 0.3476 | 0.224 | 0.2788 | 0.5406 | 0.3077 | 0.4964 | 0.5158 | 0.3294 | 0.4636 | 0.7093 | 0.5641 | 0.6739 | 0.3102 | 0.5013 | 0.2928 | 0.4839 | 0.2503 | 0.4338 | 0.3372 | 0.4862 | | 0.6108 | 46.0 | 4899 | 1.0337 | 0.3399 | 0.6192 | 0.3317 | 0.2009 | 0.2756 | 0.5243 | 0.2977 | 0.4824 | 0.503 | 0.2895 | 0.4587 | 0.6878 | 0.5765 | 0.6788 | 0.3125 | 0.4975 | 0.2873 | 0.4719 | 0.2004 | 0.3923 | 0.323 | 0.4747 | | 0.6081 | 46.9953 | 5005 | 1.0228 | 0.3444 | 0.6169 | 0.341 | 0.2173 | 0.281 | 0.5443 | 0.3041 | 0.4928 | 0.5181 | 0.3604 | 0.462 | 0.7082 | 0.5721 | 0.6887 | 0.313 | 0.5038 | 0.2941 | 0.4754 | 0.2061 | 0.4292 | 0.3367 | 0.4933 | | 0.5857 | 48.0 | 5112 | 1.0137 | 0.348 | 0.6196 | 0.3419 | 0.2091 | 0.2733 | 0.5542 | 0.308 | 0.4916 | 0.5113 | 0.297 | 0.4554 | 0.7015 | 0.5836 | 0.6905 | 0.3218 | 0.5089 | 0.2964 | 0.4848 | 0.214 | 0.3908 | 0.3241 | 0.4813 | | 0.5963 | 48.9953 | 5218 | 1.0341 | 0.3433 | 0.6262 | 0.3411 | 0.218 | 0.2703 | 0.5395 | 0.3019 | 0.4899 | 0.5166 | 0.3304 | 0.4661 | 0.7068 | 0.5829 | 0.6941 | 0.2951 | 0.5013 | 0.2938 | 0.4888 | 0.2044 | 0.4123 | 0.3405 | 0.4862 | | 0.5828 | 50.0 | 5325 | 1.0220 | 0.3402 | 0.6157 | 0.3433 | 0.2175 | 0.2748 | 0.5347 | 0.3054 | 0.496 | 0.5184 | 0.321 | 0.4733 | 0.7146 | 0.5809 | 0.6977 | 0.3071 | 0.4975 | 0.29 | 0.4857 | 0.1992 | 0.4292 | 0.3238 | 0.4818 | | 0.583 | 50.9953 | 5431 | 1.0163 | 0.3483 | 0.6272 | 0.3378 | 0.2054 | 0.2819 | 0.554 | 0.3081 | 0.4954 | 0.5174 | 0.3028 | 0.4757 | 0.701 | 0.5748 | 0.6896 | 0.3137 | 0.5165 | 0.2908 | 0.4866 | 0.234 | 0.4185 | 0.3284 | 0.476 | | 0.5574 | 52.0 | 5538 | 1.0144 | 0.3462 | 0.6241 | 0.3344 | 0.2138 | 0.2757 | 0.5384 | 0.3036 | 0.4917 | 0.5163 | 0.3087 | 0.4663 | 0.7117 | 0.5794 | 0.6941 | 0.2983 | 0.4987 | 0.3086 | 0.4853 | 0.222 | 0.4385 | 0.3225 | 0.4649 | | 0.565 | 52.9953 | 5644 | 1.0384 | 0.3361 | 0.6159 | 0.3305 | 0.2078 | 0.2691 | 0.5349 | 0.3004 | 0.4874 | 0.511 | 0.3151 | 0.4652 | 0.7071 | 0.5649 | 0.6851 | 0.2748 | 0.4886 | 0.2937 | 0.4888 | 0.222 | 0.42 | 0.325 | 0.4724 | | 0.5545 | 54.0 | 5751 | 1.0353 | 0.338 | 0.6151 | 0.3341 | 0.2055 | 0.2616 | 0.5487 | 0.3045 | 0.4796 | 0.5039 | 0.334 | 0.4435 | 0.7153 | 0.5729 | 0.6779 | 0.2751 | 0.4797 | 0.2887 | 0.4714 | 0.2189 | 0.4092 | 0.3342 | 0.4813 | | 0.5533 | 54.9953 | 5857 | 1.0273 | 0.3478 | 0.6322 | 0.3282 | 0.2019 | 0.2765 | 0.5498 | 0.3091 | 0.4827 | 0.5049 | 0.289 | 0.4568 | 0.7001 | 0.5784 | 0.6815 | 0.3102 | 0.4975 | 0.2974 | 0.4799 | 0.2306 | 0.4031 | 0.3223 | 0.4627 | | 0.5343 | 56.0 | 5964 | 1.0379 | 0.3489 | 0.631 | 0.3353 | 0.2161 | 0.2711 | 0.5601 | 0.3114 | 0.4885 | 0.5098 | 0.3114 | 0.4584 | 0.721 | 0.5659 | 0.6824 | 0.3237 | 0.5051 | 0.3084 | 0.4853 | 0.2272 | 0.4092 | 0.3193 | 0.4671 | | 0.5468 | 56.9953 | 6070 | 1.0393 | 0.3454 | 0.6401 | 0.3253 | 0.2075 | 0.2698 | 0.5397 | 0.3079 | 0.4848 | 0.5033 | 0.3134 | 0.4366 | 0.686 | 0.5642 | 0.668 | 0.3186 | 0.4797 | 0.2974 | 0.4915 | 0.2271 | 0.4092 | 0.3196 | 0.468 | | 0.5431 | 58.0 | 6177 | 1.0278 | 0.344 | 0.63 | 0.3249 | 0.2067 | 0.2747 | 0.5383 | 0.3093 | 0.4896 | 0.5083 | 0.2966 | 0.4465 | 0.7067 | 0.5634 | 0.6671 | 0.3113 | 0.4962 | 0.2989 | 0.4902 | 0.2229 | 0.4138 | 0.3235 | 0.4742 | | 0.5339 | 58.9953 | 6283 | 1.0319 | 0.3408 | 0.6341 | 0.3086 | 0.214 | 0.2713 | 0.5379 | 0.3031 | 0.4853 | 0.505 | 0.2896 | 0.4521 | 0.7106 | 0.5694 | 0.6815 | 0.2998 | 0.4709 | 0.3092 | 0.4978 | 0.2052 | 0.4092 | 0.3205 | 0.4658 | | 0.5334 | 60.0 | 6390 | 1.0279 | 0.3496 | 0.6412 | 0.3359 | 0.2139 | 0.2859 | 0.5352 | 0.3089 | 0.4929 | 0.5133 | 0.3019 | 0.4679 | 0.7023 | 0.5635 | 0.6802 | 0.3222 | 0.5 | 0.3064 | 0.4879 | 0.2309 | 0.4277 | 0.3249 | 0.4707 | | 0.5289 | 60.9953 | 6496 | 1.0478 | 0.3506 | 0.6433 | 0.321 | 0.2228 | 0.2723 | 0.5336 | 0.304 | 0.4892 | 0.5068 | 0.297 | 0.4413 | 0.693 | 0.5761 | 0.6838 | 0.3275 | 0.4873 | 0.303 | 0.4696 | 0.2246 | 0.4308 | 0.3221 | 0.4622 | | 0.5091 | 62.0 | 6603 | 1.0321 | 0.3484 | 0.6408 | 0.3242 | 0.2247 | 0.2711 | 0.5639 | 0.309 | 0.4896 | 0.5119 | 0.3139 | 0.4471 | 0.7109 | 0.5561 | 0.6779 | 0.3219 | 0.4848 | 0.297 | 0.4951 | 0.245 | 0.4308 | 0.3222 | 0.4707 | | 0.5239 | 62.9953 | 6709 | 1.0277 | 0.3516 | 0.6421 | 0.3371 | 0.2329 | 0.2764 | 0.5504 | 0.3045 | 0.491 | 0.5096 | 0.3178 | 0.4378 | 0.7055 | 0.5655 | 0.6766 | 0.3156 | 0.4646 | 0.3137 | 0.4973 | 0.2267 | 0.44 | 0.3365 | 0.4693 | | 0.5025 | 64.0 | 6816 | 1.0331 | 0.3474 | 0.6385 | 0.3144 | 0.2295 | 0.286 | 0.5378 | 0.3095 | 0.4949 | 0.5131 | 0.3183 | 0.4571 | 0.7047 | 0.5603 | 0.6878 | 0.3093 | 0.4722 | 0.3043 | 0.4866 | 0.235 | 0.4538 | 0.328 | 0.4649 | | 0.4991 | 64.9953 | 6922 | 1.0323 | 0.3457 | 0.6359 | 0.3158 | 0.2193 | 0.2808 | 0.5367 | 0.3091 | 0.485 | 0.507 | 0.2986 | 0.4511 | 0.7043 | 0.5653 | 0.6838 | 0.3106 | 0.481 | 0.2987 | 0.4804 | 0.2308 | 0.4169 | 0.3229 | 0.4729 | | 0.4969 | 66.0 | 7029 | 1.0250 | 0.3448 | 0.6468 | 0.317 | 0.23 | 0.2781 | 0.5449 | 0.3031 | 0.4919 | 0.5109 | 0.3493 | 0.4479 | 0.6951 | 0.5477 | 0.6707 | 0.311 | 0.4911 | 0.3046 | 0.4924 | 0.2424 | 0.4369 | 0.318 | 0.4631 | | 0.496 | 66.9953 | 7135 | 1.0300 | 0.3461 | 0.6272 | 0.325 | 0.2262 | 0.2772 | 0.5318 | 0.3097 | 0.4913 | 0.5118 | 0.3102 | 0.4598 | 0.6967 | 0.5751 | 0.6941 | 0.3067 | 0.4696 | 0.3018 | 0.4812 | 0.2244 | 0.4415 | 0.3224 | 0.4724 | | 0.4788 | 68.0 | 7242 | 1.0452 | 0.3402 | 0.6215 | 0.3124 | 0.2249 | 0.2665 | 0.5439 | 0.3048 | 0.4923 | 0.5102 | 0.338 | 0.4531 | 0.7045 | 0.5618 | 0.6806 | 0.3051 | 0.4797 | 0.2956 | 0.4741 | 0.2261 | 0.4492 | 0.3127 | 0.4671 | | 0.4807 | 68.9953 | 7348 | 1.0359 | 0.3461 | 0.6413 | 0.3141 | 0.2239 | 0.2736 | 0.5437 | 0.3079 | 0.4836 | 0.5028 | 0.3072 | 0.4486 | 0.6881 | 0.5605 | 0.6685 | 0.3124 | 0.4696 | 0.3014 | 0.4808 | 0.2347 | 0.4231 | 0.3216 | 0.472 | | 0.4756 | 70.0 | 7455 | 1.0310 | 0.3522 | 0.6459 | 0.3281 | 0.222 | 0.2816 | 0.5535 | 0.3078 | 0.4878 | 0.5082 | 0.2998 | 0.4607 | 0.7001 | 0.5784 | 0.6919 | 0.3348 | 0.4823 | 0.2935 | 0.4763 | 0.2294 | 0.4169 | 0.325 | 0.4738 | | 0.4738 | 70.9953 | 7561 | 1.0440 | 0.3422 | 0.6271 | 0.3121 | 0.2157 | 0.2757 | 0.5429 | 0.31 | 0.483 | 0.5072 | 0.3103 | 0.4482 | 0.6956 | 0.5563 | 0.6703 | 0.3091 | 0.4835 | 0.2995 | 0.4848 | 0.225 | 0.4277 | 0.3208 | 0.4698 | | 0.4674 | 72.0 | 7668 | 1.0492 | 0.3474 | 0.6321 | 0.3275 | 0.2241 | 0.2692 | 0.548 | 0.314 | 0.4834 | 0.5043 | 0.2946 | 0.4371 | 0.707 | 0.5626 | 0.6703 | 0.3187 | 0.4975 | 0.295 | 0.4754 | 0.2413 | 0.4123 | 0.3192 | 0.4658 | | 0.4592 | 72.9953 | 7774 | 1.0344 | 0.3545 | 0.6457 | 0.3418 | 0.2373 | 0.2789 | 0.559 | 0.314 | 0.4912 | 0.5084 | 0.3124 | 0.4394 | 0.6999 | 0.5673 | 0.6766 | 0.3326 | 0.4949 | 0.3024 | 0.4728 | 0.2468 | 0.4277 | 0.3234 | 0.4702 | | 0.4584 | 74.0 | 7881 | 1.0381 | 0.3451 | 0.6271 | 0.3398 | 0.2388 | 0.2702 | 0.5483 | 0.3144 | 0.4882 | 0.5113 | 0.3169 | 0.4468 | 0.7077 | 0.5631 | 0.6788 | 0.3154 | 0.4873 | 0.2912 | 0.4812 | 0.2251 | 0.4323 | 0.3307 | 0.4769 | | 0.4648 | 74.9953 | 7987 | 1.0515 | 0.3506 | 0.6384 | 0.3369 | 0.2269 | 0.2659 | 0.5582 | 0.3168 | 0.4842 | 0.5067 | 0.3154 | 0.4328 | 0.6958 | 0.5646 | 0.6694 | 0.3313 | 0.4886 | 0.2944 | 0.4741 | 0.2264 | 0.4262 | 0.3365 | 0.4751 | | 0.4487 | 76.0 | 8094 | 1.0408 | 0.3479 | 0.6399 | 0.3232 | 0.2223 | 0.2783 | 0.5555 | 0.3149 | 0.486 | 0.5077 | 0.322 | 0.4466 | 0.699 | 0.5573 | 0.6671 | 0.3233 | 0.4924 | 0.2944 | 0.4795 | 0.2408 | 0.4369 | 0.3235 | 0.4627 | | 0.4482 | 76.9953 | 8200 | 1.0368 | 0.3505 | 0.6396 | 0.3153 | 0.2229 | 0.2796 | 0.5552 | 0.3173 | 0.487 | 0.508 | 0.3088 | 0.4459 | 0.7042 | 0.5604 | 0.6716 | 0.3201 | 0.4911 | 0.2959 | 0.4786 | 0.2532 | 0.4292 | 0.323 | 0.4693 | | 0.4359 | 78.0 | 8307 | 1.0456 | 0.3488 | 0.6434 | 0.3181 | 0.2308 | 0.2708 | 0.549 | 0.3117 | 0.4823 | 0.5029 | 0.3098 | 0.4397 | 0.6933 | 0.5704 | 0.6716 | 0.3174 | 0.4823 | 0.2983 | 0.4777 | 0.2353 | 0.4185 | 0.3227 | 0.4644 | | 0.445 | 78.9953 | 8413 | 1.0598 | 0.3472 | 0.638 | 0.3149 | 0.2236 | 0.2719 | 0.5492 | 0.313 | 0.4873 | 0.5051 | 0.2966 | 0.4461 | 0.7045 | 0.5497 | 0.6649 | 0.3331 | 0.5013 | 0.2907 | 0.4737 | 0.2397 | 0.4231 | 0.3226 | 0.4627 | | 0.4328 | 80.0 | 8520 | 1.0558 | 0.3477 | 0.6419 | 0.3326 | 0.2335 | 0.2701 | 0.5483 | 0.3112 | 0.4865 | 0.506 | 0.3232 | 0.432 | 0.7048 | 0.5599 | 0.668 | 0.3244 | 0.4823 | 0.292 | 0.4772 | 0.2396 | 0.4369 | 0.3228 | 0.4658 | | 0.4321 | 80.9953 | 8626 | 1.0447 | 0.3474 | 0.6387 | 0.3308 | 0.2348 | 0.2749 | 0.5457 | 0.3155 | 0.485 | 0.506 | 0.3189 | 0.4442 | 0.6897 | 0.5604 | 0.6689 | 0.3165 | 0.4785 | 0.2932 | 0.4777 | 0.2362 | 0.4338 | 0.3307 | 0.4711 | | 0.4318 | 82.0 | 8733 | 1.0476 | 0.3496 | 0.6408 | 0.3292 | 0.2301 | 0.2767 | 0.543 | 0.3118 | 0.4895 | 0.51 | 0.3248 | 0.4397 | 0.6942 | 0.5553 | 0.6734 | 0.3188 | 0.4797 | 0.2965 | 0.479 | 0.2481 | 0.4462 | 0.3296 | 0.4716 | | 0.4273 | 82.9953 | 8839 | 1.0585 | 0.348 | 0.6396 | 0.3286 | 0.2309 | 0.2694 | 0.5526 | 0.3126 | 0.4843 | 0.5035 | 0.318 | 0.4372 | 0.6912 | 0.5532 | 0.6622 | 0.3262 | 0.4861 | 0.2999 | 0.4763 | 0.2363 | 0.4246 | 0.3245 | 0.4684 | | 0.4252 | 84.0 | 8946 | 1.0618 | 0.3439 | 0.6332 | 0.3285 | 0.2312 | 0.272 | 0.5316 | 0.3148 | 0.4854 | 0.5036 | 0.3173 | 0.4412 | 0.6932 | 0.5538 | 0.6626 | 0.3153 | 0.4848 | 0.2879 | 0.4723 | 0.2455 | 0.4431 | 0.3168 | 0.4551 | | 0.4158 | 84.9953 | 9052 | 1.0631 | 0.3484 | 0.6387 | 0.3257 | 0.2285 | 0.2694 | 0.5474 | 0.3152 | 0.4841 | 0.5047 | 0.3141 | 0.4346 | 0.6906 | 0.5595 | 0.6671 | 0.3351 | 0.4823 | 0.287 | 0.4732 | 0.2374 | 0.4354 | 0.323 | 0.4653 | | 0.4088 | 86.0 | 9159 | 1.0561 | 0.3525 | 0.6421 | 0.3247 | 0.2286 | 0.2768 | 0.553 | 0.3143 | 0.4856 | 0.5067 | 0.311 | 0.4405 | 0.7017 | 0.5626 | 0.6694 | 0.3299 | 0.4861 | 0.2942 | 0.4719 | 0.249 | 0.4446 | 0.3266 | 0.4618 | | 0.4194 | 86.9953 | 9265 | 1.0613 | 0.3499 | 0.6399 | 0.3269 | 0.227 | 0.2727 | 0.5463 | 0.3132 | 0.4821 | 0.5012 | 0.3048 | 0.4328 | 0.6896 | 0.562 | 0.664 | 0.3359 | 0.4911 | 0.2915 | 0.4638 | 0.2386 | 0.4246 | 0.3216 | 0.4627 | | 0.4087 | 88.0 | 9372 | 1.0764 | 0.3484 | 0.6358 | 0.3282 | 0.222 | 0.2707 | 0.556 | 0.3145 | 0.4806 | 0.4996 | 0.2991 | 0.4302 | 0.6907 | 0.5541 | 0.6599 | 0.3344 | 0.4848 | 0.2866 | 0.4701 | 0.2455 | 0.4185 | 0.3215 | 0.4649 | | 0.4031 | 88.9953 | 9478 | 1.0731 | 0.3494 | 0.6316 | 0.3283 | 0.2258 | 0.2685 | 0.5512 | 0.3144 | 0.4852 | 0.5048 | 0.3054 | 0.4275 | 0.7027 | 0.5574 | 0.6617 | 0.3338 | 0.4911 | 0.2962 | 0.4679 | 0.2356 | 0.4354 | 0.3238 | 0.468 | | 0.3968 | 90.0 | 9585 | 1.0613 | 0.35 | 0.6386 | 0.3379 | 0.2254 | 0.2739 | 0.5538 | 0.3105 | 0.4855 | 0.5043 | 0.3148 | 0.4376 | 0.6953 | 0.559 | 0.6721 | 0.335 | 0.4835 | 0.292 | 0.4679 | 0.2386 | 0.4323 | 0.3253 | 0.4658 | | 0.398 | 90.9953 | 9691 | 1.0688 | 0.345 | 0.6347 | 0.3302 | 0.2158 | 0.2666 | 0.5543 | 0.3107 | 0.4786 | 0.4958 | 0.2918 | 0.4253 | 0.6988 | 0.5566 | 0.6622 | 0.3228 | 0.4772 | 0.2946 | 0.4643 | 0.2317 | 0.4154 | 0.319 | 0.46 | | 0.3964 | 92.0 | 9798 | 1.0728 | 0.3467 | 0.6344 | 0.3302 | 0.2195 | 0.265 | 0.5541 | 0.313 | 0.4814 | 0.5 | 0.2978 | 0.4227 | 0.7024 | 0.5625 | 0.6703 | 0.3321 | 0.4873 | 0.2908 | 0.458 | 0.2296 | 0.4246 | 0.3187 | 0.4596 | | 0.3947 | 92.9953 | 9904 | 1.0710 | 0.3458 | 0.6355 | 0.3264 | 0.2211 | 0.267 | 0.5534 | 0.3141 | 0.4837 | 0.5022 | 0.3055 | 0.4276 | 0.7005 | 0.5555 | 0.6667 | 0.3352 | 0.4899 | 0.2911 | 0.4616 | 0.2274 | 0.4292 | 0.3198 | 0.4636 | | 0.3903 | 94.0 | 10011 | 1.0691 | 0.3465 | 0.6375 | 0.3308 | 0.227 | 0.2659 | 0.5562 | 0.3099 | 0.4822 | 0.502 | 0.3012 | 0.431 | 0.7001 | 0.5563 | 0.6626 | 0.3354 | 0.4911 | 0.2883 | 0.4643 | 0.229 | 0.4231 | 0.3234 | 0.4689 | | 0.3943 | 94.9953 | 10117 | 1.0708 | 0.3489 | 0.6366 | 0.3274 | 0.2278 | 0.2647 | 0.5588 | 0.3154 | 0.4838 | 0.502 | 0.3023 | 0.4258 | 0.704 | 0.5611 | 0.6653 | 0.3382 | 0.4873 | 0.2922 | 0.4643 | 0.2312 | 0.4292 | 0.3216 | 0.464 | | 0.3855 | 96.0 | 10224 | 1.0744 | 0.349 | 0.6353 | 0.3315 | 0.2215 | 0.2689 | 0.5566 | 0.318 | 0.4813 | 0.5011 | 0.2983 | 0.431 | 0.6985 | 0.5574 | 0.664 | 0.3432 | 0.4848 | 0.2908 | 0.4621 | 0.231 | 0.4292 | 0.3224 | 0.4653 | | 0.3833 | 96.9953 | 10330 | 1.0692 | 0.3474 | 0.6341 | 0.3282 | 0.2244 | 0.2693 | 0.5567 | 0.3167 | 0.4833 | 0.5038 | 0.3031 | 0.4318 | 0.7037 | 0.5553 | 0.6649 | 0.3348 | 0.4861 | 0.2918 | 0.4652 | 0.2323 | 0.4385 | 0.3227 | 0.4644 | | 0.3838 | 98.0 | 10437 | 1.0700 | 0.3464 | 0.6366 | 0.3231 | 0.2234 | 0.2692 | 0.554 | 0.3155 | 0.4842 | 0.5026 | 0.3011 | 0.4319 | 0.702 | 0.5559 | 0.6658 | 0.3326 | 0.4873 | 0.2878 | 0.4594 | 0.232 | 0.4323 | 0.3239 | 0.4684 | | 0.385 | 98.9953 | 10543 | 1.0722 | 0.3471 | 0.6369 | 0.3259 | 0.2192 | 0.2683 | 0.5535 | 0.3142 | 0.4823 | 0.5023 | 0.2976 | 0.4298 | 0.7033 | 0.5569 | 0.6676 | 0.3329 | 0.4823 | 0.2887 | 0.4612 | 0.2342 | 0.4354 | 0.3225 | 0.4649 | | 0.3644 | 99.5305 | 10600 | 1.0727 | 0.3471 | 0.6376 | 0.3269 | 0.2239 | 0.2687 | 0.5542 | 0.3146 | 0.485 | 0.5045 | 0.3033 | 0.4315 | 0.7048 | 0.556 | 0.6671 | 0.3346 | 0.4873 | 0.2879 | 0.4625 | 0.2348 | 0.44 | 0.3221 | 0.4653 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1

# tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]

qubvel-hf/sensetime-deformable-detr-finetuned-10k-cppe5-more-augs

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/tuning-sota-cppe5/runs/2jpwvl0x) # sensetime-deformable-detr-finetuned-10k-cppe5-more-augs This model is a fine-tuned version of [SenseTime/deformable-detr](https://huggingface.co/SenseTime/deformable-detr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9911 - Map: 0.3714 - Map 50: 0.6742 - Map 75: 0.3545 - Map Small: 0.226 - Map Medium: 0.2836 - Map Large: 0.5849 - Mar 1: 0.3191 - Mar 10: 0.502 - Mar 100: 0.5266 - Mar Small: 0.3445 - Mar Medium: 0.4443 - Mar Large: 0.7237 - Map Coverall: 0.5834 - Mar 100 Coverall: 0.6797 - Map Face Shield: 0.3648 - Mar 100 Face Shield: 0.5241 - Map Gloves: 0.3122 - Mar 100 Gloves: 0.5071 - Map Goggles: 0.2315 - Mar 100 Goggles: 0.4338 - Map Mask: 0.3649 - Mar 100 Mask: 0.4884 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 1337 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 8.5293 | 0.9953 | 106 | 1.7296 | 0.0265 | 0.0666 | 0.0164 | 0.0103 | 0.0138 | 0.0516 | 0.0559 | 0.1869 | 0.2518 | 0.0592 | 0.2249 | 0.3316 | 0.0774 | 0.514 | 0.0017 | 0.1671 | 0.0136 | 0.2487 | 0.0006 | 0.0815 | 0.0392 | 0.2476 | | 1.5394 | 2.0 | 213 | 1.5095 | 0.0359 | 0.0836 | 0.0262 | 0.0293 | 0.0264 | 0.0769 | 0.0626 | 0.2334 | 0.2933 | 0.1525 | 0.2717 | 0.3217 | 0.0785 | 0.555 | 0.0028 | 0.1886 | 0.0201 | 0.2732 | 0.0011 | 0.0908 | 0.0769 | 0.3591 | | 1.3761 | 2.9953 | 319 | 1.5410 | 0.0604 | 0.123 | 0.0555 | 0.0256 | 0.036 | 0.0604 | 0.0966 | 0.2526 | 0.2998 | 0.0803 | 0.258 | 0.4789 | 0.1865 | 0.5874 | 0.0016 | 0.1177 | 0.0185 | 0.2812 | 0.0026 | 0.1569 | 0.0928 | 0.3556 | | 1.2615 | 4.0 | 426 | 1.3671 | 0.0888 | 0.1756 | 0.0873 | 0.043 | 0.058 | 0.1298 | 0.1317 | 0.3164 | 0.3635 | 0.1763 | 0.3076 | 0.5184 | 0.2402 | 0.6176 | 0.0094 | 0.2759 | 0.0577 | 0.3406 | 0.0097 | 0.2031 | 0.1273 | 0.3804 | | 1.2042 | 4.9953 | 532 | 1.2824 | 0.1069 | 0.2127 | 0.101 | 0.0374 | 0.075 | 0.1509 | 0.1626 | 0.3623 | 0.4001 | 0.1967 | 0.3445 | 0.5716 | 0.2957 | 0.6477 | 0.0247 | 0.3342 | 0.0617 | 0.3442 | 0.0194 | 0.2477 | 0.1331 | 0.4267 | | 1.1568 | 6.0 | 639 | 1.2616 | 0.124 | 0.2451 | 0.117 | 0.0346 | 0.098 | 0.2056 | 0.1652 | 0.3623 | 0.3997 | 0.2053 | 0.3386 | 0.5458 | 0.3583 | 0.6477 | 0.0184 | 0.2823 | 0.0803 | 0.3799 | 0.0102 | 0.2369 | 0.1528 | 0.4516 | | 1.1207 | 6.9953 | 745 | 1.2273 | 0.1506 | 0.291 | 0.1409 | 0.0686 | 0.1205 | 0.224 | 0.1843 | 0.3925 | 0.4294 | 0.252 | 0.385 | 0.5833 | 0.4045 | 0.6541 | 0.0588 | 0.3506 | 0.086 | 0.404 | 0.014 | 0.2738 | 0.1897 | 0.4644 | | 1.0994 | 8.0 | 852 | 1.1945 | 0.1725 | 0.3216 | 0.1674 | 0.076 | 0.1565 | 0.242 | 0.2047 | 0.4149 | 0.4486 | 0.2657 | 0.3955 | 0.6151 | 0.4191 | 0.6739 | 0.0764 | 0.3848 | 0.0904 | 0.3902 | 0.0539 | 0.3338 | 0.2226 | 0.4604 | | 1.0347 | 8.9953 | 958 | 1.1692 | 0.1923 | 0.3626 | 0.1728 | 0.0719 | 0.1544 | 0.2953 | 0.2277 | 0.4381 | 0.4654 | 0.2678 | 0.3933 | 0.6539 | 0.4415 | 0.6595 | 0.0846 | 0.4278 | 0.1251 | 0.4179 | 0.0711 | 0.3492 | 0.2391 | 0.4724 | | 1.0089 | 10.0 | 1065 | 1.1513 | 0.2033 | 0.3793 | 0.1906 | 0.0771 | 0.1521 | 0.317 | 0.2301 | 0.4234 | 0.4558 | 0.2662 | 0.3725 | 0.6609 | 0.451 | 0.6441 | 0.1041 | 0.3987 | 0.1202 | 0.4237 | 0.0738 | 0.3231 | 0.2676 | 0.4893 | | 1.0118 | 10.9953 | 1171 | 1.1533 | 0.2141 | 0.408 | 0.1966 | 0.0794 | 0.1793 | 0.3118 | 0.2235 | 0.4187 | 0.458 | 0.2509 | 0.3958 | 0.6438 | 0.4976 | 0.6775 | 0.0865 | 0.4494 | 0.1302 | 0.4045 | 0.0835 | 0.2969 | 0.2726 | 0.4618 | | 0.995 | 12.0 | 1278 | 1.1530 | 0.2198 | 0.4281 | 0.2038 | 0.1102 | 0.168 | 0.3425 | 0.2464 | 0.4322 | 0.4683 | 0.2781 | 0.3951 | 0.6383 | 0.4638 | 0.6595 | 0.1443 | 0.4544 | 0.1525 | 0.4473 | 0.0843 | 0.3123 | 0.2539 | 0.468 | | 0.9732 | 12.9953 | 1384 | 1.1279 | 0.232 | 0.4426 | 0.2192 | 0.0938 | 0.1726 | 0.3843 | 0.25 | 0.448 | 0.4811 | 0.2912 | 0.4102 | 0.6636 | 0.4878 | 0.655 | 0.1329 | 0.4785 | 0.1618 | 0.4272 | 0.09 | 0.3538 | 0.2876 | 0.4911 | | 0.9398 | 14.0 | 1491 | 1.1457 | 0.2351 | 0.4554 | 0.2215 | 0.1128 | 0.174 | 0.4144 | 0.2565 | 0.4372 | 0.4627 | 0.2462 | 0.3896 | 0.688 | 0.5015 | 0.6554 | 0.1352 | 0.5203 | 0.1645 | 0.4165 | 0.1219 | 0.3031 | 0.2522 | 0.4182 | | 0.9281 | 14.9953 | 1597 | 1.1128 | 0.2545 | 0.4835 | 0.2451 | 0.1241 | 0.2078 | 0.3884 | 0.2638 | 0.4494 | 0.4776 | 0.2831 | 0.4274 | 0.6403 | 0.4969 | 0.6563 | 0.1496 | 0.4734 | 0.195 | 0.4353 | 0.1352 | 0.3554 | 0.2958 | 0.4676 | | 0.9162 | 16.0 | 1704 | 1.1145 | 0.2482 | 0.4755 | 0.2398 | 0.1053 | 0.2017 | 0.4362 | 0.2749 | 0.4637 | 0.4867 | 0.2986 | 0.4219 | 0.6875 | 0.4836 | 0.6743 | 0.1557 | 0.4848 | 0.1714 | 0.433 | 0.1453 | 0.3554 | 0.2851 | 0.4858 | | 0.9038 | 16.9953 | 1810 | 1.0968 | 0.2746 | 0.5143 | 0.2687 | 0.167 | 0.2122 | 0.4583 | 0.2825 | 0.4576 | 0.4827 | 0.2696 | 0.4182 | 0.6881 | 0.5159 | 0.6509 | 0.1481 | 0.457 | 0.2179 | 0.4509 | 0.1544 | 0.3631 | 0.3367 | 0.4916 | | 0.8973 | 18.0 | 1917 | 1.0895 | 0.2688 | 0.5085 | 0.2595 | 0.1549 | 0.2178 | 0.4381 | 0.2743 | 0.4561 | 0.481 | 0.2921 | 0.4282 | 0.6551 | 0.5211 | 0.6617 | 0.1517 | 0.4481 | 0.1978 | 0.4384 | 0.1679 | 0.3892 | 0.3053 | 0.4676 | | 0.892 | 18.9953 | 2023 | 1.0987 | 0.2736 | 0.5209 | 0.2565 | 0.1568 | 0.2152 | 0.4343 | 0.2802 | 0.4541 | 0.482 | 0.2782 | 0.4233 | 0.6792 | 0.5088 | 0.6518 | 0.187 | 0.5025 | 0.1949 | 0.4187 | 0.1581 | 0.3692 | 0.3194 | 0.4676 | | 0.8851 | 20.0 | 2130 | 1.0649 | 0.2813 | 0.5321 | 0.2756 | 0.1914 | 0.2223 | 0.4563 | 0.2901 | 0.4698 | 0.4932 | 0.3123 | 0.4281 | 0.6792 | 0.5127 | 0.6532 | 0.17 | 0.4924 | 0.223 | 0.4576 | 0.1749 | 0.3846 | 0.3261 | 0.4782 | | 0.8862 | 20.9953 | 2236 | 1.0438 | 0.2999 | 0.5575 | 0.2865 | 0.1862 | 0.2439 | 0.4748 | 0.2862 | 0.4739 | 0.4955 | 0.2754 | 0.4518 | 0.6711 | 0.558 | 0.6874 | 0.1831 | 0.5 | 0.2399 | 0.4504 | 0.1933 | 0.3723 | 0.3251 | 0.4671 | | 0.8636 | 22.0 | 2343 | 1.0833 | 0.2853 | 0.5355 | 0.2675 | 0.192 | 0.2404 | 0.4267 | 0.271 | 0.4606 | 0.4886 | 0.3272 | 0.4255 | 0.637 | 0.5164 | 0.6748 | 0.204 | 0.4823 | 0.2425 | 0.4688 | 0.1493 | 0.3631 | 0.3145 | 0.4542 | | 0.8638 | 22.9953 | 2449 | 1.0502 | 0.296 | 0.5487 | 0.2823 | 0.1887 | 0.2344 | 0.475 | 0.2926 | 0.4802 | 0.5039 | 0.3273 | 0.4325 | 0.6911 | 0.5345 | 0.6752 | 0.2049 | 0.4949 | 0.2274 | 0.4589 | 0.1935 | 0.4108 | 0.32 | 0.4796 | | 0.8337 | 24.0 | 2556 | 1.0479 | 0.2998 | 0.5571 | 0.2814 | 0.152 | 0.2356 | 0.4876 | 0.2856 | 0.4771 | 0.4998 | 0.2975 | 0.4335 | 0.704 | 0.5409 | 0.6707 | 0.2135 | 0.4633 | 0.2491 | 0.4518 | 0.1759 | 0.4246 | 0.3197 | 0.4884 | | 0.8504 | 24.9953 | 2662 | 1.0265 | 0.3073 | 0.5537 | 0.3079 | 0.1999 | 0.2561 | 0.4489 | 0.2932 | 0.4857 | 0.5159 | 0.3003 | 0.4643 | 0.6932 | 0.5294 | 0.6991 | 0.2215 | 0.5076 | 0.2618 | 0.4674 | 0.1766 | 0.4123 | 0.3472 | 0.4929 | | 0.8299 | 26.0 | 2769 | 1.0412 | 0.308 | 0.5736 | 0.299 | 0.2122 | 0.2355 | 0.4804 | 0.2938 | 0.4854 | 0.5069 | 0.3271 | 0.4385 | 0.6925 | 0.5397 | 0.6712 | 0.2425 | 0.5139 | 0.2551 | 0.4598 | 0.1792 | 0.4046 | 0.3235 | 0.4849 | | 0.8284 | 26.9953 | 2875 | 1.0276 | 0.3105 | 0.5678 | 0.2962 | 0.2051 | 0.2464 | 0.469 | 0.2956 | 0.4851 | 0.513 | 0.3045 | 0.4736 | 0.6767 | 0.5523 | 0.6833 | 0.2282 | 0.5139 | 0.2525 | 0.4679 | 0.185 | 0.3969 | 0.3346 | 0.5031 | | 0.8092 | 28.0 | 2982 | 1.0400 | 0.309 | 0.573 | 0.2986 | 0.1643 | 0.2486 | 0.479 | 0.2934 | 0.4887 | 0.5072 | 0.2957 | 0.4481 | 0.6842 | 0.5404 | 0.6743 | 0.2025 | 0.5063 | 0.2585 | 0.4554 | 0.1976 | 0.4092 | 0.346 | 0.4907 | | 0.8156 | 28.9953 | 3088 | 1.0271 | 0.3208 | 0.5894 | 0.305 | 0.2031 | 0.2619 | 0.4853 | 0.3051 | 0.5006 | 0.5193 | 0.3246 | 0.4605 | 0.7046 | 0.5503 | 0.6833 | 0.2421 | 0.5076 | 0.2555 | 0.467 | 0.2047 | 0.4323 | 0.3513 | 0.5062 | | 0.8037 | 30.0 | 3195 | 1.0355 | 0.3162 | 0.5986 | 0.295 | 0.1994 | 0.2532 | 0.4821 | 0.2986 | 0.4877 | 0.5097 | 0.286 | 0.4508 | 0.6977 | 0.5315 | 0.6595 | 0.2647 | 0.4924 | 0.2635 | 0.4647 | 0.188 | 0.4508 | 0.3335 | 0.4809 | | 0.797 | 30.9953 | 3301 | 1.0333 | 0.3091 | 0.5947 | 0.2852 | 0.1864 | 0.2525 | 0.4721 | 0.2971 | 0.4774 | 0.5051 | 0.2963 | 0.4411 | 0.6837 | 0.5442 | 0.6788 | 0.2436 | 0.4975 | 0.2568 | 0.4714 | 0.1677 | 0.4092 | 0.3331 | 0.4684 | | 0.7778 | 32.0 | 3408 | 1.0285 | 0.3325 | 0.6019 | 0.3123 | 0.2079 | 0.262 | 0.5106 | 0.304 | 0.4879 | 0.5138 | 0.3375 | 0.4535 | 0.7007 | 0.5546 | 0.6784 | 0.3003 | 0.5342 | 0.2837 | 0.4719 | 0.2064 | 0.4092 | 0.3176 | 0.4756 | | 0.7839 | 32.9953 | 3514 | 1.0155 | 0.3302 | 0.6038 | 0.3114 | 0.2003 | 0.2756 | 0.4914 | 0.3 | 0.4923 | 0.5099 | 0.3212 | 0.4624 | 0.6844 | 0.5733 | 0.6955 | 0.2739 | 0.4987 | 0.2816 | 0.4808 | 0.1803 | 0.3969 | 0.342 | 0.4778 | | 0.7687 | 34.0 | 3621 | 1.0158 | 0.3284 | 0.6116 | 0.2986 | 0.2103 | 0.2695 | 0.4791 | 0.2998 | 0.4992 | 0.5258 | 0.3411 | 0.4725 | 0.7092 | 0.5692 | 0.6959 | 0.2751 | 0.5304 | 0.2654 | 0.4746 | 0.1916 | 0.4462 | 0.3409 | 0.4818 | | 0.7798 | 34.9953 | 3727 | 1.0094 | 0.3286 | 0.5951 | 0.3134 | 0.1983 | 0.2685 | 0.5138 | 0.301 | 0.4942 | 0.5227 | 0.3001 | 0.4738 | 0.7313 | 0.5752 | 0.7009 | 0.2566 | 0.5367 | 0.2753 | 0.4902 | 0.203 | 0.4108 | 0.3327 | 0.4751 | | 0.7476 | 36.0 | 3834 | 1.0584 | 0.3212 | 0.5923 | 0.291 | 0.1856 | 0.2576 | 0.5242 | 0.3008 | 0.4828 | 0.5067 | 0.3268 | 0.4238 | 0.7241 | 0.5335 | 0.6509 | 0.2728 | 0.4987 | 0.2713 | 0.479 | 0.1889 | 0.4292 | 0.3393 | 0.4756 | | 0.758 | 36.9953 | 3940 | 1.0163 | 0.3381 | 0.6177 | 0.3258 | 0.2113 | 0.2655 | 0.5359 | 0.3041 | 0.4926 | 0.5221 | 0.3397 | 0.4575 | 0.7092 | 0.5643 | 0.6802 | 0.2738 | 0.5139 | 0.2752 | 0.496 | 0.2291 | 0.4277 | 0.3483 | 0.4929 | | 0.7328 | 38.0 | 4047 | 1.0104 | 0.3349 | 0.6295 | 0.3152 | 0.2034 | 0.2794 | 0.5199 | 0.2966 | 0.5007 | 0.5226 | 0.314 | 0.4783 | 0.6913 | 0.5632 | 0.6856 | 0.2802 | 0.5228 | 0.2773 | 0.4942 | 0.2194 | 0.4262 | 0.3344 | 0.4844 | | 0.7374 | 38.9953 | 4153 | 1.0134 | 0.3422 | 0.6331 | 0.3235 | 0.204 | 0.2763 | 0.5299 | 0.3076 | 0.4982 | 0.5229 | 0.3264 | 0.4727 | 0.7249 | 0.5615 | 0.6757 | 0.3006 | 0.5215 | 0.2717 | 0.4844 | 0.2348 | 0.4446 | 0.3427 | 0.4884 | | 0.7173 | 40.0 | 4260 | 1.0198 | 0.334 | 0.6183 | 0.3207 | 0.1992 | 0.2761 | 0.5124 | 0.305 | 0.4893 | 0.5111 | 0.3233 | 0.4566 | 0.697 | 0.5651 | 0.6802 | 0.2821 | 0.5253 | 0.261 | 0.4621 | 0.2182 | 0.4046 | 0.3437 | 0.4831 | | 0.7148 | 40.9953 | 4366 | 0.9978 | 0.3482 | 0.6318 | 0.342 | 0.2046 | 0.2859 | 0.5363 | 0.3085 | 0.5022 | 0.5245 | 0.3357 | 0.4768 | 0.7011 | 0.574 | 0.6955 | 0.3025 | 0.5304 | 0.284 | 0.4938 | 0.229 | 0.42 | 0.3517 | 0.4831 | | 0.7127 | 42.0 | 4473 | 1.0042 | 0.3485 | 0.6345 | 0.3259 | 0.2234 | 0.2791 | 0.5416 | 0.3111 | 0.4972 | 0.5216 | 0.3453 | 0.4666 | 0.7047 | 0.5772 | 0.6856 | 0.3145 | 0.538 | 0.2813 | 0.479 | 0.2297 | 0.4369 | 0.3399 | 0.4684 | | 0.7189 | 42.9953 | 4579 | 0.9994 | 0.3444 | 0.6235 | 0.3368 | 0.2151 | 0.2735 | 0.5286 | 0.3104 | 0.5028 | 0.5274 | 0.3398 | 0.4767 | 0.6915 | 0.5846 | 0.6923 | 0.3048 | 0.5241 | 0.2814 | 0.4871 | 0.2047 | 0.44 | 0.3467 | 0.4933 | | 0.7085 | 44.0 | 4686 | 1.0234 | 0.3415 | 0.6232 | 0.3203 | 0.2001 | 0.271 | 0.5241 | 0.3101 | 0.4907 | 0.5137 | 0.3153 | 0.4478 | 0.6936 | 0.5731 | 0.6811 | 0.3072 | 0.5152 | 0.2762 | 0.4799 | 0.2148 | 0.4185 | 0.3363 | 0.4738 | | 0.6929 | 44.9953 | 4792 | 1.0076 | 0.3564 | 0.6437 | 0.3355 | 0.2144 | 0.2869 | 0.538 | 0.3097 | 0.4962 | 0.5197 | 0.3187 | 0.4774 | 0.7046 | 0.5891 | 0.7 | 0.3213 | 0.5139 | 0.2797 | 0.4746 | 0.2404 | 0.4246 | 0.3516 | 0.4853 | | 0.6949 | 46.0 | 4899 | 1.0051 | 0.3548 | 0.6513 | 0.3319 | 0.2273 | 0.2876 | 0.5374 | 0.3102 | 0.5104 | 0.5319 | 0.3603 | 0.4717 | 0.7153 | 0.5864 | 0.6995 | 0.3127 | 0.5241 | 0.2817 | 0.4982 | 0.2386 | 0.4385 | 0.3546 | 0.4991 | | 0.6895 | 46.9953 | 5005 | 1.0220 | 0.3454 | 0.6389 | 0.3235 | 0.2023 | 0.2764 | 0.5265 | 0.3074 | 0.49 | 0.5133 | 0.3227 | 0.4464 | 0.7115 | 0.5772 | 0.6887 | 0.3159 | 0.5127 | 0.2738 | 0.4839 | 0.2054 | 0.3938 | 0.3547 | 0.4876 | | 0.6709 | 48.0 | 5112 | 1.0272 | 0.3473 | 0.6374 | 0.3205 | 0.2262 | 0.2689 | 0.527 | 0.3029 | 0.4893 | 0.5139 | 0.3524 | 0.431 | 0.7094 | 0.5839 | 0.6793 | 0.3089 | 0.4949 | 0.2779 | 0.4879 | 0.2197 | 0.4338 | 0.3461 | 0.4733 | | 0.7002 | 48.9953 | 5218 | 1.0188 | 0.349 | 0.645 | 0.3255 | 0.2188 | 0.2713 | 0.5228 | 0.3034 | 0.4888 | 0.5107 | 0.3435 | 0.4357 | 0.6984 | 0.5732 | 0.6788 | 0.316 | 0.4873 | 0.2867 | 0.4938 | 0.2202 | 0.4185 | 0.3488 | 0.4751 | | 0.6732 | 50.0 | 5325 | 1.0171 | 0.35 | 0.6378 | 0.3213 | 0.2193 | 0.2835 | 0.5176 | 0.3053 | 0.4928 | 0.5157 | 0.3323 | 0.4532 | 0.7 | 0.5792 | 0.682 | 0.3271 | 0.4937 | 0.2805 | 0.4897 | 0.2054 | 0.4231 | 0.3579 | 0.4902 | | 0.6866 | 50.9953 | 5431 | 1.0090 | 0.3538 | 0.6428 | 0.3434 | 0.2264 | 0.2822 | 0.5369 | 0.3131 | 0.5026 | 0.5245 | 0.3388 | 0.4515 | 0.7113 | 0.5798 | 0.6883 | 0.3469 | 0.5203 | 0.2896 | 0.5009 | 0.2072 | 0.4462 | 0.3458 | 0.4667 | | 0.6538 | 52.0 | 5538 | 1.0059 | 0.3516 | 0.6406 | 0.3322 | 0.2332 | 0.2843 | 0.5078 | 0.314 | 0.5022 | 0.5281 | 0.3696 | 0.4706 | 0.6918 | 0.5761 | 0.6793 | 0.3438 | 0.5203 | 0.2854 | 0.5058 | 0.2064 | 0.4554 | 0.3465 | 0.4796 | | 0.6531 | 52.9953 | 5644 | 1.0035 | 0.3628 | 0.6559 | 0.3368 | 0.2265 | 0.293 | 0.5513 | 0.3135 | 0.501 | 0.5225 | 0.3514 | 0.4637 | 0.715 | 0.5788 | 0.6901 | 0.3587 | 0.5228 | 0.2939 | 0.4906 | 0.2326 | 0.4323 | 0.3498 | 0.4764 | | 0.6406 | 54.0 | 5751 | 0.9991 | 0.3588 | 0.6417 | 0.3544 | 0.2267 | 0.2905 | 0.5306 | 0.3222 | 0.5071 | 0.5287 | 0.351 | 0.4625 | 0.7189 | 0.5678 | 0.6784 | 0.3426 | 0.5038 | 0.2858 | 0.4933 | 0.2254 | 0.4662 | 0.3725 | 0.5018 | | 0.657 | 54.9953 | 5857 | 1.0076 | 0.3542 | 0.6512 | 0.339 | 0.2209 | 0.2857 | 0.538 | 0.3069 | 0.5015 | 0.5231 | 0.3524 | 0.4679 | 0.7062 | 0.5718 | 0.6784 | 0.3414 | 0.5177 | 0.2952 | 0.4996 | 0.2124 | 0.4385 | 0.3501 | 0.4813 | | 0.6402 | 56.0 | 5964 | 0.9918 | 0.3605 | 0.652 | 0.3421 | 0.2311 | 0.2913 | 0.5183 | 0.3186 | 0.5075 | 0.5283 | 0.3531 | 0.4621 | 0.7194 | 0.5814 | 0.6919 | 0.339 | 0.5203 | 0.2968 | 0.4996 | 0.2286 | 0.4369 | 0.357 | 0.4929 | | 0.6484 | 56.9953 | 6070 | 0.9921 | 0.3573 | 0.649 | 0.3516 | 0.2157 | 0.2891 | 0.5269 | 0.3079 | 0.4988 | 0.5203 | 0.3319 | 0.4464 | 0.7003 | 0.5739 | 0.686 | 0.3577 | 0.5228 | 0.2922 | 0.4964 | 0.2123 | 0.4169 | 0.3501 | 0.4791 | | 0.6532 | 58.0 | 6177 | 1.0018 | 0.358 | 0.6383 | 0.3572 | 0.2156 | 0.2825 | 0.5278 | 0.3075 | 0.4984 | 0.5223 | 0.331 | 0.4471 | 0.6955 | 0.5757 | 0.6838 | 0.3443 | 0.5127 | 0.2947 | 0.5009 | 0.2097 | 0.4277 | 0.3656 | 0.4867 | | 0.6334 | 58.9953 | 6283 | 1.0088 | 0.3543 | 0.6515 | 0.3324 | 0.2214 | 0.2868 | 0.5213 | 0.3055 | 0.4962 | 0.5197 | 0.3428 | 0.4442 | 0.7049 | 0.571 | 0.6802 | 0.3352 | 0.5076 | 0.2883 | 0.4862 | 0.2197 | 0.4431 | 0.3572 | 0.4813 | | 0.6236 | 60.0 | 6390 | 0.9933 | 0.3612 | 0.6485 | 0.3449 | 0.2121 | 0.2941 | 0.523 | 0.3161 | 0.5046 | 0.5262 | 0.3305 | 0.4712 | 0.7022 | 0.5812 | 0.6905 | 0.345 | 0.519 | 0.2932 | 0.4978 | 0.2336 | 0.4477 | 0.3528 | 0.476 | | 0.6294 | 60.9953 | 6496 | 0.9987 | 0.3579 | 0.652 | 0.3353 | 0.2127 | 0.2929 | 0.5445 | 0.3108 | 0.4964 | 0.5231 | 0.3383 | 0.4649 | 0.7097 | 0.5713 | 0.6815 | 0.3413 | 0.5127 | 0.3004 | 0.4955 | 0.2301 | 0.4538 | 0.3466 | 0.472 | | 0.6214 | 62.0 | 6603 | 1.0151 | 0.3581 | 0.6551 | 0.3316 | 0.2173 | 0.2886 | 0.549 | 0.3136 | 0.4965 | 0.513 | 0.3241 | 0.4447 | 0.7 | 0.5721 | 0.6707 | 0.3341 | 0.4911 | 0.2976 | 0.4938 | 0.2294 | 0.4246 | 0.3571 | 0.4849 | | 0.6336 | 62.9953 | 6709 | 1.0027 | 0.3592 | 0.658 | 0.3345 | 0.2247 | 0.2869 | 0.5429 | 0.3145 | 0.4986 | 0.5246 | 0.334 | 0.4603 | 0.7119 | 0.5739 | 0.682 | 0.3459 | 0.5342 | 0.2995 | 0.4996 | 0.2242 | 0.4262 | 0.3524 | 0.4813 | | 0.621 | 64.0 | 6816 | 1.0044 | 0.3609 | 0.6589 | 0.3461 | 0.2236 | 0.2818 | 0.5455 | 0.3162 | 0.4982 | 0.5221 | 0.3418 | 0.462 | 0.7027 | 0.5796 | 0.6865 | 0.3514 | 0.5228 | 0.2969 | 0.5013 | 0.2149 | 0.4108 | 0.3615 | 0.4889 | | 0.6101 | 64.9953 | 6922 | 1.0033 | 0.3676 | 0.668 | 0.3447 | 0.2296 | 0.2977 | 0.5585 | 0.3226 | 0.4976 | 0.5239 | 0.3524 | 0.4703 | 0.7025 | 0.5679 | 0.6842 | 0.3594 | 0.5152 | 0.3062 | 0.4942 | 0.2428 | 0.4338 | 0.3615 | 0.492 | | 0.6076 | 66.0 | 7029 | 0.9941 | 0.3689 | 0.6645 | 0.3522 | 0.2319 | 0.2985 | 0.5601 | 0.3186 | 0.5003 | 0.5252 | 0.3508 | 0.4711 | 0.6945 | 0.5753 | 0.6905 | 0.3515 | 0.5101 | 0.3076 | 0.5058 | 0.2471 | 0.4338 | 0.3631 | 0.4858 | | 0.6004 | 66.9953 | 7135 | 0.9888 | 0.3638 | 0.6631 | 0.3417 | 0.2283 | 0.3053 | 0.5454 | 0.31 | 0.499 | 0.5247 | 0.3405 | 0.4752 | 0.6956 | 0.5704 | 0.6847 | 0.3435 | 0.5089 | 0.3084 | 0.5045 | 0.2381 | 0.4462 | 0.3585 | 0.4791 | | 0.5985 | 68.0 | 7242 | 0.9908 | 0.3642 | 0.6615 | 0.34 | 0.227 | 0.2876 | 0.541 | 0.3139 | 0.4954 | 0.5252 | 0.3562 | 0.4679 | 0.694 | 0.5786 | 0.6919 | 0.3348 | 0.4987 | 0.3017 | 0.4924 | 0.232 | 0.4431 | 0.3737 | 0.5 | | 0.5962 | 68.9953 | 7348 | 0.9841 | 0.3689 | 0.6699 | 0.3442 | 0.2293 | 0.2957 | 0.5557 | 0.3212 | 0.5088 | 0.5314 | 0.3522 | 0.4687 | 0.7093 | 0.5826 | 0.6865 | 0.363 | 0.5215 | 0.3027 | 0.5018 | 0.2322 | 0.4585 | 0.364 | 0.4889 | | 0.5967 | 70.0 | 7455 | 1.0001 | 0.3636 | 0.6702 | 0.3307 | 0.2242 | 0.29 | 0.5608 | 0.3134 | 0.4967 | 0.5249 | 0.3454 | 0.4636 | 0.7088 | 0.5712 | 0.686 | 0.3459 | 0.5177 | 0.3085 | 0.5089 | 0.2384 | 0.4354 | 0.3539 | 0.4764 | | 0.5867 | 70.9953 | 7561 | 0.9964 | 0.3622 | 0.6648 | 0.3244 | 0.2245 | 0.2915 | 0.5393 | 0.3143 | 0.4964 | 0.5191 | 0.3377 | 0.4607 | 0.6897 | 0.5824 | 0.6865 | 0.3328 | 0.5101 | 0.3052 | 0.5004 | 0.2342 | 0.4308 | 0.3566 | 0.4676 | | 0.5868 | 72.0 | 7668 | 0.9980 | 0.3643 | 0.665 | 0.3393 | 0.2257 | 0.2947 | 0.5463 | 0.3163 | 0.5009 | 0.5215 | 0.3281 | 0.4579 | 0.6978 | 0.586 | 0.6869 | 0.3453 | 0.5089 | 0.3085 | 0.5013 | 0.2219 | 0.4246 | 0.3597 | 0.4858 | | 0.5774 | 72.9953 | 7774 | 0.9955 | 0.3707 | 0.6702 | 0.3441 | 0.2287 | 0.303 | 0.551 | 0.3221 | 0.5013 | 0.5222 | 0.3255 | 0.4583 | 0.7021 | 0.5911 | 0.691 | 0.3537 | 0.5089 | 0.3071 | 0.4982 | 0.2425 | 0.4354 | 0.3593 | 0.4773 | | 0.5671 | 74.0 | 7881 | 0.9984 | 0.3679 | 0.6699 | 0.3348 | 0.221 | 0.3006 | 0.5606 | 0.3158 | 0.497 | 0.5228 | 0.3193 | 0.4645 | 0.7144 | 0.585 | 0.6892 | 0.3592 | 0.5316 | 0.2977 | 0.4884 | 0.2421 | 0.4246 | 0.3556 | 0.48 | | 0.5757 | 74.9953 | 7987 | 0.9951 | 0.3698 | 0.6791 | 0.3427 | 0.2276 | 0.2908 | 0.5622 | 0.3161 | 0.5019 | 0.5273 | 0.3439 | 0.4567 | 0.7122 | 0.5872 | 0.6892 | 0.3566 | 0.5291 | 0.304 | 0.5027 | 0.2395 | 0.4262 | 0.3615 | 0.4893 | | 0.5622 | 76.0 | 8094 | 1.0045 | 0.366 | 0.6724 | 0.3297 | 0.2095 | 0.2988 | 0.5485 | 0.3126 | 0.4983 | 0.5187 | 0.3127 | 0.4549 | 0.6987 | 0.5883 | 0.6896 | 0.3453 | 0.5152 | 0.3063 | 0.4951 | 0.2414 | 0.4231 | 0.3489 | 0.4707 | | 0.5692 | 76.9953 | 8200 | 0.9920 | 0.372 | 0.6785 | 0.3435 | 0.229 | 0.2999 | 0.5517 | 0.3169 | 0.5042 | 0.5272 | 0.3422 | 0.4511 | 0.7139 | 0.5897 | 0.6892 | 0.3452 | 0.5089 | 0.3025 | 0.5018 | 0.2578 | 0.4431 | 0.3646 | 0.4929 | | 0.5633 | 78.0 | 8307 | 0.9977 | 0.3663 | 0.6788 | 0.3341 | 0.2171 | 0.2959 | 0.5507 | 0.3143 | 0.4984 | 0.5189 | 0.3155 | 0.4583 | 0.6929 | 0.5866 | 0.691 | 0.3494 | 0.5038 | 0.3 | 0.4893 | 0.2388 | 0.4369 | 0.3569 | 0.4733 | | 0.5671 | 78.9953 | 8413 | 0.9957 | 0.3649 | 0.6697 | 0.3343 | 0.2222 | 0.2848 | 0.5576 | 0.3146 | 0.5011 | 0.5227 | 0.3147 | 0.4604 | 0.7049 | 0.5839 | 0.6901 | 0.3487 | 0.5114 | 0.3043 | 0.496 | 0.234 | 0.4431 | 0.3538 | 0.4729 | | 0.5496 | 80.0 | 8520 | 0.9874 | 0.3671 | 0.667 | 0.3476 | 0.2313 | 0.2869 | 0.5656 | 0.3153 | 0.503 | 0.5282 | 0.3407 | 0.4584 | 0.7089 | 0.5876 | 0.6964 | 0.3491 | 0.5089 | 0.3027 | 0.504 | 0.2307 | 0.4338 | 0.3655 | 0.4978 | | 0.5628 | 80.9953 | 8626 | 0.9996 | 0.3664 | 0.6683 | 0.3343 | 0.2148 | 0.288 | 0.5608 | 0.3162 | 0.4997 | 0.5215 | 0.3092 | 0.4619 | 0.6996 | 0.5885 | 0.6896 | 0.3541 | 0.5203 | 0.3081 | 0.4951 | 0.2333 | 0.4277 | 0.3478 | 0.4747 | | 0.5609 | 82.0 | 8733 | 0.9844 | 0.3712 | 0.6712 | 0.3547 | 0.2264 | 0.2906 | 0.5841 | 0.3206 | 0.5043 | 0.5334 | 0.361 | 0.4736 | 0.7136 | 0.5874 | 0.6982 | 0.3723 | 0.5443 | 0.3037 | 0.5031 | 0.2299 | 0.4338 | 0.3626 | 0.4876 | | 0.5581 | 82.9953 | 8839 | 0.9873 | 0.3699 | 0.6706 | 0.3568 | 0.2302 | 0.2896 | 0.5803 | 0.3224 | 0.5115 | 0.5333 | 0.3533 | 0.4764 | 0.7146 | 0.5853 | 0.6905 | 0.3735 | 0.5481 | 0.3054 | 0.5036 | 0.2339 | 0.44 | 0.3517 | 0.4844 | | 0.5539 | 84.0 | 8946 | 0.9930 | 0.3686 | 0.6638 | 0.354 | 0.2285 | 0.2868 | 0.565 | 0.3166 | 0.5006 | 0.5228 | 0.3556 | 0.4537 | 0.6896 | 0.5846 | 0.6784 | 0.3534 | 0.5127 | 0.3075 | 0.4929 | 0.2357 | 0.4323 | 0.362 | 0.4978 | | 0.5481 | 84.9953 | 9052 | 0.9930 | 0.3714 | 0.6746 | 0.3588 | 0.221 | 0.2916 | 0.5803 | 0.3177 | 0.4979 | 0.5222 | 0.3152 | 0.4599 | 0.711 | 0.5915 | 0.6883 | 0.355 | 0.5038 | 0.3077 | 0.5013 | 0.2504 | 0.4338 | 0.3525 | 0.4836 | | 0.5405 | 86.0 | 9159 | 0.9839 | 0.3808 | 0.6833 | 0.3759 | 0.236 | 0.2986 | 0.595 | 0.3208 | 0.5112 | 0.5343 | 0.3523 | 0.4722 | 0.7192 | 0.5949 | 0.6937 | 0.3826 | 0.538 | 0.3108 | 0.504 | 0.2475 | 0.4385 | 0.3685 | 0.4973 | | 0.5532 | 86.9953 | 9265 | 0.9859 | 0.3782 | 0.677 | 0.3672 | 0.2331 | 0.3023 | 0.5736 | 0.322 | 0.5076 | 0.5317 | 0.348 | 0.471 | 0.7091 | 0.5907 | 0.6865 | 0.3714 | 0.5228 | 0.315 | 0.5112 | 0.2551 | 0.4492 | 0.3588 | 0.4889 | | 0.5478 | 88.0 | 9372 | 0.9918 | 0.3702 | 0.6746 | 0.3544 | 0.2255 | 0.2911 | 0.5666 | 0.3203 | 0.5101 | 0.5326 | 0.3492 | 0.4616 | 0.7194 | 0.589 | 0.6923 | 0.3545 | 0.5354 | 0.3092 | 0.5018 | 0.2419 | 0.4369 | 0.3566 | 0.4964 | | 0.5532 | 88.9953 | 9478 | 0.9928 | 0.3715 | 0.6745 | 0.3518 | 0.2266 | 0.2887 | 0.5828 | 0.3232 | 0.5087 | 0.5288 | 0.3494 | 0.4602 | 0.7206 | 0.5857 | 0.6874 | 0.365 | 0.5228 | 0.3092 | 0.5031 | 0.2387 | 0.4369 | 0.359 | 0.4938 | | 0.5285 | 90.0 | 9585 | 0.9974 | 0.3706 | 0.6768 | 0.3474 | 0.2167 | 0.2854 | 0.5773 | 0.3197 | 0.5014 | 0.5226 | 0.333 | 0.4542 | 0.7111 | 0.5885 | 0.6869 | 0.362 | 0.5152 | 0.3083 | 0.4924 | 0.2354 | 0.4369 | 0.3588 | 0.4818 | | 0.5262 | 90.9953 | 9691 | 0.9878 | 0.3712 | 0.6715 | 0.3515 | 0.2274 | 0.2869 | 0.5817 | 0.319 | 0.5012 | 0.522 | 0.3362 | 0.4556 | 0.7103 | 0.5862 | 0.6833 | 0.364 | 0.5165 | 0.3136 | 0.5004 | 0.235 | 0.4277 | 0.3573 | 0.4822 | | 0.5282 | 92.0 | 9798 | 0.9987 | 0.3678 | 0.6722 | 0.3388 | 0.2241 | 0.2803 | 0.5825 | 0.3168 | 0.4989 | 0.5214 | 0.3418 | 0.4403 | 0.7094 | 0.5843 | 0.6793 | 0.3555 | 0.5089 | 0.3106 | 0.5063 | 0.2378 | 0.4323 | 0.351 | 0.4804 | | 0.5294 | 92.9953 | 9904 | 0.9893 | 0.3692 | 0.6677 | 0.3468 | 0.2254 | 0.2829 | 0.5824 | 0.3175 | 0.5027 | 0.5261 | 0.3528 | 0.4565 | 0.7133 | 0.5836 | 0.6833 | 0.3524 | 0.5076 | 0.3107 | 0.5067 | 0.238 | 0.4415 | 0.3611 | 0.4911 | | 0.5122 | 94.0 | 10011 | 0.9880 | 0.3716 | 0.6687 | 0.3577 | 0.2239 | 0.2891 | 0.5818 | 0.3172 | 0.5027 | 0.5285 | 0.3487 | 0.4577 | 0.7144 | 0.5848 | 0.6869 | 0.3595 | 0.5127 | 0.3123 | 0.5094 | 0.2382 | 0.44 | 0.3634 | 0.4933 | | 0.5358 | 94.9953 | 10117 | 0.9913 | 0.3717 | 0.6662 | 0.3481 | 0.2242 | 0.2874 | 0.5813 | 0.3208 | 0.5052 | 0.5301 | 0.3474 | 0.4647 | 0.7192 | 0.587 | 0.6905 | 0.3641 | 0.5177 | 0.3139 | 0.508 | 0.2337 | 0.4415 | 0.3599 | 0.4929 | | 0.5233 | 96.0 | 10224 | 0.9908 | 0.3704 | 0.6692 | 0.3508 | 0.2253 | 0.2858 | 0.5802 | 0.318 | 0.5023 | 0.5274 | 0.3491 | 0.4604 | 0.7148 | 0.5802 | 0.682 | 0.3639 | 0.5215 | 0.3112 | 0.5063 | 0.2356 | 0.4415 | 0.3612 | 0.4858 | | 0.5136 | 96.9953 | 10330 | 0.9903 | 0.3724 | 0.6732 | 0.3511 | 0.2265 | 0.2847 | 0.5814 | 0.3191 | 0.5027 | 0.5268 | 0.3445 | 0.4537 | 0.7195 | 0.5812 | 0.6824 | 0.3691 | 0.5304 | 0.3142 | 0.5067 | 0.2354 | 0.4308 | 0.3619 | 0.4836 | | 0.5204 | 98.0 | 10437 | 0.9903 | 0.3722 | 0.674 | 0.352 | 0.2277 | 0.2851 | 0.5843 | 0.3197 | 0.5036 | 0.5272 | 0.3448 | 0.4543 | 0.7207 | 0.5841 | 0.6824 | 0.3658 | 0.5228 | 0.3146 | 0.508 | 0.2303 | 0.4308 | 0.3661 | 0.492 | | 0.5237 | 98.9953 | 10543 | 0.9917 | 0.3721 | 0.6715 | 0.3545 | 0.2264 | 0.2841 | 0.5841 | 0.3197 | 0.5021 | 0.527 | 0.3454 | 0.4453 | 0.7226 | 0.5826 | 0.6797 | 0.3684 | 0.5241 | 0.3111 | 0.5063 | 0.2332 | 0.4369 | 0.3648 | 0.488 | | 0.4776 | 99.5305 | 10600 | 0.9911 | 0.3714 | 0.6742 | 0.3545 | 0.226 | 0.2836 | 0.5849 | 0.3191 | 0.502 | 0.5266 | 0.3445 | 0.4443 | 0.7237 | 0.5834 | 0.6797 | 0.3648 | 0.5241 | 0.3122 | 0.5071 | 0.2315 | 0.4338 | 0.3649 | 0.4884 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/u73br2l9) # facebook-detr-resnet-50-finetuned-10k-cppe5-with-augs This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.2371 - Map: 0.2873 - Map 50: 0.5678 - Map 75: 0.2501 - Map Small: 0.126 - Map Medium: 0.2327 - Map Large: 0.4873 - Mar 1: 0.2843 - Mar 10: 0.4643 - Mar 100: 0.4762 - Mar Small: 0.2338 - Mar Medium: 0.4167 - Mar Large: 0.7114 - Map Coverall: 0.5461 - Mar 100 Coverall: 0.6932 - Map Face Shield: 0.2167 - Mar 100 Face Shield: 0.4785 - Map Gloves: 0.2135 - Mar 100 Gloves: 0.4094 - Map Goggles: 0.173 - Mar 100 Goggles: 0.4092 - Map Mask: 0.2871 - Mar 100 Mask: 0.3907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 2.6324 | 1.0 | 107 | 2.3696 | 0.0355 | 0.0772 | 0.0305 | 0.0094 | 0.0044 | 0.0388 | 0.0606 | 0.1313 | 0.1643 | 0.0314 | 0.0921 | 0.1907 | 0.1559 | 0.536 | 0.0 | 0.0 | 0.0064 | 0.1433 | 0.0 | 0.0 | 0.0151 | 0.1422 | | 2.0851 | 2.0 | 214 | 2.1468 | 0.0518 | 0.1164 | 0.0389 | 0.0106 | 0.0151 | 0.0683 | 0.0809 | 0.1628 | 0.1881 | 0.023 | 0.1069 | 0.2515 | 0.2068 | 0.5842 | 0.0 | 0.0 | 0.0174 | 0.167 | 0.0 | 0.0 | 0.035 | 0.1893 | | 1.9849 | 3.0 | 321 | 2.0694 | 0.0592 | 0.1436 | 0.0377 | 0.0187 | 0.023 | 0.0609 | 0.0796 | 0.1666 | 0.191 | 0.0527 | 0.1032 | 0.2411 | 0.2119 | 0.5486 | 0.0 | 0.0 | 0.0226 | 0.1799 | 0.0 | 0.0 | 0.0615 | 0.2262 | | 1.8364 | 4.0 | 428 | 2.0431 | 0.0715 | 0.1666 | 0.0625 | 0.0031 | 0.0232 | 0.09 | 0.0775 | 0.1641 | 0.183 | 0.0083 | 0.1006 | 0.2708 | 0.2923 | 0.5604 | 0.0012 | 0.0177 | 0.0203 | 0.1612 | 0.0 | 0.0 | 0.0437 | 0.1756 | | 1.7433 | 5.0 | 535 | 1.9001 | 0.0938 | 0.2115 | 0.0758 | 0.0229 | 0.0473 | 0.1031 | 0.1066 | 0.216 | 0.2311 | 0.0451 | 0.1423 | 0.3186 | 0.3168 | 0.5847 | 0.0064 | 0.0937 | 0.05 | 0.2277 | 0.0 | 0.0 | 0.0959 | 0.2493 | | 1.7113 | 6.0 | 642 | 1.8483 | 0.1007 | 0.2225 | 0.0773 | 0.0203 | 0.0593 | 0.1341 | 0.1109 | 0.2213 | 0.2463 | 0.0646 | 0.1711 | 0.3303 | 0.3462 | 0.5914 | 0.0132 | 0.0987 | 0.0451 | 0.2571 | 0.0002 | 0.0062 | 0.0987 | 0.2782 | | 1.6518 | 7.0 | 749 | 1.8376 | 0.1055 | 0.2347 | 0.0807 | 0.0199 | 0.0596 | 0.1405 | 0.1127 | 0.2364 | 0.2598 | 0.0778 | 0.1949 | 0.3276 | 0.3693 | 0.5869 | 0.0168 | 0.1367 | 0.0417 | 0.2397 | 0.0046 | 0.0523 | 0.0953 | 0.2831 | | 1.6048 | 8.0 | 856 | 1.8041 | 0.1098 | 0.2576 | 0.0857 | 0.0155 | 0.0772 | 0.1578 | 0.1284 | 0.2423 | 0.2708 | 0.0602 | 0.2057 | 0.3631 | 0.3512 | 0.6149 | 0.033 | 0.1342 | 0.0402 | 0.2491 | 0.004 | 0.0862 | 0.1204 | 0.2698 | | 1.6026 | 9.0 | 963 | 1.7173 | 0.1243 | 0.2773 | 0.1041 | 0.0415 | 0.0688 | 0.1767 | 0.1426 | 0.2852 | 0.3101 | 0.0857 | 0.2275 | 0.4249 | 0.3889 | 0.6378 | 0.0347 | 0.2253 | 0.0547 | 0.271 | 0.0159 | 0.1308 | 0.1274 | 0.2858 | | 1.5298 | 10.0 | 1070 | 1.7915 | 0.1154 | 0.2763 | 0.0804 | 0.018 | 0.0789 | 0.1762 | 0.1447 | 0.2629 | 0.2913 | 0.0774 | 0.2191 | 0.3935 | 0.3615 | 0.618 | 0.0415 | 0.2127 | 0.0351 | 0.2603 | 0.0076 | 0.0846 | 0.1315 | 0.2809 | | 1.5114 | 11.0 | 1177 | 1.7964 | 0.1232 | 0.2755 | 0.0915 | 0.0396 | 0.0849 | 0.1728 | 0.144 | 0.2876 | 0.3007 | 0.0868 | 0.2161 | 0.4535 | 0.3588 | 0.5923 | 0.035 | 0.2291 | 0.057 | 0.2241 | 0.0195 | 0.1877 | 0.1455 | 0.2702 | | 1.5283 | 12.0 | 1284 | 1.7017 | 0.1444 | 0.3075 | 0.1223 | 0.0215 | 0.0985 | 0.2396 | 0.1803 | 0.3244 | 0.3456 | 0.0531 | 0.282 | 0.5554 | 0.4122 | 0.632 | 0.0666 | 0.3215 | 0.0618 | 0.2625 | 0.0086 | 0.2046 | 0.1727 | 0.3076 | | 1.5034 | 13.0 | 1391 | 1.6344 | 0.1538 | 0.3418 | 0.1226 | 0.0423 | 0.1165 | 0.2381 | 0.1871 | 0.337 | 0.3578 | 0.1195 | 0.2901 | 0.5214 | 0.4204 | 0.6284 | 0.0705 | 0.319 | 0.0634 | 0.2884 | 0.0191 | 0.2277 | 0.1959 | 0.3253 | | 1.445 | 14.0 | 1498 | 1.5685 | 0.167 | 0.3626 | 0.15 | 0.0561 | 0.1266 | 0.2715 | 0.1998 | 0.357 | 0.3701 | 0.1068 | 0.2922 | 0.5693 | 0.4227 | 0.6342 | 0.0873 | 0.3797 | 0.0744 | 0.2951 | 0.0441 | 0.2108 | 0.2065 | 0.3307 | | 1.3998 | 15.0 | 1605 | 1.5679 | 0.1645 | 0.3515 | 0.1383 | 0.0269 | 0.117 | 0.2592 | 0.1992 | 0.3295 | 0.3539 | 0.0645 | 0.2751 | 0.5533 | 0.4535 | 0.6703 | 0.0887 | 0.3494 | 0.0866 | 0.3116 | 0.0276 | 0.1554 | 0.1659 | 0.2827 | | 1.398 | 16.0 | 1712 | 1.6191 | 0.1578 | 0.3459 | 0.1205 | 0.0623 | 0.0967 | 0.258 | 0.1729 | 0.3255 | 0.3386 | 0.1106 | 0.2686 | 0.5066 | 0.4363 | 0.6 | 0.0971 | 0.3089 | 0.0703 | 0.2839 | 0.0258 | 0.2 | 0.1593 | 0.3004 | | 1.369 | 17.0 | 1819 | 1.6099 | 0.1654 | 0.3517 | 0.1371 | 0.0341 | 0.099 | 0.2543 | 0.1852 | 0.3085 | 0.3232 | 0.0587 | 0.2331 | 0.5126 | 0.4663 | 0.6329 | 0.0784 | 0.2785 | 0.0761 | 0.2906 | 0.019 | 0.12 | 0.187 | 0.2942 | | 1.3264 | 18.0 | 1926 | 1.5610 | 0.1784 | 0.3752 | 0.1594 | 0.036 | 0.1249 | 0.3082 | 0.2061 | 0.3388 | 0.3561 | 0.0959 | 0.2702 | 0.5669 | 0.5 | 0.6707 | 0.0768 | 0.2937 | 0.0899 | 0.2933 | 0.0291 | 0.2108 | 0.1962 | 0.312 | | 1.3513 | 19.0 | 2033 | 1.4462 | 0.1864 | 0.3933 | 0.154 | 0.0559 | 0.1364 | 0.2738 | 0.2135 | 0.3767 | 0.3931 | 0.1216 | 0.3178 | 0.5744 | 0.4834 | 0.677 | 0.0976 | 0.3595 | 0.1022 | 0.3438 | 0.0248 | 0.2554 | 0.224 | 0.3298 | | 1.3034 | 20.0 | 2140 | 1.4527 | 0.1965 | 0.412 | 0.1713 | 0.051 | 0.1463 | 0.2967 | 0.2087 | 0.3643 | 0.3805 | 0.1422 | 0.2931 | 0.5833 | 0.4982 | 0.6495 | 0.1144 | 0.362 | 0.0997 | 0.3263 | 0.0497 | 0.2277 | 0.2204 | 0.3369 | | 1.2823 | 21.0 | 2247 | 1.4872 | 0.1835 | 0.3972 | 0.1556 | 0.0596 | 0.1308 | 0.282 | 0.2148 | 0.3924 | 0.4147 | 0.1236 | 0.3657 | 0.5903 | 0.4622 | 0.6455 | 0.0727 | 0.3873 | 0.1195 | 0.3558 | 0.0544 | 0.3415 | 0.2084 | 0.3431 | | 1.3099 | 22.0 | 2354 | 1.4723 | 0.1903 | 0.407 | 0.1632 | 0.0594 | 0.1343 | 0.2937 | 0.2158 | 0.3817 | 0.396 | 0.1357 | 0.3222 | 0.5737 | 0.4719 | 0.6486 | 0.1089 | 0.3722 | 0.1153 | 0.3161 | 0.0404 | 0.3092 | 0.2149 | 0.3338 | | 1.2691 | 23.0 | 2461 | 1.4648 | 0.1897 | 0.403 | 0.1626 | 0.0598 | 0.1476 | 0.2933 | 0.2129 | 0.3676 | 0.3851 | 0.1154 | 0.3247 | 0.592 | 0.4836 | 0.6658 | 0.0711 | 0.3025 | 0.1217 | 0.3679 | 0.0594 | 0.2615 | 0.2127 | 0.328 | | 1.3018 | 24.0 | 2568 | 1.5593 | 0.174 | 0.3964 | 0.1358 | 0.0568 | 0.1344 | 0.2776 | 0.2076 | 0.3571 | 0.3754 | 0.1412 | 0.2946 | 0.5732 | 0.4208 | 0.6347 | 0.0782 | 0.2987 | 0.1125 | 0.3304 | 0.0444 | 0.2831 | 0.2141 | 0.3302 | | 1.2744 | 25.0 | 2675 | 1.4785 | 0.1863 | 0.3983 | 0.1556 | 0.0706 | 0.1314 | 0.3032 | 0.221 | 0.3784 | 0.3988 | 0.1453 | 0.3331 | 0.5824 | 0.4831 | 0.6716 | 0.0934 | 0.3392 | 0.1155 | 0.3647 | 0.0612 | 0.3277 | 0.1783 | 0.2907 | | 1.3089 | 26.0 | 2782 | 1.4503 | 0.1969 | 0.4129 | 0.166 | 0.0904 | 0.1342 | 0.3274 | 0.2303 | 0.3907 | 0.4057 | 0.154 | 0.3252 | 0.6182 | 0.4773 | 0.6653 | 0.109 | 0.4114 | 0.1176 | 0.3504 | 0.0758 | 0.2877 | 0.2046 | 0.3138 | | 1.2391 | 27.0 | 2889 | 1.4961 | 0.1992 | 0.4097 | 0.1714 | 0.067 | 0.1354 | 0.3227 | 0.2308 | 0.3803 | 0.3971 | 0.1333 | 0.3179 | 0.623 | 0.4826 | 0.6473 | 0.114 | 0.3329 | 0.1169 | 0.3621 | 0.0538 | 0.2862 | 0.2286 | 0.3569 | | 1.2156 | 28.0 | 2996 | 1.4349 | 0.1926 | 0.4121 | 0.1612 | 0.0655 | 0.1425 | 0.2933 | 0.2279 | 0.4047 | 0.425 | 0.1521 | 0.36 | 0.6217 | 0.4698 | 0.6797 | 0.0974 | 0.3924 | 0.1097 | 0.3402 | 0.061 | 0.3677 | 0.2253 | 0.3449 | | 1.2131 | 29.0 | 3103 | 1.4869 | 0.2002 | 0.4106 | 0.1706 | 0.0662 | 0.1516 | 0.3142 | 0.2342 | 0.3959 | 0.4175 | 0.1389 | 0.3525 | 0.6269 | 0.4846 | 0.6514 | 0.0845 | 0.3899 | 0.1196 | 0.3701 | 0.0699 | 0.3277 | 0.2425 | 0.3484 | | 1.2368 | 30.0 | 3210 | 1.3779 | 0.218 | 0.4403 | 0.185 | 0.084 | 0.169 | 0.3567 | 0.241 | 0.4115 | 0.4326 | 0.148 | 0.3738 | 0.6216 | 0.5063 | 0.6815 | 0.122 | 0.4342 | 0.1497 | 0.3969 | 0.066 | 0.3031 | 0.2462 | 0.3471 | | 1.1998 | 31.0 | 3317 | 1.4472 | 0.198 | 0.4299 | 0.16 | 0.0595 | 0.1492 | 0.3264 | 0.2239 | 0.3883 | 0.3992 | 0.127 | 0.3237 | 0.6434 | 0.4806 | 0.6455 | 0.1023 | 0.3722 | 0.1246 | 0.3509 | 0.0601 | 0.3154 | 0.2224 | 0.312 | | 1.1681 | 32.0 | 3424 | 1.4495 | 0.2221 | 0.4518 | 0.1953 | 0.0777 | 0.1696 | 0.3691 | 0.2318 | 0.3938 | 0.4082 | 0.1272 | 0.3478 | 0.6191 | 0.5124 | 0.6757 | 0.1293 | 0.362 | 0.148 | 0.3531 | 0.0893 | 0.32 | 0.2318 | 0.3302 | | 1.1764 | 33.0 | 3531 | 1.4152 | 0.2008 | 0.4169 | 0.1755 | 0.0844 | 0.154 | 0.3627 | 0.2432 | 0.4092 | 0.4304 | 0.182 | 0.3751 | 0.6587 | 0.4828 | 0.6802 | 0.0883 | 0.3886 | 0.1488 | 0.3884 | 0.069 | 0.3692 | 0.2154 | 0.3258 | | 1.1979 | 34.0 | 3638 | 1.4428 | 0.2013 | 0.4058 | 0.1813 | 0.0525 | 0.1428 | 0.3315 | 0.2318 | 0.3933 | 0.4123 | 0.1116 | 0.3492 | 0.6431 | 0.4904 | 0.6486 | 0.0817 | 0.3785 | 0.1434 | 0.3576 | 0.0536 | 0.3277 | 0.2373 | 0.3493 | | 1.1635 | 35.0 | 3745 | 1.3778 | 0.2114 | 0.4473 | 0.1701 | 0.0619 | 0.1497 | 0.372 | 0.2475 | 0.3926 | 0.4125 | 0.1264 | 0.3367 | 0.6545 | 0.4973 | 0.6577 | 0.1031 | 0.3595 | 0.1518 | 0.379 | 0.0604 | 0.3262 | 0.2444 | 0.34 | | 1.1449 | 36.0 | 3852 | 1.3933 | 0.2136 | 0.4427 | 0.1846 | 0.0791 | 0.1615 | 0.3765 | 0.2354 | 0.4041 | 0.4206 | 0.1575 | 0.3565 | 0.6543 | 0.4915 | 0.6626 | 0.1052 | 0.4101 | 0.1672 | 0.3795 | 0.0773 | 0.3169 | 0.227 | 0.3338 | | 1.1453 | 37.0 | 3959 | 1.3954 | 0.205 | 0.4224 | 0.176 | 0.0648 | 0.1498 | 0.3456 | 0.2412 | 0.3984 | 0.4095 | 0.1209 | 0.3492 | 0.6536 | 0.492 | 0.6423 | 0.1063 | 0.3633 | 0.1628 | 0.3893 | 0.0527 | 0.3292 | 0.2114 | 0.3236 | | 1.1424 | 38.0 | 4066 | 1.3781 | 0.2137 | 0.4373 | 0.1839 | 0.0774 | 0.1634 | 0.3815 | 0.2488 | 0.4149 | 0.4281 | 0.1632 | 0.3589 | 0.6559 | 0.4926 | 0.6559 | 0.101 | 0.4 | 0.1576 | 0.3616 | 0.059 | 0.3415 | 0.258 | 0.3813 | | 1.1118 | 39.0 | 4173 | 1.3672 | 0.2154 | 0.4547 | 0.1737 | 0.0746 | 0.1538 | 0.3736 | 0.2448 | 0.3939 | 0.4125 | 0.1372 | 0.3446 | 0.6372 | 0.5024 | 0.6779 | 0.1303 | 0.3608 | 0.1493 | 0.3719 | 0.0607 | 0.3123 | 0.2341 | 0.3396 | | 1.088 | 40.0 | 4280 | 1.3414 | 0.2193 | 0.4552 | 0.1867 | 0.0824 | 0.1711 | 0.3886 | 0.2451 | 0.4043 | 0.4178 | 0.1678 | 0.3501 | 0.6465 | 0.4978 | 0.6658 | 0.1297 | 0.3557 | 0.1644 | 0.404 | 0.0618 | 0.3077 | 0.2426 | 0.356 | | 1.1114 | 41.0 | 4387 | 1.3819 | 0.2228 | 0.4721 | 0.175 | 0.0879 | 0.1609 | 0.3706 | 0.255 | 0.4006 | 0.4146 | 0.1458 | 0.3427 | 0.6389 | 0.4924 | 0.6689 | 0.1164 | 0.3734 | 0.169 | 0.3679 | 0.0961 | 0.3138 | 0.2402 | 0.3489 | | 1.0758 | 42.0 | 4494 | 1.3459 | 0.2286 | 0.4696 | 0.2048 | 0.0897 | 0.1806 | 0.3734 | 0.2526 | 0.4245 | 0.4366 | 0.1566 | 0.3898 | 0.6341 | 0.5112 | 0.6815 | 0.1497 | 0.4038 | 0.1742 | 0.3938 | 0.059 | 0.3338 | 0.2491 | 0.3702 | | 1.0747 | 43.0 | 4601 | 1.4411 | 0.2178 | 0.4397 | 0.1951 | 0.0581 | 0.1557 | 0.3977 | 0.232 | 0.3933 | 0.4086 | 0.1258 | 0.3301 | 0.6699 | 0.4929 | 0.6604 | 0.1317 | 0.3633 | 0.1632 | 0.3598 | 0.1014 | 0.34 | 0.1997 | 0.3196 | | 1.0909 | 44.0 | 4708 | 1.3192 | 0.2424 | 0.4916 | 0.2152 | 0.0842 | 0.1953 | 0.4032 | 0.26 | 0.4237 | 0.4387 | 0.1597 | 0.3801 | 0.66 | 0.5041 | 0.6842 | 0.1765 | 0.4038 | 0.1901 | 0.3879 | 0.0983 | 0.3585 | 0.2431 | 0.3591 | | 1.0527 | 45.0 | 4815 | 1.3110 | 0.2362 | 0.4902 | 0.2009 | 0.1033 | 0.1842 | 0.4042 | 0.2505 | 0.4306 | 0.4496 | 0.1869 | 0.3896 | 0.6601 | 0.5178 | 0.6797 | 0.1379 | 0.4215 | 0.1828 | 0.4098 | 0.0848 | 0.3754 | 0.2576 | 0.3613 | | 1.0421 | 46.0 | 4922 | 1.3186 | 0.2376 | 0.4944 | 0.213 | 0.097 | 0.19 | 0.3833 | 0.2587 | 0.4303 | 0.4487 | 0.2199 | 0.399 | 0.643 | 0.5174 | 0.6824 | 0.1569 | 0.4114 | 0.171 | 0.3902 | 0.0818 | 0.3923 | 0.261 | 0.3671 | | 1.0428 | 47.0 | 5029 | 1.3096 | 0.2421 | 0.4797 | 0.2235 | 0.0976 | 0.196 | 0.4236 | 0.2574 | 0.4299 | 0.4437 | 0.2217 | 0.3688 | 0.6646 | 0.5242 | 0.6824 | 0.155 | 0.419 | 0.1757 | 0.396 | 0.1006 | 0.3585 | 0.255 | 0.3627 | | 1.0369 | 48.0 | 5136 | 1.3049 | 0.2447 | 0.4953 | 0.2069 | 0.0929 | 0.1971 | 0.421 | 0.2651 | 0.4284 | 0.4393 | 0.1395 | 0.3866 | 0.6669 | 0.5228 | 0.6892 | 0.1539 | 0.419 | 0.1757 | 0.3763 | 0.1091 | 0.3492 | 0.2619 | 0.3627 | | 1.0315 | 49.0 | 5243 | 1.3189 | 0.2417 | 0.4851 | 0.2149 | 0.0914 | 0.1967 | 0.4224 | 0.2682 | 0.4241 | 0.4431 | 0.1874 | 0.3756 | 0.6742 | 0.5079 | 0.691 | 0.1679 | 0.4215 | 0.1627 | 0.3857 | 0.1174 | 0.3569 | 0.2527 | 0.3604 | | 1.0149 | 50.0 | 5350 | 1.3127 | 0.2399 | 0.4962 | 0.2041 | 0.096 | 0.1854 | 0.4276 | 0.2638 | 0.4178 | 0.4325 | 0.1657 | 0.3617 | 0.6608 | 0.5148 | 0.6797 | 0.1434 | 0.3734 | 0.1616 | 0.3746 | 0.1342 | 0.3831 | 0.2454 | 0.3516 | | 1.0223 | 51.0 | 5457 | 1.2917 | 0.2419 | 0.4981 | 0.2153 | 0.0942 | 0.2027 | 0.4113 | 0.2574 | 0.4243 | 0.4418 | 0.1948 | 0.3921 | 0.6428 | 0.5136 | 0.6928 | 0.1563 | 0.4038 | 0.1727 | 0.3987 | 0.1049 | 0.36 | 0.2618 | 0.3538 | | 1.0072 | 52.0 | 5564 | 1.3413 | 0.2634 | 0.5259 | 0.2292 | 0.0985 | 0.2074 | 0.4356 | 0.2615 | 0.415 | 0.4305 | 0.1818 | 0.354 | 0.6634 | 0.5187 | 0.6739 | 0.2066 | 0.4076 | 0.1795 | 0.3915 | 0.1321 | 0.3215 | 0.2802 | 0.3582 | | 0.9923 | 53.0 | 5671 | 1.3195 | 0.2393 | 0.4806 | 0.2104 | 0.1019 | 0.1819 | 0.3959 | 0.2597 | 0.4239 | 0.4406 | 0.1933 | 0.3776 | 0.6387 | 0.5188 | 0.677 | 0.1376 | 0.4114 | 0.1713 | 0.4103 | 0.091 | 0.3308 | 0.2775 | 0.3738 | | 0.9981 | 54.0 | 5778 | 1.3229 | 0.2406 | 0.5014 | 0.2014 | 0.1122 | 0.1896 | 0.404 | 0.2441 | 0.4128 | 0.4303 | 0.1999 | 0.3707 | 0.6241 | 0.5172 | 0.673 | 0.1521 | 0.3975 | 0.1695 | 0.4009 | 0.1067 | 0.32 | 0.2574 | 0.36 | | 0.9892 | 55.0 | 5885 | 1.3100 | 0.2483 | 0.5044 | 0.2104 | 0.0779 | 0.1985 | 0.4205 | 0.2606 | 0.4207 | 0.4357 | 0.1744 | 0.3783 | 0.6592 | 0.5088 | 0.6608 | 0.1744 | 0.4051 | 0.1689 | 0.3915 | 0.1261 | 0.36 | 0.2632 | 0.3609 | | 0.9704 | 56.0 | 5992 | 1.3102 | 0.2508 | 0.5103 | 0.22 | 0.0998 | 0.1951 | 0.4222 | 0.259 | 0.4147 | 0.4341 | 0.158 | 0.3784 | 0.6439 | 0.5205 | 0.6797 | 0.1459 | 0.4038 | 0.1683 | 0.4045 | 0.1467 | 0.3338 | 0.2723 | 0.3484 | | 0.9724 | 57.0 | 6099 | 1.2838 | 0.2591 | 0.5087 | 0.2339 | 0.1102 | 0.2132 | 0.4197 | 0.267 | 0.4329 | 0.4461 | 0.2133 | 0.3891 | 0.6508 | 0.5396 | 0.6901 | 0.1688 | 0.4139 | 0.1781 | 0.3835 | 0.1362 | 0.3708 | 0.2727 | 0.3724 | | 0.9768 | 58.0 | 6206 | 1.2982 | 0.2591 | 0.5133 | 0.2325 | 0.0919 | 0.2165 | 0.4129 | 0.2634 | 0.4314 | 0.4431 | 0.1627 | 0.3999 | 0.6589 | 0.5289 | 0.6923 | 0.171 | 0.4013 | 0.1841 | 0.3821 | 0.1417 | 0.3708 | 0.2695 | 0.3689 | | 0.974 | 59.0 | 6313 | 1.2888 | 0.2611 | 0.5216 | 0.239 | 0.1126 | 0.2094 | 0.4504 | 0.2659 | 0.4367 | 0.4506 | 0.2099 | 0.3957 | 0.6611 | 0.5364 | 0.6833 | 0.1824 | 0.4304 | 0.1768 | 0.3884 | 0.1455 | 0.38 | 0.2646 | 0.3711 | | 0.9585 | 60.0 | 6420 | 1.2801 | 0.269 | 0.5399 | 0.2305 | 0.103 | 0.2209 | 0.4622 | 0.2636 | 0.4453 | 0.4548 | 0.227 | 0.3944 | 0.6777 | 0.5471 | 0.6896 | 0.1805 | 0.4392 | 0.1935 | 0.3862 | 0.1518 | 0.3908 | 0.272 | 0.368 | | 0.9693 | 61.0 | 6527 | 1.2816 | 0.2685 | 0.5368 | 0.2347 | 0.0904 | 0.2268 | 0.4546 | 0.2661 | 0.4332 | 0.4445 | 0.1645 | 0.3918 | 0.6802 | 0.5394 | 0.6811 | 0.1894 | 0.4481 | 0.1913 | 0.3754 | 0.1505 | 0.3585 | 0.2718 | 0.3596 | | 0.9653 | 62.0 | 6634 | 1.2668 | 0.2718 | 0.5518 | 0.2315 | 0.1013 | 0.2221 | 0.4447 | 0.2714 | 0.4392 | 0.4524 | 0.2027 | 0.3974 | 0.6521 | 0.5416 | 0.6833 | 0.1993 | 0.4418 | 0.193 | 0.4018 | 0.1515 | 0.3646 | 0.2733 | 0.3707 | | 0.9367 | 63.0 | 6741 | 1.2756 | 0.2678 | 0.5419 | 0.2284 | 0.0915 | 0.2106 | 0.4618 | 0.2696 | 0.4304 | 0.4467 | 0.2066 | 0.3823 | 0.6624 | 0.5449 | 0.6941 | 0.1986 | 0.4418 | 0.1872 | 0.4098 | 0.1375 | 0.32 | 0.2706 | 0.3676 | | 0.9397 | 64.0 | 6848 | 1.2792 | 0.2693 | 0.543 | 0.2349 | 0.1105 | 0.2191 | 0.4305 | 0.2671 | 0.447 | 0.4594 | 0.2058 | 0.4023 | 0.6729 | 0.532 | 0.6856 | 0.1938 | 0.4544 | 0.1966 | 0.3942 | 0.1431 | 0.3892 | 0.2811 | 0.3738 | | 0.9362 | 65.0 | 6955 | 1.2815 | 0.2622 | 0.5268 | 0.2297 | 0.0993 | 0.2152 | 0.4254 | 0.2702 | 0.4417 | 0.4564 | 0.1804 | 0.404 | 0.6691 | 0.5354 | 0.6815 | 0.1833 | 0.4608 | 0.1828 | 0.3763 | 0.1314 | 0.3831 | 0.2783 | 0.3804 | | 0.9256 | 66.0 | 7062 | 1.2706 | 0.2785 | 0.5545 | 0.2555 | 0.11 | 0.2213 | 0.4632 | 0.2807 | 0.4514 | 0.4665 | 0.2383 | 0.4052 | 0.6819 | 0.5466 | 0.6977 | 0.2198 | 0.4532 | 0.1932 | 0.4076 | 0.1533 | 0.3938 | 0.2795 | 0.38 | | 0.9251 | 67.0 | 7169 | 1.2693 | 0.2779 | 0.5672 | 0.2466 | 0.1024 | 0.2213 | 0.4665 | 0.2746 | 0.4364 | 0.4467 | 0.1908 | 0.3882 | 0.6738 | 0.5463 | 0.6896 | 0.2235 | 0.4329 | 0.1956 | 0.3853 | 0.1504 | 0.3508 | 0.2735 | 0.3747 | | 0.9284 | 68.0 | 7276 | 1.2592 | 0.2784 | 0.5506 | 0.2458 | 0.0927 | 0.2304 | 0.483 | 0.2735 | 0.4356 | 0.4443 | 0.1732 | 0.3946 | 0.6872 | 0.5438 | 0.6896 | 0.2034 | 0.4038 | 0.2033 | 0.3951 | 0.1668 | 0.3585 | 0.2749 | 0.3747 | | 0.9217 | 69.0 | 7383 | 1.2788 | 0.2751 | 0.5401 | 0.2457 | 0.0984 | 0.223 | 0.4747 | 0.2762 | 0.4435 | 0.4547 | 0.2076 | 0.4048 | 0.6831 | 0.5437 | 0.6896 | 0.2062 | 0.4392 | 0.1936 | 0.3888 | 0.1552 | 0.3738 | 0.277 | 0.3818 | | 0.8987 | 70.0 | 7490 | 1.2390 | 0.2757 | 0.5535 | 0.2487 | 0.1123 | 0.2263 | 0.4587 | 0.2759 | 0.4482 | 0.4603 | 0.2193 | 0.4165 | 0.6799 | 0.5415 | 0.6901 | 0.1939 | 0.4532 | 0.2053 | 0.4116 | 0.1652 | 0.3646 | 0.2727 | 0.3822 | | 0.8797 | 71.0 | 7597 | 1.2614 | 0.2749 | 0.5451 | 0.2406 | 0.1055 | 0.2251 | 0.4616 | 0.275 | 0.446 | 0.459 | 0.1965 | 0.4088 | 0.6842 | 0.5438 | 0.6928 | 0.19 | 0.4506 | 0.2133 | 0.4058 | 0.1504 | 0.3723 | 0.2768 | 0.3733 | | 0.8864 | 72.0 | 7704 | 1.2601 | 0.2813 | 0.5445 | 0.2524 | 0.1029 | 0.2261 | 0.4549 | 0.28 | 0.4474 | 0.4608 | 0.2296 | 0.4052 | 0.6677 | 0.5449 | 0.6878 | 0.2062 | 0.4342 | 0.2122 | 0.4196 | 0.1731 | 0.3877 | 0.2701 | 0.3747 | | 0.8739 | 73.0 | 7811 | 1.2542 | 0.2809 | 0.556 | 0.2491 | 0.1104 | 0.2179 | 0.4702 | 0.2839 | 0.4483 | 0.4652 | 0.2438 | 0.4059 | 0.6685 | 0.5439 | 0.6847 | 0.1978 | 0.4468 | 0.2114 | 0.429 | 0.1767 | 0.3892 | 0.2746 | 0.376 | | 0.8851 | 74.0 | 7918 | 1.3047 | 0.271 | 0.5478 | 0.2363 | 0.1153 | 0.2078 | 0.4572 | 0.2842 | 0.4438 | 0.4542 | 0.2441 | 0.3865 | 0.665 | 0.521 | 0.6676 | 0.1916 | 0.4203 | 0.214 | 0.4286 | 0.1578 | 0.3892 | 0.2704 | 0.3653 | | 0.8902 | 75.0 | 8025 | 1.2808 | 0.2709 | 0.5429 | 0.2351 | 0.101 | 0.2174 | 0.4628 | 0.2806 | 0.4448 | 0.4583 | 0.2163 | 0.397 | 0.6919 | 0.5314 | 0.677 | 0.1856 | 0.4342 | 0.1961 | 0.4036 | 0.1625 | 0.3969 | 0.2791 | 0.38 | | 0.8874 | 76.0 | 8132 | 1.2806 | 0.2812 | 0.5664 | 0.2465 | 0.113 | 0.2339 | 0.4718 | 0.2864 | 0.4519 | 0.4645 | 0.2363 | 0.4141 | 0.688 | 0.5339 | 0.6869 | 0.194 | 0.4481 | 0.208 | 0.4228 | 0.1807 | 0.3738 | 0.2897 | 0.3907 | | 0.8809 | 77.0 | 8239 | 1.2476 | 0.2807 | 0.5632 | 0.2575 | 0.1312 | 0.2228 | 0.4686 | 0.2888 | 0.4592 | 0.4738 | 0.2428 | 0.4097 | 0.7011 | 0.5351 | 0.6914 | 0.1972 | 0.4582 | 0.2129 | 0.4259 | 0.1747 | 0.4092 | 0.2834 | 0.384 | | 0.856 | 78.0 | 8346 | 1.2580 | 0.2878 | 0.5675 | 0.2528 | 0.1216 | 0.2298 | 0.4649 | 0.2918 | 0.4565 | 0.4712 | 0.2357 | 0.4122 | 0.6921 | 0.5297 | 0.6923 | 0.2236 | 0.4456 | 0.216 | 0.4384 | 0.1805 | 0.3985 | 0.2891 | 0.3813 | | 0.8509 | 79.0 | 8453 | 1.2741 | 0.2811 | 0.5657 | 0.2445 | 0.131 | 0.2218 | 0.4685 | 0.2832 | 0.4469 | 0.4609 | 0.2213 | 0.3994 | 0.6813 | 0.5281 | 0.6842 | 0.2263 | 0.4506 | 0.205 | 0.4179 | 0.1643 | 0.3708 | 0.2817 | 0.3809 | | 0.88 | 80.0 | 8560 | 1.2556 | 0.2818 | 0.5628 | 0.2442 | 0.1218 | 0.2313 | 0.4529 | 0.2848 | 0.4525 | 0.4659 | 0.2211 | 0.4178 | 0.68 | 0.536 | 0.6878 | 0.2158 | 0.438 | 0.2157 | 0.4348 | 0.1503 | 0.3831 | 0.2913 | 0.3858 | | 0.8739 | 81.0 | 8667 | 1.2754 | 0.2797 | 0.5595 | 0.2432 | 0.1169 | 0.2255 | 0.4812 | 0.2791 | 0.4481 | 0.4632 | 0.2257 | 0.3972 | 0.6992 | 0.5369 | 0.6901 | 0.2077 | 0.4544 | 0.2024 | 0.4076 | 0.1652 | 0.3846 | 0.2864 | 0.3791 | | 0.8604 | 82.0 | 8774 | 1.2531 | 0.2775 | 0.5547 | 0.2411 | 0.121 | 0.2204 | 0.4861 | 0.2823 | 0.4461 | 0.4577 | 0.2244 | 0.3945 | 0.6931 | 0.5363 | 0.6824 | 0.2038 | 0.4215 | 0.1948 | 0.4085 | 0.1749 | 0.4062 | 0.2777 | 0.3698 | | 0.8504 | 83.0 | 8881 | 1.2448 | 0.2837 | 0.5589 | 0.2539 | 0.1182 | 0.2199 | 0.4942 | 0.2894 | 0.4561 | 0.4682 | 0.2282 | 0.4074 | 0.6941 | 0.5404 | 0.6946 | 0.2168 | 0.4646 | 0.2078 | 0.4174 | 0.1691 | 0.38 | 0.2844 | 0.3844 | | 0.8394 | 84.0 | 8988 | 1.2491 | 0.2766 | 0.5478 | 0.2385 | 0.1219 | 0.2293 | 0.4674 | 0.2776 | 0.4501 | 0.4611 | 0.2267 | 0.4055 | 0.6803 | 0.544 | 0.7023 | 0.189 | 0.4203 | 0.2092 | 0.4067 | 0.164 | 0.4031 | 0.2769 | 0.3733 | | 0.8257 | 85.0 | 9095 | 1.2410 | 0.2805 | 0.5591 | 0.2415 | 0.1251 | 0.2292 | 0.471 | 0.2783 | 0.4592 | 0.4689 | 0.2249 | 0.4214 | 0.6866 | 0.5408 | 0.6973 | 0.1995 | 0.4608 | 0.2056 | 0.408 | 0.1613 | 0.3831 | 0.295 | 0.3951 | | 0.8416 | 86.0 | 9202 | 1.2492 | 0.2868 | 0.5613 | 0.2512 | 0.1136 | 0.2345 | 0.4828 | 0.2807 | 0.455 | 0.4644 | 0.2239 | 0.4123 | 0.6914 | 0.5519 | 0.6905 | 0.209 | 0.4443 | 0.2155 | 0.4143 | 0.1671 | 0.3815 | 0.2907 | 0.3916 | | 0.8395 | 87.0 | 9309 | 1.2354 | 0.2863 | 0.5545 | 0.2498 | 0.1311 | 0.2408 | 0.4847 | 0.2865 | 0.4603 | 0.4709 | 0.2264 | 0.4195 | 0.6952 | 0.538 | 0.6914 | 0.2185 | 0.4734 | 0.2144 | 0.4152 | 0.1696 | 0.3908 | 0.2908 | 0.3836 | | 0.831 | 88.0 | 9416 | 1.2456 | 0.2801 | 0.5556 | 0.2431 | 0.122 | 0.2307 | 0.4571 | 0.2787 | 0.4571 | 0.4697 | 0.2377 | 0.4118 | 0.6943 | 0.5398 | 0.6946 | 0.1915 | 0.4418 | 0.2088 | 0.4223 | 0.1688 | 0.4062 | 0.2916 | 0.3836 | | 0.8135 | 89.0 | 9523 | 1.2334 | 0.2837 | 0.5639 | 0.2552 | 0.1354 | 0.2321 | 0.4607 | 0.2801 | 0.4679 | 0.4786 | 0.2477 | 0.4247 | 0.7046 | 0.5416 | 0.6937 | 0.2148 | 0.4747 | 0.2154 | 0.4308 | 0.156 | 0.4077 | 0.2908 | 0.3862 | | 0.8169 | 90.0 | 9630 | 1.2384 | 0.2866 | 0.5663 | 0.2508 | 0.1454 | 0.237 | 0.4681 | 0.2799 | 0.4634 | 0.4749 | 0.2483 | 0.4152 | 0.6961 | 0.5472 | 0.6928 | 0.2252 | 0.4709 | 0.2082 | 0.4174 | 0.1521 | 0.4 | 0.3003 | 0.3933 | | 0.8085 | 91.0 | 9737 | 1.2410 | 0.2913 | 0.5751 | 0.2568 | 0.1375 | 0.2386 | 0.476 | 0.2839 | 0.4661 | 0.4805 | 0.2439 | 0.4281 | 0.6948 | 0.5469 | 0.6982 | 0.2298 | 0.4848 | 0.2134 | 0.4183 | 0.1742 | 0.4046 | 0.2924 | 0.3964 | | 0.8272 | 92.0 | 9844 | 1.2486 | 0.29 | 0.5747 | 0.26 | 0.1216 | 0.2434 | 0.4781 | 0.2828 | 0.4639 | 0.4757 | 0.2383 | 0.4208 | 0.6864 | 0.545 | 0.6959 | 0.219 | 0.4722 | 0.2112 | 0.4098 | 0.183 | 0.4062 | 0.2915 | 0.3947 | | 0.7949 | 93.0 | 9951 | 1.2430 | 0.2786 | 0.5565 | 0.2355 | 0.1252 | 0.2239 | 0.4703 | 0.2809 | 0.4591 | 0.4724 | 0.2344 | 0.4165 | 0.7 | 0.5329 | 0.6838 | 0.2075 | 0.4696 | 0.2052 | 0.4219 | 0.162 | 0.3954 | 0.2852 | 0.3911 | | 0.8009 | 94.0 | 10058 | 1.2419 | 0.2866 | 0.5667 | 0.2557 | 0.1403 | 0.2326 | 0.4687 | 0.2796 | 0.4672 | 0.4787 | 0.24 | 0.4214 | 0.7029 | 0.5401 | 0.6865 | 0.2134 | 0.4848 | 0.2098 | 0.4192 | 0.1778 | 0.4046 | 0.2917 | 0.3982 | | 0.8088 | 95.0 | 10165 | 1.2328 | 0.2838 | 0.57 | 0.249 | 0.1482 | 0.2328 | 0.4686 | 0.2851 | 0.4616 | 0.4754 | 0.2413 | 0.4191 | 0.6996 | 0.5383 | 0.6847 | 0.2113 | 0.4797 | 0.2114 | 0.4183 | 0.1672 | 0.3954 | 0.2907 | 0.3987 | | 0.8113 | 96.0 | 10272 | 1.2335 | 0.2852 | 0.5734 | 0.2426 | 0.1313 | 0.2336 | 0.4788 | 0.2848 | 0.4614 | 0.4742 | 0.2416 | 0.4169 | 0.6924 | 0.5401 | 0.686 | 0.2135 | 0.481 | 0.2115 | 0.4121 | 0.1693 | 0.3954 | 0.2914 | 0.3964 | | 0.7898 | 97.0 | 10379 | 1.2327 | 0.2867 | 0.5712 | 0.2504 | 0.1332 | 0.2335 | 0.4682 | 0.288 | 0.4648 | 0.4785 | 0.2391 | 0.421 | 0.7037 | 0.5415 | 0.6892 | 0.2193 | 0.4949 | 0.2123 | 0.4112 | 0.1712 | 0.4015 | 0.2891 | 0.3956 | | 0.8043 | 98.0 | 10486 | 1.2280 | 0.288 | 0.5693 | 0.2456 | 0.1247 | 0.2341 | 0.4886 | 0.29 | 0.4667 | 0.4785 | 0.2389 | 0.4194 | 0.711 | 0.5462 | 0.6914 | 0.2148 | 0.4785 | 0.2133 | 0.4152 | 0.1746 | 0.4108 | 0.2908 | 0.3964 | | 0.8017 | 99.0 | 10593 | 1.2349 | 0.2863 | 0.5661 | 0.2508 | 0.123 | 0.2321 | 0.4837 | 0.2882 | 0.4639 | 0.4755 | 0.2338 | 0.4168 | 0.7093 | 0.5448 | 0.691 | 0.2127 | 0.4722 | 0.2136 | 0.4103 | 0.1744 | 0.4123 | 0.286 | 0.3916 | | 0.7948 | 100.0 | 10700 | 1.2371 | 0.2873 | 0.5678 | 0.2501 | 0.126 | 0.2327 | 0.4873 | 0.2843 | 0.4643 | 0.4762 | 0.2338 | 0.4167 | 0.7114 | 0.5461 | 0.6932 | 0.2167 | 0.4785 | 0.2135 | 0.4094 | 0.173 | 0.4092 | 0.2871 | 0.3907 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session2

# tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session2 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]

SIS-2024-spring/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

ChiJuiChen/Lab8_DETR_BOAT

# Lab8_DETR_BOAT This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.0+cu118 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

Wellyowo/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

uwwee/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

sunfu-chou/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

leowang707/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

cj94/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

wennnny/detr-resnet-50-finetuned-real-boat-dataset

# detr-resnet-50-finetuned-real-boat-dataset This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.2 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

[ "ballonboat", "bigboat", "boat", "jetski", "katamaran", "sailboat", "smallboat", "speedboat", "wam_v" ]

nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session6

# tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session6 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]

Dilipan/detr-finetuned-edzola-form-section

# Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8" ]

zajko194/detr-resnet-50_finetuned_cppe5

# detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

schoonhovenra/20240502

# 20240502 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 400 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1

[ "wheel_front_left", "wheel_front_right", "wheel_rear_left", "wheel_rear_right", "head_light_left", "head_light_right", "rear_light_left", "rear_light_right", "third_brake_light", "mirror_left", "mirror_right", "brand_badge", "brand_text", "engine_type", "type_badge", "filler_cap", "tow_bar", "roof_rack", "exhaust", "panorama_roof", "antenna", "spare_wheel", "parking_sensor", "license_plate_holder", "fog_light_front_left", "fog_light_front_right", "model_text", "driving_assistance_grill", "driving_assistance_windshield", "license_plate", "car", "backseat", "driving wheel", "dashboard", "center console", "window front left", "window front right", "back mirror" ]

nsugianto/detr-resnet50_finetuned_mstbltransf_lsdocelementdetv1type5_session1

# detr-resnet50_finetuned_mstbltransf_lsdocelementdetv1type5_session1 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1

[ "leftcap_rightval", "topcap_bottomval", "paragraphtext", "tablenogroup", "tablewithgroup" ]

jweaths/detr-resnet-50_finetuned_cppe5

# detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

[ "coverall", "face_shield", "gloves", "goggles", "mask" ]

mserrasa/yolo_finetuned_VinBigData

# yolo_finetuned_VinBigData This model is a fine-tuned version of [mserrasa/yolo_finetuned_VinBigData](https://huggingface.co/mserrasa/yolo_finetuned_VinBigData) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

[ "aortic enlargement", "cardiomegaly", "nodule/mass", "pleural thickening", "pulmonary fibrosis" ]

mserrasa/yolos_finetuned_VinBigData

# yolos_finetuned_VinBigData This model is a fine-tuned version of [mserrasa/yolos_finetuned_VinBigData](https://huggingface.co/mserrasa/yolos_finetuned_VinBigData) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

[ "aortic enlargement", "cardiomegaly", "nodule/mass", "pleural thickening", "pulmonary fibrosis" ]