model_id
stringlengths 9
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|---|---|---|
goodjin/furniture_use_data_finetuning |
<!-- 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. -->
# furniture_use_data_finetuning
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: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"furniture",
"chair",
"sofa",
"table"
] |
kimteawan/furniture_use_data_finetuning |
<!-- 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. -->
# furniture_use_data_finetuning
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: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"furniture",
"chair",
"sofa",
"table"
] |
wnsdud030415/use_data_finetuning |
<!-- 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. -->
# use_data_finetuning
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: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"furniture",
"chair",
"sofa",
"table"
] |
dia218/use_data_finetuning |
<!-- 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. -->
# use_data_finetuning
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: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"furniture",
"chair",
"sofa",
"table"
] |
rdpatilds/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 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: 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 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
seongmin31/furniture-ngpea_use_data_finetuning |
<!-- 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. -->
# furniture-ngpea_use_data_finetuning
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: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"furniture",
"chair",
"sofa",
"table"
] |
aParadigmP/table-transformer-detection-custom-ale |
# Table Transformer (fine-tuned for Table Detection)
Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer).
Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
## Usage
You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info. | [
"table",
"table rotated"
] |
apjanco/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 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"municipio",
"departamento"
] |
kariver/detr-resnet-101_finetuned_food-roboflow |
<!-- 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-101_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) 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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
SukhmanVig/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 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: 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: 150
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
kariver/detr-resnet-50_adamw_hf_finetuned_food-roboflow |
<!-- 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_adamw_hf_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5869
## 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: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.0037 | 1.52 | 50 | 5.2294 |
| 4.9032 | 3.03 | 100 | 4.1657 |
| 3.9974 | 4.55 | 150 | 3.3157 |
| 3.2722 | 6.06 | 200 | 2.9964 |
| 3.0497 | 7.58 | 250 | 2.6456 |
| 2.8708 | 9.09 | 300 | 2.7218 |
| 2.7799 | 10.61 | 350 | 2.6176 |
| 2.7701 | 12.12 | 400 | 2.6078 |
| 2.677 | 13.64 | 450 | 2.5869 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
akshit8/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 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: 1e-05
- train_batch_size: 5
- 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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
bigmouse/Fashionpedia57000 | https://github.com/KMnP/fashionpedia-api
Trained "detection-datasets/fashionpedia" hugging face dataset with the following params:
training_args = TrainingArguments(
output_dir="trained_model",
per_device_train_batch_size=8,
num_train_epochs=10,
fp16=False,
save_steps=200,
logging_first_step=True,
logging_steps=10,
learning_rate=1e-5,
weight_decay=1e-4,
save_total_limit=2,
remove_unused_columns=False,
push_to_hub=False,
) | [
"shirt, blouse",
"top, t-shirt, sweatshirt",
"sweater",
"cardigan",
"jacket",
"vest",
"pants",
"shorts",
"skirt",
"coat",
"dress",
"jumpsuit",
"cape",
"glasses",
"hat",
"headband, head covering, hair accessory",
"tie",
"glove",
"watch",
"belt",
"leg warmer",
"tights, stockings",
"sock",
"shoe",
"bag, wallet",
"scarf",
"umbrella",
"hood",
"collar",
"lapel",
"epaulette",
"sleeve",
"pocket",
"neckline",
"buckle",
"zipper",
"applique",
"bead",
"bow",
"flower",
"fringe",
"ribbon",
"rivet",
"ruffle",
"sequin",
"tassel"
] |
kariver/detr-resnet-50_adafactor_finetuned_food-roboflow |
<!-- 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_adafactor_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5062
## 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: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.4038 | 1.52 | 50 | 5.7574 |
| 5.3942 | 3.03 | 100 | 4.7285 |
| 4.5081 | 4.55 | 150 | 3.8652 |
| 3.6495 | 6.06 | 200 | 3.0781 |
| 3.2792 | 7.58 | 250 | 2.8378 |
| 3.0286 | 9.09 | 300 | 2.6613 |
| 2.948 | 10.61 | 350 | 2.6172 |
| 2.8826 | 12.12 | 400 | 2.5483 |
| 2.7976 | 13.64 | 450 | 2.5062 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-50_adamw_torch_fused_finetuned_food-roboflow |
<!-- 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_adamw_torch_fused_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4652
## 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: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3234 | 1.52 | 50 | 5.6491 |
| 5.275 | 3.03 | 100 | 4.4271 |
| 4.192 | 4.55 | 150 | 3.2741 |
| 3.2675 | 6.06 | 200 | 2.7332 |
| 3.0463 | 7.58 | 250 | 2.6176 |
| 2.8906 | 9.09 | 300 | 2.5238 |
| 2.8289 | 10.61 | 350 | 2.5048 |
| 2.782 | 12.12 | 400 | 2.5033 |
| 2.7336 | 13.64 | 450 | 2.4652 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-50_adamw_torch_fused_adam_beta1-0_8_finetuned_food-roboflow |
<!-- 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_adamw_torch_fused_adam_beta1-0_8_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8078
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.8,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.6897 | 1.52 | 50 | 6.2985 |
| 5.5704 | 3.03 | 100 | 5.1916 |
| 4.3645 | 4.55 | 150 | 4.0882 |
| 3.4115 | 6.06 | 200 | 3.3534 |
| 3.0109 | 7.58 | 250 | 3.0666 |
| 2.8836 | 9.09 | 300 | 3.0176 |
| 2.7564 | 10.61 | 350 | 2.9294 |
| 2.7007 | 12.12 | 400 | 2.9228 |
| 2.7032 | 13.64 | 450 | 2.8650 |
| 2.6248 | 15.15 | 500 | 2.8833 |
| 2.5995 | 16.67 | 550 | 2.8085 |
| 2.5616 | 18.18 | 600 | 2.7843 |
| 2.5582 | 19.7 | 650 | 2.8078 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kts1234/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 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: 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 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"0",
"1",
"2",
"3",
"5",
"6",
"7"
] |
daniel-itg/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 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: 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 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
piecurus/detr-resnet-50_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_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.
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
kariver/conditional-detr-resnet-50_adamw_hf_finetuned_food-roboflow |
<!-- 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. -->
# conditional-detr-resnet-50_adamw_hf_finetuned_food-roboflow
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 10.2661
## 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4448.7409 | 0.77 | 50 | 2784.9878 |
| 2312.7831 | 1.54 | 100 | 1275.4084 |
| 1139.5262 | 2.31 | 150 | 825.0410 |
| 857.6314 | 3.08 | 200 | 626.2924 |
| 630.5604 | 3.85 | 250 | 492.9028 |
| 518.8461 | 4.62 | 300 | 391.5704 |
| 418.3785 | 5.38 | 350 | 312.8008 |
| 331.9224 | 6.15 | 400 | 250.7763 |
| 261.2112 | 6.92 | 450 | 201.8681 |
| 207.421 | 7.69 | 500 | 163.3361 |
| 172.0157 | 8.46 | 550 | 132.3763 |
| 140.2059 | 9.23 | 600 | 108.1960 |
| 110.3696 | 10.0 | 650 | 89.2497 |
| 95.9428 | 10.77 | 700 | 73.2426 |
| 78.812 | 11.54 | 750 | 61.4362 |
| 64.1493 | 12.31 | 800 | 51.2599 |
| 56.184 | 13.08 | 850 | 43.4741 |
| 46.7644 | 13.85 | 900 | 37.4028 |
| 38.1726 | 14.62 | 950 | 32.6764 |
| 34.7277 | 15.38 | 1000 | 28.1207 |
| 29.9978 | 16.15 | 1050 | 25.0045 |
| 27.5957 | 16.92 | 1100 | 22.3012 |
| 23.6549 | 17.69 | 1150 | 19.9766 |
| 21.4961 | 18.46 | 1200 | 18.2427 |
| 19.3312 | 19.23 | 1250 | 16.8829 |
| 17.8215 | 20.0 | 1300 | 15.5794 |
| 16.2877 | 20.77 | 1350 | 14.5258 |
| 16.0017 | 21.54 | 1400 | 13.5888 |
| 14.6977 | 22.31 | 1450 | 13.0312 |
| 13.9111 | 23.08 | 1500 | 12.4389 |
| 13.5826 | 23.85 | 1550 | 11.8718 |
| 12.5621 | 24.62 | 1600 | 11.6370 |
| 12.1993 | 25.38 | 1650 | 11.1523 |
| 12.2491 | 26.15 | 1700 | 10.8586 |
| 11.5879 | 26.92 | 1750 | 10.6795 |
| 11.4736 | 27.69 | 1800 | 10.5273 |
| 11.5405 | 28.46 | 1850 | 10.4190 |
| 11.5894 | 29.23 | 1900 | 10.3225 |
| 11.0308 | 30.0 | 1950 | 10.2661 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-50_sgd_finetuned_food-roboflow |
<!-- 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_sgd_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 7.6389
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.2801 | 0.77 | 50 | 7.6418 |
| 7.3084 | 1.54 | 100 | 7.6658 |
| 7.3307 | 2.31 | 150 | 7.5842 |
| 7.287 | 3.08 | 200 | 7.6073 |
| 7.3372 | 3.85 | 250 | 7.7152 |
| 7.3168 | 4.62 | 300 | 7.6034 |
| 7.3691 | 5.38 | 350 | 7.6833 |
| 7.2893 | 6.15 | 400 | 7.6372 |
| 7.3304 | 6.92 | 450 | 7.6150 |
| 7.2394 | 7.69 | 500 | 7.6044 |
| 7.4489 | 8.46 | 550 | 7.6391 |
| 7.1353 | 9.23 | 600 | 7.7143 |
| 7.3786 | 10.0 | 650 | 7.6010 |
| 7.289 | 10.77 | 700 | 7.6191 |
| 7.2943 | 11.54 | 750 | 7.6662 |
| 7.2791 | 12.31 | 800 | 7.6043 |
| 7.2665 | 13.08 | 850 | 7.6339 |
| 7.2157 | 13.85 | 900 | 7.6102 |
| 7.309 | 14.62 | 950 | 7.6389 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-50_adamw_hf_epoch_finetuned_food-roboflow |
<!-- 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_adamw_hf_epoch_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9613
## 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: 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 33 | 3.4739 |
| 4.5266 | 2.0 | 66 | 3.4064 |
| 4.5266 | 3.0 | 99 | 3.2739 |
| 3.2133 | 4.0 | 132 | 3.3611 |
| 3.3196 | 5.0 | 165 | 3.1082 |
| 3.3196 | 6.0 | 198 | 3.2548 |
| 2.9882 | 7.0 | 231 | 3.2691 |
| 3.1559 | 8.0 | 264 | 3.2925 |
| 3.1559 | 9.0 | 297 | 3.1214 |
| 2.9855 | 10.0 | 330 | 3.0595 |
| 2.8269 | 11.0 | 363 | 3.0367 |
| 2.8269 | 12.0 | 396 | 3.0276 |
| 2.7938 | 13.0 | 429 | 3.0012 |
| 2.7678 | 14.0 | 462 | 2.9559 |
| 2.7678 | 15.0 | 495 | 2.9613 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
AntonioZhang/Candy_detector |
<!-- 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. -->
# Candy_detector
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: 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: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"owl",
"unicorn_head",
"unicorn_whole",
"moon"
] |
kariver/detr-resnet-101_sgd_finetuned_food-roboflow |
<!-- 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-101_sgd_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9270
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.7367 | 0.77 | 50 | 6.2388 |
| 5.9103 | 1.54 | 100 | 5.4768 |
| 4.8918 | 2.31 | 150 | 4.3742 |
| 3.827 | 3.08 | 200 | 3.8198 |
| 3.4129 | 3.85 | 250 | 3.3190 |
| 3.0329 | 4.62 | 300 | 3.1967 |
| 2.8295 | 5.38 | 350 | 3.1841 |
| 2.8599 | 6.15 | 400 | 3.1155 |
| 2.7862 | 6.92 | 450 | 3.1008 |
| 2.7885 | 7.69 | 500 | 3.0490 |
| 2.6737 | 8.46 | 550 | 3.0872 |
| 2.7679 | 9.23 | 600 | 3.0429 |
| 2.6093 | 10.0 | 650 | 2.9775 |
| 2.6316 | 10.77 | 700 | 3.0016 |
| 2.5801 | 11.54 | 750 | 2.9701 |
| 2.6009 | 12.31 | 800 | 2.8919 |
| 2.5841 | 13.08 | 850 | 2.9398 |
| 2.5394 | 13.85 | 900 | 2.9266 |
| 2.5189 | 14.62 | 950 | 2.9270 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-101_adamw_torch_finetuned_food-roboflow |
<!-- 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-101_adamw_torch_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0549
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.854 | 0.77 | 50 | 6.6564 |
| 5.8031 | 1.54 | 100 | 5.7440 |
| 5.0476 | 2.31 | 150 | 4.8884 |
| 4.1629 | 3.08 | 200 | 3.9522 |
| 3.4843 | 3.85 | 250 | 3.5612 |
| 3.0905 | 4.62 | 300 | 3.7328 |
| 3.0965 | 5.38 | 350 | 3.3028 |
| 2.8764 | 6.15 | 400 | 3.1671 |
| 2.8236 | 6.92 | 450 | 3.2082 |
| 2.8467 | 7.69 | 500 | 3.0500 |
| 2.6769 | 8.46 | 550 | 3.0538 |
| 2.7194 | 9.23 | 600 | 3.0982 |
| 2.6311 | 10.0 | 650 | 3.0520 |
| 2.6772 | 10.77 | 700 | 2.9950 |
| 2.577 | 11.54 | 750 | 3.0134 |
| 2.6274 | 12.31 | 800 | 3.0523 |
| 2.597 | 13.08 | 850 | 3.0120 |
| 2.56 | 13.85 | 900 | 2.9795 |
| 2.5803 | 14.62 | 950 | 3.0549 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-101_adafactor_finetuned_food-roboflow |
<!-- 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-101_adafactor_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1003
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5311 | 0.77 | 50 | 6.4188 |
| 5.6597 | 1.54 | 100 | 5.8132 |
| 5.1568 | 2.31 | 150 | 5.1265 |
| 4.4672 | 3.08 | 200 | 4.3264 |
| 3.8033 | 3.85 | 250 | 3.9444 |
| 3.2053 | 4.62 | 300 | 3.6319 |
| 3.127 | 5.38 | 350 | 3.3079 |
| 2.9082 | 6.15 | 400 | 3.1760 |
| 2.8546 | 6.92 | 450 | 3.1659 |
| 2.8393 | 7.69 | 500 | 3.0233 |
| 2.6973 | 8.46 | 550 | 3.0817 |
| 2.7047 | 9.23 | 600 | 3.0849 |
| 2.673 | 10.0 | 650 | 3.1122 |
| 2.6781 | 10.77 | 700 | 3.0709 |
| 2.5901 | 11.54 | 750 | 3.0382 |
| 2.6568 | 12.31 | 800 | 3.0246 |
| 2.5929 | 13.08 | 850 | 3.0384 |
| 2.5709 | 13.85 | 900 | 2.9862 |
| 2.5784 | 14.62 | 950 | 3.1003 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kariver/detr-resnet-101_adagrad_finetuned_food-roboflow |
<!-- 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-101_adagrad_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9027
## 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: 4
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.708 | 0.77 | 50 | 6.6090 |
| 6.2622 | 1.54 | 100 | 6.3841 |
| 6.1083 | 2.31 | 150 | 6.3279 |
| 6.1302 | 3.08 | 200 | 6.1396 |
| 6.0668 | 3.85 | 250 | 6.1742 |
| 5.9788 | 4.62 | 300 | 6.1002 |
| 5.9065 | 5.38 | 350 | 6.0478 |
| 5.8597 | 6.15 | 400 | 5.9363 |
| 5.8188 | 6.92 | 450 | 5.9914 |
| 5.7599 | 7.69 | 500 | 5.8783 |
| 5.6732 | 8.46 | 550 | 5.9710 |
| 5.743 | 9.23 | 600 | 6.0130 |
| 5.6341 | 10.0 | 650 | 5.8789 |
| 5.6265 | 10.77 | 700 | 5.8644 |
| 5.7164 | 11.54 | 750 | 5.9142 |
| 5.6104 | 12.31 | 800 | 5.9677 |
| 5.6572 | 13.08 | 850 | 5.8372 |
| 5.7094 | 13.85 | 900 | 5.8269 |
| 5.7456 | 14.62 | 950 | 5.9027 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
1aurent/REVA-QCAV |
# Model card for REVA-QCAV
A DEtection TRansformer (DETR) model with a ResNet-50 backbone ([facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)) finetuned on a custom photogrammetry calibration sphere dataset.
## Model Usage
### Object Detection (using `transformers`)
```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from huggingface_hub import hf_hub_download
from PIL import Image
import torch
# download example image
img_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="examples/chevaux.jpg")
img = Image.open(img_path)
# transform image using image_processor
image_processor = AutoImageProcessor.from_pretrained("1aurent/REVA-QCAV")
data = image_processor(img, return_tensors="pt")
# get outputs from the model
model = AutoModelForObjectDetection.from_pretrained("1aurent/REVA-QCAV")
with torch.no_grad():
output = model(**data)
# use image_processor post processing
img_CHW = torch.tensor([img.height, img.width]).unsqueeze(0)
output_processed = image_processor.post_process_object_detection(output, threshold=0.9, target_sizes=img_CHW)
```
### Object Detection (using `onnxruntime`)
```python
from transformers.models.detr.modeling_detr import DetrObjectDetectionOutput
from transformers import AutoImageProcessor
from huggingface_hub import hf_hub_download
import onnxruntime as ort
from PIL import Image
import torch
# download onnx and start inference session
onnx_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="model.onnx")
session = ort.InferenceSession(onnx_path)
# download example image
img_path = hf_hub_download(repo_id="1aurent/REVA-QCAV", filename="examples/chevaux.jpg")
img = Image.open(img_path)
# transform image using image_processor
image_processor = AutoImageProcessor.from_pretrained("1aurent/REVA-QCAV")
data = image_processor(img, return_tensors="np").data
# get logits and bbox predictions using onnx session
logits, pred_boxes = session.run(
output_names=["logits", "pred_boxes"],
input_feed=data,
)
# wrap outputs inside DetrObjectDetectionOutput
output = DetrObjectDetectionOutput(
logits=torch.tensor(logits),
pred_boxes=torch.tensor(pred_boxes),
)
# use image_processor post processing
img_CHW = torch.tensor([img.height, img.width]).unsqueeze(0)
output_processed = image_processor.post_process_object_detection(output, threshold=0.9, target_sizes=img_CHW)
```
## Citation
```bibtex
@article{reva-qcav,
author = {Laurent Fainsin and Jean MΓ©lou and Lilian Calvet and Antoine Laurent and Axel Carlier and Jean-Denis Durou},
title = {Neural sphere detection in images for lighting calibration},
journal = {QCAV},
year = {2023},
url = {https://hal.science/hal-04160733}
}
```
| [
"matte",
"shiny",
"chrome"
] |
tree12344/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 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: 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: 200
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
SukhmanVig/detr-candy-counter |
<!-- 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-candy-counter
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: 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: 150
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
tree12344/detr-resnet-50_finetuned_candy_detector |
<!-- 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_candy_detector
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: 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: 200
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
kariver/detr-resnet-101_rmsprop_finetuned_food-roboflow |
<!-- 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-101_rmsprop_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9853
## 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: 4
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.8836 | 0.77 | 50 | 4.5609 |
| 3.9193 | 1.54 | 100 | 3.5764 |
| 3.2186 | 2.31 | 150 | 3.2685 |
| 2.8952 | 3.08 | 200 | 3.1941 |
| 2.8565 | 3.85 | 250 | 3.1280 |
| 2.7563 | 4.62 | 300 | 3.0448 |
| 2.668 | 5.38 | 350 | 3.0325 |
| 2.6474 | 6.15 | 400 | 2.9764 |
| 2.6463 | 6.92 | 450 | 2.9752 |
| 2.6616 | 7.69 | 500 | 3.0352 |
| 2.5155 | 8.46 | 550 | 2.9928 |
| 2.5778 | 9.23 | 600 | 2.9603 |
| 2.5876 | 10.0 | 650 | 2.9502 |
| 2.513 | 10.77 | 700 | 3.0151 |
| 2.5598 | 11.54 | 750 | 3.0011 |
| 2.491 | 12.31 | 800 | 3.0134 |
| 2.5103 | 13.08 | 850 | 2.9825 |
| 2.5497 | 13.85 | 900 | 3.0169 |
| 2.5738 | 14.62 | 950 | 2.9853 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"akabare khursani",
"apple",
"artichoke",
"ash gourd -kubhindo-",
"asparagus -kurilo-",
"avocado",
"bacon",
"bamboo shoots -tama-",
"banana",
"beans",
"beaten rice -chiura-",
"beef",
"beetroot",
"bethu ko saag",
"bitter gourd",
"black lentils",
"black beans",
"bottle gourd -lauka-",
"bread",
"brinjal",
"broad beans -bakullo-",
"broccoli",
"buff meat",
"butter",
"cabbage",
"capsicum",
"carrot",
"cassava -ghar tarul-",
"cauliflower",
"chayote-iskus-",
"cheese",
"chicken gizzards",
"chicken",
"chickpeas",
"chili pepper -khursani-",
"chili powder",
"chowmein noodles",
"cinnamon",
"coriander -dhaniya-",
"corn",
"cornflakec",
"crab meat",
"cucumber",
"egg",
"farsi ko munta",
"fiddlehead ferns -niguro-",
"fish",
"garden peas",
"garden cress-chamsur ko saag-",
"garlic",
"ginger",
"green brinjal",
"green lentils",
"green mint -pudina-",
"green peas",
"green soyabean -hariyo bhatmas-",
"gundruk",
"ham",
"ice",
"jack fruit",
"ketchup",
"lapsi -nepali hog plum-",
"lemon -nimbu-",
"lime -kagati-",
"long beans -bodi-",
"masyaura",
"milk",
"minced meat",
"moringa leaves -sajyun ko munta-",
"mushroom",
"mutton",
"nutrela -soya chunks-",
"okra -bhindi-",
"olive oil",
"onion leaves",
"onion",
"orange",
"palak -indian spinach-",
"palungo -nepali spinach-",
"paneer",
"papaya",
"pea",
"pear",
"pointed gourd -chuche karela-",
"pork",
"potato",
"pumpkin -farsi-",
"radish",
"rahar ko daal",
"rayo ko saag",
"red beans",
"red lentils",
"rice -chamal-",
"sajjyun -moringa drumsticks-",
"salt",
"sausage",
"snake gourd -chichindo-",
"soy sauce",
"soyabean -bhatmas-",
"sponge gourd -ghiraula-",
"stinging nettle -sisnu-",
"strawberry",
"sugar",
"sweet potato -suthuni-",
"taro leaves -karkalo-",
"taro root-pidalu-",
"thukpa noodles",
"tofu",
"tomato",
"tori ko saag",
"tree tomato -rukh tamatar-",
"turnip",
"wallnut",
"water melon",
"wheat",
"yellow lentils",
"kimchi",
"mayonnaise",
"noodle",
"seaweed"
] |
kts1234/detr-resnet-50_finetuned_candy_data |
<!-- 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_candy_data
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: 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: 400
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
| [
"black_star",
"cat",
"grey_star",
"moon",
"owl",
"unicorn_head",
"unicorn_whole",
"insect"
] |
tengkai/outcome |
<!-- 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. -->
# outcome
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: 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 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
oscarmak/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
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
Jarven/Candy_detector |
<!-- 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. -->
# Candy_detector
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: 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
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
Jarven/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 was trained from scratch 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: 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: 300
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
apjanco/detr-resnet-50_finetuned_ANC_objects |
<!-- 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_ANC_objects
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: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.12.1
| [
"0",
"1"
] |
chenchen96/detr-resnet-50_finetuned_candy_data |
<!-- 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_candy_data
This model was trained from scratch 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: 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: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
kriskrisgz/detr-resnet-50_finetuned_candy_data |
<!-- 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_candy_data
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: 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: 300
### Training results
### Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
NKDE/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 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: 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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
oscarmak/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. -->
# output
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"n/a",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"street sign",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"hat",
"backpack",
"umbrella",
"shoe",
"eye glasses",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"plate",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"mirror",
"dining table",
"window",
"desk",
"toilet",
"door",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"blender",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
] |
ztian2000/detr-resnet-50_finetuned_candy |
<!-- 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_candy
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: 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: 300
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
wusundoma/detr-resnet-50_finetuned_candy_data |
<!-- 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_candy_data
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: 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: 800
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
| [
"black_star",
"cat",
"grey_star",
"moon",
"owl",
"unicorn_head",
"unicorn_whole",
"insect"
] |
nashaatk/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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
MustangV/candy_detector_finetuned |
<!-- 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. -->
# candy_detector_finetuned
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: 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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"black_star",
"cat",
"grey_star",
"insect",
"moon",
"owl",
"unicorn_head",
"unicorn_whole"
] |
yunusskeete/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 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: 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 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
anhtvn/detr_finetuned_lisa |
<!-- 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_finetuned_lisa
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: 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: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| [
"go",
"warning",
"stop"
] |
microsoft/table-transformer-structure-recognition-v1.1-pub |
# Table Transformer (pre-trained for Table Structure Recognition)
Table Transformer (TATR) model trained on PubTables1M. It was introduced in the paper [Aligning benchmark datasets for table structure recognition](https://arxiv.org/abs/2303.00716) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer).
Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
## Usage
You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info. | [
"table",
"table column",
"table row",
"table column header",
"table projected row header",
"table spanning cell"
] |
microsoft/table-transformer-structure-recognition-v1.1-fin |
# Table Transformer (pre-trained for Table Structure Recognition)
Table Transformer (TATR) model trained on FinTabNet.c. It was introduced in the paper [Aligning benchmark datasets for table structure recognition](https://arxiv.org/abs/2303.00716) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer).
Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
## Usage
You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info. | [
"table",
"table column",
"table row",
"table column header",
"table projected row header",
"table spanning cell"
] |
microsoft/table-transformer-structure-recognition-v1.1-all |
# Table Transformer (pre-trained for Table Structure Recognition)
Table Transformer (TATR) model trained on PubTables1M and FinTabNet.c. It was introduced in the paper [Aligning benchmark datasets for table structure recognition](https://arxiv.org/abs/2303.00716) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer).
Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
## Usage
You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info. | [
"table",
"table column",
"table row",
"table column header",
"table projected row header",
"table spanning cell"
] |
pacov3/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 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: 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: 15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
lms7127/table_detr_10ep | μλ λ§ν¬μ λͺ¨λΈμ custom dataλ₯Ό μΆκ°ν΄ λ§λ€μμ΅λλ€.
https://huggingface.co/TahaDouaji/detr-doc-table-detection
μ½λ μμ
metrics:
import os
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
import cv2
from PIL import Image, ImageDraw, ImageFont
model = DetrForObjectDetection.from_pretrained("lms7127/table_detr_10ep")
processor = DetrImageProcessor.from_pretrained("lms7127/table_detr_10ep")
font_path = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
#λ³νν μ΄λ―Έμ§ λͺ©λ‘ λΆλ¬μ€κΈ°
image_path = '/path/to/image'
save_path ="/path/to/save"
img = Image.open(image_path)
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():#μΆκ°νμ΅ λ°©μ§
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
width, height = img.size
postprocessed_outputs = processor.post_process_object_detection(outputs,
target_sizes=[(height, width)],
threshold=0.7)
results = postprocessed_outputs[0]
draw = ImageDraw.Draw(img)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
class_label = model.config.id2label[label.item()]
confidence = round(score.item(), 3)
# Draw rectangle
draw.rectangle(box, outline="red", width = 5)
# Add text
font_size=50
font = ImageFont.truetype(font_path, font_size)
text = f"{class_label}: {confidence}"
text_width, text_height = draw.textsize(text)
text_location = [box[0], box[1] - text_height - 4]
draw.rectangle([text_location[0], text_location[1], text_location[0] + text_width, text_location[1] + text_height], fill="red")
draw.text(text_location, text, fill="white", font=font)
img.save(save_path,"JPEG")
| [
"table",
"table"
] |
lms7127/table_detr_30ep | μλ λ§ν¬μ λͺ¨λΈμ custom dataλ₯Ό μΆκ°ν΄ λ§λ€μμ΅λλ€. https://huggingface.co/TahaDouaji/detr-doc-table-detection
μ½λ μμ
metrics:
import os
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
import cv2
from PIL import Image, ImageDraw, ImageFont
model = DetrForObjectDetection.from_pretrained("lms7127/table_detr_30ep")
processor = DetrImageProcessor.from_pretrained("lms7127/table_detr_30ep")
font_path = os.path.join(cv2.__path__[0],'qt','fonts','DejaVuSans.ttf')
#λ³νν μ΄λ―Έμ§ λͺ©λ‘ λΆλ¬μ€κΈ°
image_path = '/path/to/image'
save_path ="/path/to/save"
img = Image.open(image_path)
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():#μΆκ°νμ΅ λ°©μ§
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.9
width, height = img.size
postprocessed_outputs = processor.post_process_object_detection(outputs,
target_sizes=[(height, width)],
threshold=0.7)
results = postprocessed_outputs[0]
draw = ImageDraw.Draw(img)
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
class_label = model.config.id2label[label.item()]
confidence = round(score.item(), 3)
# Draw rectangle
draw.rectangle(box, outline="red", width = 5)
# Add text
font_size=50
font = ImageFont.truetype(font_path, font_size)
text = f"{class_label}: {confidence}"
text_width, text_height = draw.textsize(text)
text_location = [box[0], box[1] - text_height - 4]
draw.rectangle([text_location[0], text_location[1], text_location[0] + text_width, text_location[1] + text_height], fill="red")
draw.text(text_location, text, fill="white", font=font)
img.save(save_path,"JPEG") | [
"table",
"table"
] |
akashmaggon/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 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: 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
susnato/detr-resnet-50_finetuned_plant_disease_detection_processed |
<!-- 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_plant_disease_detection_processed
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: 0.6402
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2965 | 0.19 | 50 | 4.4784 |
| 4.7649 | 0.38 | 100 | 4.3439 |
| 4.4907 | 0.57 | 150 | 4.0077 |
| 4.3973 | 0.76 | 200 | 3.2143 |
| 3.4084 | 0.95 | 250 | 2.6818 |
| 2.7091 | 1.14 | 300 | 2.3603 |
| 2.4601 | 1.33 | 350 | 1.9004 |
| 2.1096 | 1.52 | 400 | 1.5639 |
| 1.6941 | 1.7 | 450 | 1.3240 |
| 1.4949 | 1.89 | 500 | 1.1247 |
| 1.2246 | 2.08 | 550 | 1.0421 |
| 1.4479 | 2.27 | 600 | 1.1546 |
| 1.1327 | 2.46 | 650 | 1.1098 |
| 1.1184 | 2.65 | 700 | 0.8950 |
| 1.0516 | 2.84 | 750 | 0.8601 |
| 1.2556 | 3.03 | 800 | 0.8575 |
| 1.1216 | 3.22 | 850 | 0.8314 |
| 1.1027 | 3.41 | 900 | 1.0676 |
| 1.0815 | 3.6 | 950 | 0.9716 |
| 1.2254 | 3.79 | 1000 | 1.0091 |
| 0.9896 | 3.98 | 1050 | 0.7600 |
| 1.0736 | 4.17 | 1100 | 0.8907 |
| 1.2462 | 4.36 | 1150 | 0.7506 |
| 0.9959 | 4.55 | 1200 | 0.7623 |
| 1.0895 | 4.73 | 1250 | 0.7570 |
| 1.0736 | 4.92 | 1300 | 0.8248 |
| 1.1015 | 5.11 | 1350 | 0.8682 |
| 1.1423 | 5.3 | 1400 | 0.8340 |
| 1.0906 | 5.49 | 1450 | 0.8372 |
| 0.9333 | 5.68 | 1500 | 0.8420 |
| 1.1347 | 5.87 | 1550 | 0.8718 |
| 0.9407 | 6.06 | 1600 | 0.8270 |
| 0.8138 | 6.25 | 1650 | 0.8241 |
| 0.8731 | 6.44 | 1700 | 0.8013 |
| 1.0146 | 6.63 | 1750 | 0.7704 |
| 0.8847 | 6.82 | 1800 | 0.8885 |
| 1.0283 | 7.01 | 1850 | 0.8804 |
| 1.0359 | 7.2 | 1900 | 0.7907 |
| 0.987 | 7.39 | 1950 | 0.7997 |
| 1.0279 | 7.58 | 2000 | 0.9095 |
| 0.9027 | 7.77 | 2050 | 0.6823 |
| 0.927 | 7.95 | 2100 | 0.6728 |
| 1.0499 | 8.14 | 2150 | 0.6537 |
| 0.9774 | 8.33 | 2200 | 0.6455 |
| 0.9171 | 8.52 | 2250 | 0.6456 |
| 1.0002 | 8.71 | 2300 | 0.6723 |
| 0.9052 | 8.9 | 2350 | 0.6554 |
| 0.9029 | 9.09 | 2400 | 0.7272 |
| 1.0247 | 9.28 | 2450 | 0.6997 |
| 0.8296 | 9.47 | 2500 | 0.6661 |
| 1.0659 | 9.66 | 2550 | 0.7914 |
| 1.0226 | 9.85 | 2600 | 0.7823 |
| 0.9419 | 10.04 | 2650 | 0.7709 |
| 0.9008 | 10.23 | 2700 | 0.8114 |
| 0.826 | 10.42 | 2750 | 0.7042 |
| 0.7957 | 10.61 | 2800 | 0.7764 |
| 1.0086 | 10.8 | 2850 | 0.8362 |
| 1.0076 | 10.98 | 2900 | 0.8048 |
| 0.9613 | 11.17 | 2950 | 0.6945 |
| 0.9155 | 11.36 | 3000 | 0.7011 |
| 0.9436 | 11.55 | 3050 | 0.6524 |
| 0.9134 | 11.74 | 3100 | 0.6582 |
| 0.817 | 11.93 | 3150 | 0.6678 |
| 0.8545 | 12.12 | 3200 | 0.6520 |
| 0.9801 | 12.31 | 3250 | 0.7813 |
| 0.8566 | 12.5 | 3300 | 0.7205 |
| 0.8966 | 12.69 | 3350 | 0.6326 |
| 0.8705 | 12.88 | 3400 | 0.6577 |
| 0.8193 | 13.07 | 3450 | 0.6391 |
| 0.8099 | 13.26 | 3500 | 0.6658 |
| 0.921 | 13.45 | 3550 | 0.6535 |
| 0.7915 | 13.64 | 3600 | 0.6576 |
| 1.1439 | 13.83 | 3650 | 0.6593 |
| 0.8702 | 14.02 | 3700 | 0.6519 |
| 0.73 | 14.2 | 3750 | 0.6403 |
| 0.8306 | 14.39 | 3800 | 0.6393 |
| 0.8678 | 14.58 | 3850 | 0.6405 |
| 1.0003 | 14.77 | 3900 | 0.6407 |
| 1.023 | 14.96 | 3950 | 0.6402 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
| [
"cherry leaf",
"peach leaf",
"corn leaf blight",
"apple rust leaf",
"potato leaf late blight",
"strawberry leaf",
"tomato leaf late blight",
"tomato mold leaf",
"potato leaf early blight",
"apple leaf",
"tomato leaf yellow virus",
"blueberry leaf",
"tomato leaf mosaic virus",
"raspberry leaf",
"tomato leaf bacterial spot",
"squash powdery mildew leaf",
"grape leaf",
"tomato early blight leaf",
"apple scab leaf",
"tomato septoria leaf spot",
"tomato leaf",
"soyabean leaf",
"bell_pepper leaf spot",
"bell_pepper leaf"
] |
JoseArmando07/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 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: 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: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
HarshaSingamshetty1/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 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: 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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
yunusskeete/deformable-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. -->
# deformable-detr-resnet-50_finetuned_cppe5
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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
SirSkandrani/Object_Detection_for_Medical_Equipement |
<!-- 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. -->
# model
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: 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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
AAEfimov/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 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: 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 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
josephlyr/detr-resnet-50_finetuned_road_traffic |
<!-- 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_road_traffic
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: 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: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"road-traffic",
"bicycles",
"buses",
"crosswalks",
"fire hydrants",
"motorcycles",
"traffic lights",
"vehicles"
] |
aisuko/ft-detr-with-cppe-5 |
<!-- 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. -->
# ft-detr-with-cppe-5
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: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
haihuynh/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 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: 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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
nqtruong/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 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: 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: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
gabrielblins/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 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: 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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
superb-ai/deta-swin-large | # Detection Transformers with Assignment
By [Jeffrey Ouyang-Zhang](https://jozhang97.github.io/), [Jang Hyun Cho](https://sites.google.com/view/janghyuncho/), [Xingyi Zhou](https://www.cs.utexas.edu/~zhouxy/), [Philipp KrΓ€henbΓΌhl](http://www.philkr.net/)
From the paper [NMS Strikes Back](https://arxiv.org/abs/2212.06137).
**TL; DR.** **De**tection **T**ransformers with **A**ssignment (DETA) re-introduce IoU assignment and NMS for transformer-based detectors. DETA trains and tests comparibly as fast as Deformable-DETR and converges much faster (50.2 mAP in 12 epochs on COCO). | [
"n/a",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"n/a",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"n/a",
"backpack",
"umbrella",
"n/a",
"n/a",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"n/a",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"n/a",
"dining table",
"n/a",
"n/a",
"toilet",
"n/a",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"n/a",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
] |
justin-shopcapsule/yolos-fashionpedia |
This is a fine-tunned object detection model for fashion.
For more details of the implementation you can check the source code [here](https://github.com/valntinaf/fine_tunning_YOLOS_for_fashion)
the dataset used for its training is available [here](https://huggingface.co/datasets/detection-datasets/fashionpedia)
this model supports the following categories:
CATS = ['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel']
| [
"shirt, blouse",
"top, t-shirt, sweatshirt",
"sweater",
"jacket",
"pants",
"shorts",
"coat",
"dress",
"jumpsuit"
] |
eryuefei/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 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: 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 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
diegokauer/detr-coe-int |
<!-- 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-coe-int
This model is a fine-tuned version of [diegokauer/detr-coe-int](https://huggingface.co/diegokauer/detr-coe-int) 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: 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: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
| [
"11",
"3"
] |
joeZhuang/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 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
TaiQuach/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: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
diegokauer/conditional-detr-coe-int |
<!-- 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. -->
# conditional-detr-coe-int
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-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: 2e-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: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
| [
"propuesta",
"etapa"
] |
cramade/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 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: 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 4.36.2
- Pytorch 2.0.0+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
BhavanaMalla/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 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
senthilsk/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 excavatordetector 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 4.31.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
| [
"excavators",
"dump truck",
"wheel loader"
] |
EroshkinIvan/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.
It achieves the following results on the evaluation set:
- Loss: 3.7727
## 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: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.0989 | 1.0 | 125 | 3.4853 |
| 2.6414 | 2.0 | 250 | 3.4603 |
| 2.4811 | 3.0 | 375 | 3.1249 |
| 2.2018 | 4.0 | 500 | 2.9870 |
| 2.1618 | 5.0 | 625 | 3.2940 |
| 2.0608 | 6.0 | 750 | 3.1065 |
| 1.9556 | 7.0 | 875 | 3.0672 |
| 1.9142 | 8.0 | 1000 | 3.1347 |
| 1.8984 | 9.0 | 1125 | 3.0545 |
| 1.7799 | 10.0 | 1250 | 3.1041 |
| 1.7396 | 11.0 | 1375 | 2.9855 |
| 1.6741 | 12.0 | 1500 | 2.9899 |
| 1.6367 | 13.0 | 1625 | 3.0423 |
| 1.5807 | 14.0 | 1750 | 2.9943 |
| 1.6542 | 15.0 | 1875 | 3.0440 |
| 1.6268 | 16.0 | 2000 | 3.0157 |
| 1.5376 | 17.0 | 2125 | 3.0821 |
| 1.5396 | 18.0 | 2250 | 3.0359 |
| 1.5513 | 19.0 | 2375 | 3.0542 |
| 1.5038 | 20.0 | 2500 | 3.0153 |
| 1.4642 | 21.0 | 2625 | 3.0700 |
| 1.4331 | 22.0 | 2750 | 2.9970 |
| 1.4549 | 23.0 | 2875 | 3.0449 |
| 1.3796 | 24.0 | 3000 | 2.9348 |
| 1.3486 | 25.0 | 3125 | 3.0530 |
| 1.3557 | 26.0 | 3250 | 2.9036 |
| 1.3322 | 27.0 | 3375 | 2.8978 |
| 1.2866 | 28.0 | 3500 | 2.9449 |
| 1.2197 | 29.0 | 3625 | 2.9240 |
| 1.2596 | 30.0 | 3750 | 2.9422 |
| 1.2618 | 31.0 | 3875 | 2.8963 |
| 1.2298 | 32.0 | 4000 | 2.9116 |
| 1.2755 | 33.0 | 4125 | 2.8461 |
| 1.2062 | 34.0 | 4250 | 2.8651 |
| 1.2536 | 35.0 | 4375 | 2.8515 |
| 1.2543 | 36.0 | 4500 | 2.8212 |
| 1.2047 | 37.0 | 4625 | 2.8645 |
| 1.1618 | 38.0 | 4750 | 2.8755 |
| 1.1341 | 39.0 | 4875 | 2.9445 |
| 1.1881 | 40.0 | 5000 | 2.8731 |
| 1.2004 | 41.0 | 5125 | 2.8917 |
| 1.105 | 42.0 | 5250 | 2.9692 |
| 1.1408 | 43.0 | 5375 | 2.8619 |
| 1.0987 | 44.0 | 5500 | 2.8797 |
| 1.0857 | 45.0 | 5625 | 2.9032 |
| 1.0983 | 46.0 | 5750 | 2.7954 |
| 1.1254 | 47.0 | 5875 | 2.8693 |
| 0.9895 | 48.0 | 6000 | 2.9085 |
| 1.0401 | 49.0 | 6125 | 2.9256 |
| 1.0427 | 50.0 | 6250 | 2.9414 |
| 1.0813 | 51.0 | 6375 | 2.9730 |
| 1.043 | 52.0 | 6500 | 2.9698 |
| 1.0406 | 53.0 | 6625 | 2.9039 |
| 1.0178 | 54.0 | 6750 | 2.8910 |
| 1.0342 | 55.0 | 6875 | 2.8973 |
| 0.9433 | 56.0 | 7000 | 2.9515 |
| 1.0011 | 57.0 | 7125 | 2.8979 |
| 0.9683 | 58.0 | 7250 | 2.9770 |
| 0.9852 | 59.0 | 7375 | 2.9760 |
| 0.8886 | 60.0 | 7500 | 2.9978 |
| 0.9192 | 61.0 | 7625 | 2.9287 |
| 1.0015 | 62.0 | 7750 | 3.0118 |
| 0.9786 | 63.0 | 7875 | 2.9599 |
| 0.9238 | 64.0 | 8000 | 2.9459 |
| 0.9055 | 65.0 | 8125 | 2.9782 |
| 0.8864 | 66.0 | 8250 | 2.9396 |
| 0.8986 | 67.0 | 8375 | 3.0249 |
| 0.9571 | 68.0 | 8500 | 3.0207 |
| 0.8921 | 69.0 | 8625 | 2.9716 |
| 0.847 | 70.0 | 8750 | 3.0404 |
| 0.881 | 71.0 | 8875 | 2.9912 |
| 0.8919 | 72.0 | 9000 | 2.8573 |
| 0.8949 | 73.0 | 9125 | 2.8769 |
| 0.8704 | 74.0 | 9250 | 2.8301 |
| 0.8159 | 75.0 | 9375 | 2.9823 |
| 0.8586 | 76.0 | 9500 | 2.9548 |
| 0.8398 | 77.0 | 9625 | 2.9726 |
| 0.8368 | 78.0 | 9750 | 2.9853 |
| 0.8597 | 79.0 | 9875 | 3.0549 |
| 0.8176 | 80.0 | 10000 | 3.0531 |
| 0.838 | 81.0 | 10125 | 2.9602 |
| 0.8059 | 82.0 | 10250 | 2.9129 |
| 0.8176 | 83.0 | 10375 | 2.9702 |
| 0.8318 | 84.0 | 10500 | 2.9154 |
| 0.7104 | 85.0 | 10625 | 2.9706 |
| 0.7804 | 86.0 | 10750 | 2.9344 |
| 0.7696 | 87.0 | 10875 | 3.0594 |
| 0.7478 | 88.0 | 11000 | 2.9454 |
| 0.7374 | 89.0 | 11125 | 2.9786 |
| 0.7977 | 90.0 | 11250 | 2.9514 |
| 0.7864 | 91.0 | 11375 | 2.9400 |
| 0.7665 | 92.0 | 11500 | 2.8717 |
| 0.7539 | 93.0 | 11625 | 3.0417 |
| 0.6882 | 94.0 | 11750 | 2.9567 |
| 0.7424 | 95.0 | 11875 | 2.9805 |
| 0.7238 | 96.0 | 12000 | 3.0428 |
| 0.7383 | 97.0 | 12125 | 2.9852 |
| 0.6602 | 98.0 | 12250 | 3.0132 |
| 0.6971 | 99.0 | 12375 | 2.9537 |
| 0.7379 | 100.0 | 12500 | 2.9592 |
| 0.7207 | 101.0 | 12625 | 2.9905 |
| 0.7012 | 102.0 | 12750 | 3.0638 |
| 0.6768 | 103.0 | 12875 | 3.0401 |
| 0.6777 | 104.0 | 13000 | 3.0396 |
| 0.6913 | 105.0 | 13125 | 2.9501 |
| 0.6654 | 106.0 | 13250 | 3.1079 |
| 0.6393 | 107.0 | 13375 | 3.0405 |
| 0.6465 | 108.0 | 13500 | 3.1579 |
| 0.6379 | 109.0 | 13625 | 3.1174 |
| 0.6662 | 110.0 | 13750 | 3.0548 |
| 0.657 | 111.0 | 13875 | 3.0542 |
| 0.6193 | 112.0 | 14000 | 3.0411 |
| 0.5961 | 113.0 | 14125 | 3.0915 |
| 0.6438 | 114.0 | 14250 | 3.0924 |
| 0.5815 | 115.0 | 14375 | 3.0309 |
| 0.6113 | 116.0 | 14500 | 3.0300 |
| 0.635 | 117.0 | 14625 | 2.9968 |
| 0.5875 | 118.0 | 14750 | 2.9821 |
| 0.6447 | 119.0 | 14875 | 3.0285 |
| 0.5632 | 120.0 | 15000 | 3.0684 |
| 0.6206 | 121.0 | 15125 | 3.0065 |
| 0.5929 | 122.0 | 15250 | 3.0490 |
| 0.5509 | 123.0 | 15375 | 3.0520 |
| 0.6068 | 124.0 | 15500 | 3.0957 |
| 0.5747 | 125.0 | 15625 | 2.9621 |
| 0.5844 | 126.0 | 15750 | 3.0194 |
| 0.5684 | 127.0 | 15875 | 3.1037 |
| 0.6356 | 128.0 | 16000 | 3.0752 |
| 0.5644 | 129.0 | 16125 | 3.0063 |
| 0.6303 | 130.0 | 16250 | 3.0204 |
| 0.5432 | 131.0 | 16375 | 3.0809 |
| 0.6153 | 132.0 | 16500 | 3.1015 |
| 0.5662 | 133.0 | 16625 | 3.0639 |
| 0.5704 | 134.0 | 16750 | 3.1974 |
| 0.603 | 135.0 | 16875 | 3.1371 |
| 0.526 | 136.0 | 17000 | 3.1381 |
| 0.5767 | 137.0 | 17125 | 3.1614 |
| 0.5591 | 138.0 | 17250 | 3.2744 |
| 0.5609 | 139.0 | 17375 | 3.0405 |
| 0.5229 | 140.0 | 17500 | 3.0773 |
| 0.5367 | 141.0 | 17625 | 3.1367 |
| 0.5719 | 142.0 | 17750 | 3.1770 |
| 0.5172 | 143.0 | 17875 | 3.0953 |
| 0.5592 | 144.0 | 18000 | 3.2524 |
| 0.5422 | 145.0 | 18125 | 3.1534 |
| 0.5007 | 146.0 | 18250 | 3.1571 |
| 0.5348 | 147.0 | 18375 | 3.0949 |
| 0.5123 | 148.0 | 18500 | 3.1381 |
| 0.4839 | 149.0 | 18625 | 3.1624 |
| 0.5207 | 150.0 | 18750 | 3.0585 |
| 0.5236 | 151.0 | 18875 | 3.0886 |
| 0.5144 | 152.0 | 19000 | 3.1348 |
| 0.4882 | 153.0 | 19125 | 3.1027 |
| 0.4618 | 154.0 | 19250 | 3.1335 |
| 0.4573 | 155.0 | 19375 | 3.1687 |
| 0.4956 | 156.0 | 19500 | 3.2155 |
| 0.5073 | 157.0 | 19625 | 3.2512 |
| 0.5334 | 158.0 | 19750 | 3.2262 |
| 0.5014 | 159.0 | 19875 | 3.2350 |
| 0.4519 | 160.0 | 20000 | 3.2416 |
| 0.5042 | 161.0 | 20125 | 3.1955 |
| 0.4624 | 162.0 | 20250 | 3.2036 |
| 0.4577 | 163.0 | 20375 | 3.2498 |
| 0.5032 | 164.0 | 20500 | 3.1687 |
| 0.4894 | 165.0 | 20625 | 3.1920 |
| 0.4621 | 166.0 | 20750 | 3.2275 |
| 0.4896 | 167.0 | 20875 | 3.1416 |
| 0.4998 | 168.0 | 21000 | 3.1483 |
| 0.4941 | 169.0 | 21125 | 3.1408 |
| 0.4307 | 170.0 | 21250 | 3.2056 |
| 0.4284 | 171.0 | 21375 | 3.2112 |
| 0.4431 | 172.0 | 21500 | 3.1926 |
| 0.4429 | 173.0 | 21625 | 3.0972 |
| 0.4832 | 174.0 | 21750 | 3.2309 |
| 0.4417 | 175.0 | 21875 | 3.2027 |
| 0.4022 | 176.0 | 22000 | 3.2380 |
| 0.4777 | 177.0 | 22125 | 3.3315 |
| 0.462 | 178.0 | 22250 | 3.1854 |
| 0.4209 | 179.0 | 22375 | 3.1563 |
| 0.4271 | 180.0 | 22500 | 3.3036 |
| 0.4359 | 181.0 | 22625 | 3.3058 |
| 0.4324 | 182.0 | 22750 | 3.3639 |
| 0.4252 | 183.0 | 22875 | 3.2810 |
| 0.4382 | 184.0 | 23000 | 3.4633 |
| 0.4344 | 185.0 | 23125 | 3.2875 |
| 0.4639 | 186.0 | 23250 | 3.2771 |
| 0.4104 | 187.0 | 23375 | 3.2768 |
| 0.437 | 188.0 | 23500 | 3.3128 |
| 0.4469 | 189.0 | 23625 | 3.2389 |
| 0.4084 | 190.0 | 23750 | 3.4082 |
| 0.4333 | 191.0 | 23875 | 3.3177 |
| 0.4337 | 192.0 | 24000 | 3.3474 |
| 0.4173 | 193.0 | 24125 | 3.3495 |
| 0.386 | 194.0 | 24250 | 3.3413 |
| 0.4279 | 195.0 | 24375 | 3.3327 |
| 0.4046 | 196.0 | 24500 | 3.3383 |
| 0.4163 | 197.0 | 24625 | 3.3235 |
| 0.4032 | 198.0 | 24750 | 3.3549 |
| 0.381 | 199.0 | 24875 | 3.2899 |
| 0.3858 | 200.0 | 25000 | 3.3752 |
| 0.4085 | 201.0 | 25125 | 3.2569 |
| 0.3643 | 202.0 | 25250 | 3.3265 |
| 0.3621 | 203.0 | 25375 | 3.3730 |
| 0.4749 | 204.0 | 25500 | 3.3738 |
| 0.3969 | 205.0 | 25625 | 3.3619 |
| 0.3677 | 206.0 | 25750 | 3.4378 |
| 0.3838 | 207.0 | 25875 | 3.3412 |
| 0.4063 | 208.0 | 26000 | 3.3268 |
| 0.3719 | 209.0 | 26125 | 3.4574 |
| 0.3803 | 210.0 | 26250 | 3.3598 |
| 0.4093 | 211.0 | 26375 | 3.3738 |
| 0.38 | 212.0 | 26500 | 3.2644 |
| 0.3757 | 213.0 | 26625 | 3.3872 |
| 0.4116 | 214.0 | 26750 | 3.4318 |
| 0.3741 | 215.0 | 26875 | 3.2945 |
| 0.3809 | 216.0 | 27000 | 3.4419 |
| 0.3625 | 217.0 | 27125 | 3.4126 |
| 0.3772 | 218.0 | 27250 | 3.3693 |
| 0.3494 | 219.0 | 27375 | 3.3014 |
| 0.3521 | 220.0 | 27500 | 3.4202 |
| 0.3498 | 221.0 | 27625 | 3.2887 |
| 0.3716 | 222.0 | 27750 | 3.5634 |
| 0.346 | 223.0 | 27875 | 3.3463 |
| 0.388 | 224.0 | 28000 | 3.4088 |
| 0.3708 | 225.0 | 28125 | 3.3841 |
| 0.3964 | 226.0 | 28250 | 3.3839 |
| 0.3897 | 227.0 | 28375 | 3.4874 |
| 0.3272 | 228.0 | 28500 | 3.5225 |
| 0.3582 | 229.0 | 28625 | 3.4964 |
| 0.3656 | 230.0 | 28750 | 3.3781 |
| 0.3497 | 231.0 | 28875 | 3.4067 |
| 0.3318 | 232.0 | 29000 | 3.4918 |
| 0.3565 | 233.0 | 29125 | 3.5039 |
| 0.3865 | 234.0 | 29250 | 3.5416 |
| 0.3583 | 235.0 | 29375 | 3.4231 |
| 0.3464 | 236.0 | 29500 | 3.4524 |
| 0.3465 | 237.0 | 29625 | 3.4779 |
| 0.3428 | 238.0 | 29750 | 3.4889 |
| 0.3847 | 239.0 | 29875 | 3.5142 |
| 0.3505 | 240.0 | 30000 | 3.5132 |
| 0.344 | 241.0 | 30125 | 3.5439 |
| 0.3741 | 242.0 | 30250 | 3.4861 |
| 0.3045 | 243.0 | 30375 | 3.4534 |
| 0.3443 | 244.0 | 30500 | 3.4675 |
| 0.3719 | 245.0 | 30625 | 3.4354 |
| 0.3534 | 246.0 | 30750 | 3.4817 |
| 0.3644 | 247.0 | 30875 | 3.5027 |
| 0.3157 | 248.0 | 31000 | 3.5055 |
| 0.3393 | 249.0 | 31125 | 3.3962 |
| 0.3054 | 250.0 | 31250 | 3.4470 |
| 0.3434 | 251.0 | 31375 | 3.5036 |
| 0.3141 | 252.0 | 31500 | 3.5428 |
| 0.3227 | 253.0 | 31625 | 3.5025 |
| 0.3199 | 254.0 | 31750 | 3.5110 |
| 0.3667 | 255.0 | 31875 | 3.5168 |
| 0.3442 | 256.0 | 32000 | 3.5739 |
| 0.366 | 257.0 | 32125 | 3.5094 |
| 0.3053 | 258.0 | 32250 | 3.4360 |
| 0.3595 | 259.0 | 32375 | 3.5895 |
| 0.3329 | 260.0 | 32500 | 3.5869 |
| 0.3139 | 261.0 | 32625 | 3.5317 |
| 0.299 | 262.0 | 32750 | 3.6398 |
| 0.3131 | 263.0 | 32875 | 3.5696 |
| 0.3218 | 264.0 | 33000 | 3.5519 |
| 0.3677 | 265.0 | 33125 | 3.6712 |
| 0.3447 | 266.0 | 33250 | 3.5278 |
| 0.3094 | 267.0 | 33375 | 3.5613 |
| 0.3031 | 268.0 | 33500 | 3.4634 |
| 0.3234 | 269.0 | 33625 | 3.5966 |
| 0.3489 | 270.0 | 33750 | 3.5239 |
| 0.3168 | 271.0 | 33875 | 3.6847 |
| 0.3151 | 272.0 | 34000 | 3.5559 |
| 0.2843 | 273.0 | 34125 | 3.5995 |
| 0.3003 | 274.0 | 34250 | 3.6388 |
| 0.3154 | 275.0 | 34375 | 3.6759 |
| 0.3178 | 276.0 | 34500 | 3.5199 |
| 0.3436 | 277.0 | 34625 | 3.5651 |
| 0.3136 | 278.0 | 34750 | 3.5722 |
| 0.3252 | 279.0 | 34875 | 3.4851 |
| 0.3404 | 280.0 | 35000 | 3.6847 |
| 0.304 | 281.0 | 35125 | 3.5653 |
| 0.3395 | 282.0 | 35250 | 3.6775 |
| 0.3431 | 283.0 | 35375 | 3.5556 |
| 0.2861 | 284.0 | 35500 | 3.6451 |
| 0.3066 | 285.0 | 35625 | 3.6052 |
| 0.3151 | 286.0 | 35750 | 3.6406 |
| 0.3143 | 287.0 | 35875 | 3.6744 |
| 0.2873 | 288.0 | 36000 | 3.6218 |
| 0.3296 | 289.0 | 36125 | 3.4993 |
| 0.3024 | 290.0 | 36250 | 3.5596 |
| 0.3138 | 291.0 | 36375 | 3.5875 |
| 0.2964 | 292.0 | 36500 | 3.5953 |
| 0.2935 | 293.0 | 36625 | 3.5550 |
| 0.2856 | 294.0 | 36750 | 3.5805 |
| 0.2998 | 295.0 | 36875 | 3.6153 |
| 0.2809 | 296.0 | 37000 | 3.6440 |
| 0.3197 | 297.0 | 37125 | 3.6127 |
| 0.2863 | 298.0 | 37250 | 3.6362 |
| 0.3355 | 299.0 | 37375 | 3.7291 |
| 0.2942 | 300.0 | 37500 | 3.6750 |
| 0.3187 | 301.0 | 37625 | 3.6617 |
| 0.3191 | 302.0 | 37750 | 3.6898 |
| 0.2716 | 303.0 | 37875 | 3.6238 |
| 0.2911 | 304.0 | 38000 | 3.6409 |
| 0.3231 | 305.0 | 38125 | 3.6807 |
| 0.2723 | 306.0 | 38250 | 3.7038 |
| 0.2812 | 307.0 | 38375 | 3.6565 |
| 0.3225 | 308.0 | 38500 | 3.6680 |
| 0.2803 | 309.0 | 38625 | 3.7389 |
| 0.2852 | 310.0 | 38750 | 3.7257 |
| 0.2958 | 311.0 | 38875 | 3.7873 |
| 0.3191 | 312.0 | 39000 | 3.8238 |
| 0.2815 | 313.0 | 39125 | 3.7388 |
| 0.2681 | 314.0 | 39250 | 3.7543 |
| 0.3259 | 315.0 | 39375 | 3.8022 |
| 0.2804 | 316.0 | 39500 | 3.7106 |
| 0.3214 | 317.0 | 39625 | 3.6244 |
| 0.2732 | 318.0 | 39750 | 3.7712 |
| 0.3054 | 319.0 | 39875 | 3.6776 |
| 0.263 | 320.0 | 40000 | 3.6850 |
| 0.2644 | 321.0 | 40125 | 3.7169 |
| 0.2796 | 322.0 | 40250 | 3.7958 |
| 0.2928 | 323.0 | 40375 | 3.7770 |
| 0.2774 | 324.0 | 40500 | 3.7142 |
| 0.3048 | 325.0 | 40625 | 3.7942 |
| 0.2637 | 326.0 | 40750 | 3.7499 |
| 0.2549 | 327.0 | 40875 | 3.7323 |
| 0.2681 | 328.0 | 41000 | 3.8373 |
| 0.2735 | 329.0 | 41125 | 3.7600 |
| 0.291 | 330.0 | 41250 | 3.6715 |
| 0.278 | 331.0 | 41375 | 3.6660 |
| 0.2785 | 332.0 | 41500 | 3.7076 |
| 0.2632 | 333.0 | 41625 | 3.7408 |
| 0.2994 | 334.0 | 41750 | 3.7214 |
| 0.2563 | 335.0 | 41875 | 3.7326 |
| 0.2755 | 336.0 | 42000 | 3.7088 |
| 0.287 | 337.0 | 42125 | 3.7493 |
| 0.3144 | 338.0 | 42250 | 3.7180 |
| 0.2816 | 339.0 | 42375 | 3.7289 |
| 0.2515 | 340.0 | 42500 | 3.6592 |
| 0.2647 | 341.0 | 42625 | 3.6483 |
| 0.2833 | 342.0 | 42750 | 3.7359 |
| 0.2678 | 343.0 | 42875 | 3.7351 |
| 0.2929 | 344.0 | 43000 | 3.7129 |
| 0.2604 | 345.0 | 43125 | 3.7604 |
| 0.2406 | 346.0 | 43250 | 3.6867 |
| 0.2802 | 347.0 | 43375 | 3.6935 |
| 0.2702 | 348.0 | 43500 | 3.6744 |
| 0.2836 | 349.0 | 43625 | 3.7118 |
| 0.2454 | 350.0 | 43750 | 3.7354 |
| 0.238 | 351.0 | 43875 | 3.7200 |
| 0.2663 | 352.0 | 44000 | 3.7351 |
| 0.272 | 353.0 | 44125 | 3.7509 |
| 0.2422 | 354.0 | 44250 | 3.7413 |
| 0.3032 | 355.0 | 44375 | 3.6266 |
| 0.2617 | 356.0 | 44500 | 3.7229 |
| 0.2618 | 357.0 | 44625 | 3.7356 |
| 0.2619 | 358.0 | 44750 | 3.7178 |
| 0.2588 | 359.0 | 44875 | 3.7896 |
| 0.2508 | 360.0 | 45000 | 3.7686 |
| 0.2791 | 361.0 | 45125 | 3.7918 |
| 0.27 | 362.0 | 45250 | 3.7870 |
| 0.2841 | 363.0 | 45375 | 3.7675 |
| 0.2776 | 364.0 | 45500 | 3.7090 |
| 0.2752 | 365.0 | 45625 | 3.6754 |
| 0.2727 | 366.0 | 45750 | 3.6542 |
| 0.2423 | 367.0 | 45875 | 3.6399 |
| 0.263 | 368.0 | 46000 | 3.6337 |
| 0.2562 | 369.0 | 46125 | 3.7343 |
| 0.2704 | 370.0 | 46250 | 3.6502 |
| 0.2604 | 371.0 | 46375 | 3.7519 |
| 0.2312 | 372.0 | 46500 | 3.7563 |
| 0.2761 | 373.0 | 46625 | 3.6497 |
| 0.265 | 374.0 | 46750 | 3.7312 |
| 0.2545 | 375.0 | 46875 | 3.7273 |
| 0.2551 | 376.0 | 47000 | 3.8266 |
| 0.2604 | 377.0 | 47125 | 3.7659 |
| 0.252 | 378.0 | 47250 | 3.8003 |
| 0.2468 | 379.0 | 47375 | 3.7535 |
| 0.289 | 380.0 | 47500 | 3.7376 |
| 0.2449 | 381.0 | 47625 | 3.7216 |
| 0.2471 | 382.0 | 47750 | 3.7704 |
| 0.2627 | 383.0 | 47875 | 3.7510 |
| 0.2454 | 384.0 | 48000 | 3.8007 |
| 0.2391 | 385.0 | 48125 | 3.7535 |
| 0.2452 | 386.0 | 48250 | 3.7905 |
| 0.2608 | 387.0 | 48375 | 3.8170 |
| 0.2662 | 388.0 | 48500 | 3.7836 |
| 0.235 | 389.0 | 48625 | 3.7754 |
| 0.2425 | 390.0 | 48750 | 3.7201 |
| 0.2463 | 391.0 | 48875 | 3.8298 |
| 0.2507 | 392.0 | 49000 | 3.8252 |
| 0.2451 | 393.0 | 49125 | 3.7625 |
| 0.2418 | 394.0 | 49250 | 3.7482 |
| 0.2397 | 395.0 | 49375 | 3.7875 |
| 0.2773 | 396.0 | 49500 | 3.8159 |
| 0.256 | 397.0 | 49625 | 3.8322 |
| 0.2304 | 398.0 | 49750 | 3.8012 |
| 0.2363 | 399.0 | 49875 | 3.8033 |
| 0.2575 | 400.0 | 50000 | 3.7727 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
akar49/crack-cond_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. -->
# crack-cond_detr
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the crack_detection-merged-ii 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"crack",
"fissures"
] |
Murdock007/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: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"type1",
"type2",
"type3"
] |
Murdock007/detr-resnet-50_finetuned_stages |
<!-- 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_stages
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: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"stage 1",
"stage 2",
"stage 3",
"stage 4",
"stage 5"
] |
muqtadar/yolos-tiny |
# YOLOS (tiny-sized) model
YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS).
Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models.
### How to use
Here is how to use this model:
```python
from transformers import YolosImageProcessor, YolosForObjectDetection
from PIL import Image
import torch
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# model predicts bounding boxes and corresponding COCO classes
logits = outputs.logits
bboxes = outputs.pred_boxes
# print results
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(
f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
```
Currently, both the feature extractor and model support PyTorch.
## Training data
The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
### Training
The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 300 epochs on COCO.
## Evaluation results
This model achieves an AP (average precision) of **28.7** on COCO 2017 validation. For more details regarding evaluation results, we refer to the original paper.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2106-00666,
author = {Yuxin Fang and
Bencheng Liao and
Xinggang Wang and
Jiemin Fang and
Jiyang Qi and
Rui Wu and
Jianwei Niu and
Wenyu Liu},
title = {You Only Look at One Sequence: Rethinking Transformer in Vision through
Object Detection},
journal = {CoRR},
volume = {abs/2106.00666},
year = {2021},
url = {https://arxiv.org/abs/2106.00666},
eprinttype = {arXiv},
eprint = {2106.00666},
timestamp = {Fri, 29 Apr 2022 19:49:16 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | [
"n/a",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"n/a",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"n/a",
"backpack",
"umbrella",
"n/a",
"n/a",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"n/a",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"n/a",
"dining table",
"n/a",
"n/a",
"toilet",
"n/a",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"n/a",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
] |
BestPhilForSure/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
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
synergycodes/diagram_detr_r50_finetuned |
<!-- 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. -->
# daigram_detr_r50_albumentations_finetuning
This model is a fine-tuned version of [kacper-cierzniewski/daigram_detr_r50_albumentations](https://huggingface.co/kacper-cierzniewski/daigram_detr_r50_albumentations) on the bpmn-shapes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9817
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9457 | 12.5 | 50 | 1.0238 |
| 0.9717 | 25.0 | 100 | 1.0411 |
| 0.9823 | 37.5 | 150 | 1.0269 |
| 0.9524 | 50.0 | 200 | 1.0518 |
| 0.9886 | 62.5 | 250 | 1.0548 |
| 0.9638 | 75.0 | 300 | 1.0454 |
| 0.948 | 87.5 | 350 | 1.0240 |
| 0.9312 | 100.0 | 400 | 1.0281 |
| 0.9183 | 112.5 | 450 | 1.0112 |
| 0.9219 | 125.0 | 500 | 1.0110 |
| 0.9285 | 137.5 | 550 | 1.0325 |
| 0.9177 | 150.0 | 600 | 1.0009 |
| 0.9323 | 162.5 | 650 | 1.0124 |
| 0.9333 | 175.0 | 700 | 1.0154 |
| 0.9386 | 187.5 | 750 | 1.0188 |
| 0.9586 | 200.0 | 800 | 0.9978 |
| 0.894 | 212.5 | 850 | 1.0087 |
| 0.8999 | 225.0 | 900 | 1.0055 |
| 0.9324 | 237.5 | 950 | 1.0185 |
| 0.9313 | 250.0 | 1000 | 0.9840 |
| 0.9177 | 262.5 | 1050 | 0.9785 |
| 0.8918 | 275.0 | 1100 | 0.9874 |
| 0.9145 | 287.5 | 1150 | 0.9802 |
| 0.89 | 300.0 | 1200 | 0.9879 |
| 0.8818 | 312.5 | 1250 | 0.9857 |
| 0.9256 | 325.0 | 1300 | 0.9951 |
| 0.9028 | 337.5 | 1350 | 1.0001 |
| 0.9252 | 350.0 | 1400 | 1.0033 |
| 0.9017 | 362.5 | 1450 | 0.9916 |
| 0.8783 | 375.0 | 1500 | 0.9858 |
| 0.911 | 387.5 | 1550 | 0.9758 |
| 0.8797 | 400.0 | 1600 | 0.9810 |
| 0.8995 | 412.5 | 1650 | 0.9840 |
| 0.8781 | 425.0 | 1700 | 0.9843 |
| 0.8897 | 437.5 | 1750 | 0.9745 |
| 0.905 | 450.0 | 1800 | 0.9825 |
| 0.8961 | 462.5 | 1850 | 0.9781 |
| 0.8865 | 475.0 | 1900 | 0.9781 |
| 0.8824 | 487.5 | 1950 | 0.9794 |
| 0.8836 | 500.0 | 2000 | 0.9817 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"shapes",
"arrow",
"circle",
"diamond",
"pointer",
"rectangle",
"triangle"
] |
hieudt/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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
drabindr/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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9"
] |
brainer/detr-resnet-50-dc5 |
<!-- 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-dc5
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: 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 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
| [
"0",
"1",
"100",
"1000",
"10000",
"10002",
"10003",
"10004",
"10005",
"10006",
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hilmantm/detr-traffic-accident-detection |
# Traffic Accident Detection
## Overview
The [DETR](https://huggingface.co/facebook/detr-resnet-50) (DEtection Transfomer) model utilized in this implementation serves as a sophisticated solution for accident detection. This state-of-the-art model leverages the power of transformers, originally designed for natural language processing, to excel in object detection tasks. Trained on a diverse dataset, the DETR model demonstrates its capability to identify and locate objects within images, particularly excelling in the crucial task of accident detection within traffic scenes.
Harnessing advanced computer vision techniques, DETR offers unparalleled accuracy and efficiency in recognizing potential incidents, providing valuable insights for enhancing road safety. Its utilization is pivotal in real-time monitoring and analysis, empowering applications geared towards automated accident detection and response systems.
This DETR model is equipped with a robust post-processing pipeline, incorporating Non-Maximum Suppression (NMS) to refine detections and deliver precise and actionable results. Combined with efficient inference times, this DETR model stands as a powerful tool in the realm of accident detection, contributing to the development of intelligent and safety-focused systems in various domains.
## Dataset
Introducing a cutting-edge approach to accident detection, this model employs the DETR (DEtection Transfomer) architecture, specifically designed to seamlessly identify accidents within a comprehensive scene captured in a single image. Unlike traditional methods, this innovative model operates within the context of full images, leveraging the power of transformer-based object detection.
Table 1: When we use dataset focuses on accident label, model fails to detect accidents when traffic jams.
| traffic jams | traffic jams |
|-------|-------|
|  |  |
Table 2: When we use multi label dataset (accident and vehicle), model can detect accidents accurately without reducing detection performance when traffic jams
| traffic jams | traffic jams | accident | accident |
|-------|-------|------|-------|
|  |  |  |  |
Trained on a diverse and multilabel dataset, including 'accident' and 'vehicle' labels, the model excels in simultaneously recognizing both accident-related incidents and the presence of vehicles. This dual-label dataset enhances the model's capacity to comprehensively understand and interpret complex traffic scenarios, making it a potent tool for real-time accident detection and analysis.
By adopting a holistic perspective on the entire image, this DETR-based model contributes to a more robust and nuanced understanding of potential accidents, fostering advancements in automated safety systems. Its proficiency in detecting accidents within the broader context of traffic scenes positions it as a valuable asset for applications dedicated to enhancing road safety and emergency response.
[](https://dl.acm.org/doi/10.1145/3669754.3669766)
[](https://universe.roboflow.com/hilmantm/traffic-accident-detection)
[](https://huggingface.co/spaces/hilmantm/detr-traffic-accident-detection) | [
"accident",
"accident",
"vehicle"
] |
lknjbvhgjhibkhvj/keklol123 |
This is a fine-tunned object detection model for fashion.
For more details of the implementation you can check the source code [here](https://github.com/valntinaf/fine_tunning_YOLOS_for_fashion)
the dataset used for its training is available [here](https://huggingface.co/datasets/detection-datasets/fashionpedia)
this model supports the following categories:
CATS = ['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel']
 | [
"shirt, blouse",
"top, t-shirt, sweatshirt",
"sweater",
"cardigan",
"jacket",
"vest",
"pants",
"shorts",
"skirt",
"coat",
"dress",
"jumpsuit",
"cape",
"glasses",
"hat",
"headband, head covering, hair accessory",
"tie",
"glove",
"watch",
"belt",
"leg warmer",
"tights, stockings",
"sock",
"shoe",
"bag, wallet",
"scarf",
"umbrella",
"hood",
"collar",
"lapel",
"epaulette",
"sleeve",
"pocket",
"neckline",
"buckle",
"zipper",
"applique",
"bead",
"bow",
"flower",
"fringe",
"ribbon",
"rivet",
"ruffle",
"sequin",
"tassel"
] |
aliosama8399/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 hardhatdetection 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 4.37.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
| [
"hardhat",
"no-hardhat"
] |
Oblix/detr-doc-table-detection_ONNX | https://huggingface.co/TahaDouaji/detr-doc-table-detection with ONNX weights to be compatible with Transformers.js. | [
"table",
"table"
] |
gg-insoore/detr-finetuned-cardamage-v2 |
# Model Card for Model ID
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## 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. -->
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#### Preprocessing [optional]
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#### 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. -->
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## 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. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed]
| [
"label_0",
"label_1",
"label_2",
"label_3"
] |
zacmccormick/detr-finetuned-telephone-poles |
# 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. -->
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## 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. -->
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[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]
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## How to Get Started with the Model
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[More Information Needed]
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## Evaluation
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| [
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7",
"label_8",
"label_9",
"label_10",
"label_11"
] |
C-Stuti/table-transformer-detection_10 |
# Model Card for Model ID
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| [
"table",
"table rotated"
] |
rnud/detr-resnet50-finetuning-watermark-pita |
# Model Card for Model ID
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## How to Get Started with the Model
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[More Information Needed]
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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).
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| [
"label_0",
"label_1",
"label_2"
] |
test123nananan/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-07
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"1",
"2"
] |
rnud/detr-resnet-50_finetuned |
<!-- 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
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: 5e-06
- train_batch_size: 24
- eval_batch_size: 24
- 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 4.37.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"logo",
"text"
] |
rnud/detr-resnet50-finetuning-watermark-pita-v2 |
# Model Card for Model ID
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## Model Details
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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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).
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[More Information Needed]
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| [
"label_0",
"label_1",
"label_2"
] |
mankook/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 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
Gihun145/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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| [
"coverall",
"face_shield",
"gloves",
"goggles",
"mask"
] |
sekhharr/detr_finetuned_v3_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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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<!-- 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"
] |
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