---
library_name: transformers
tags:
- object-detection
- Document
- Layout
- Analysis
- DocLayNet
- mAP
datasets:
- ds4sd/DocLayNet
license: apache-2.0
base_model:
- SenseTime/deformable-detr
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Deformable-DETR-Document-Layout-Analysis

This model was fine-tuned on the doc_lay_net dataset for Document Layout Analysis using full-sized DocLayNet Public Dataset.

## Model description

The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. 

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.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png)

## Intended uses & limitations

You can use the model to predict Bounding Box for 11 different Classes of Document Layout Analysis.

### How to use

```python
from transformers import AutoImageProcessor, DeformableDetrForObjectDetection
import torch
from PIL import Image
import requests

url = "string-url-of-a-Document_page"
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("pascalrai/Deformable-DETR-Document-Layout-Analyzer")
model = DeformableDetrForObjectDetection.from_pretrained("pascalrai/Deformable-DETR-Document-Layout-Analyzer")

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)

# convert outputs (bounding boxes and class logits) to COCO API
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[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}"
    )
```

## Evaluation on DocLayNet

Evaluation of the Trained model on Test Dataset of DocLayNet (On 3 epoch):
```
{'map': 0.6086,
 'map_50': 0.836,
 'map_75': 0.6662,
 'map_small': 0.3269,
 'map_medium': 0.501,
 'map_large': 0.6712,
 'mar_1': 0.3336,
 'mar_10': 0.7113,
 'mar_100': 0.7596,
 'mar_small': 0.4667,
 'mar_medium': 0.6717,
 'mar_large': 0.8436,
 'map_0': 0.5709,
 'mar_100_0': 0.7639,
 'map_1': 0.4685,
 'mar_100_1': 0.7468,
 'map_2': 0.5776,
 'mar_100_2': 0.7163,
 'map_3': 0.7143,
 'mar_100_3': 0.8251,
 'map_4': 0.4056,
 'mar_100_4': 0.533,
 'map_5': 0.5095,
 'mar_100_5': 0.6686,
 'map_6': 0.6826,
 'mar_100_6': 0.8387,
 'map_7': 0.5859,
 'mar_100_7': 0.7308,
 'map_8': 0.7871,
 'mar_100_8': 0.8852,
 'map_9': 0.7898,
 'mar_100_9': 0.8617,
 'map_10': 0.6034,
 'mar_100_10': 0.7854}
```

### Training hyperparameters

The model was trained on A10G 24GB GPU for 21 hours.

The following hyperparameters were used during training:
- learning_rate: 5e-05
- eff_train_batch_size: 12
- eff_eval_batch_size: 12
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10

### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0

### BibTeX entry and citation info

```bibtex
@misc{https://doi.org/10.48550/arxiv.2010.04159,
  doi = {10.48550/ARXIV.2010.04159},
  url = {https://arxiv.org/abs/2010.04159}, 
  author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection},
  publisher = {arXiv},
  year = {2020},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
@article{doclaynet2022,
  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
  doi = {10.1145/3534678.353904},
  url = {https://doi.org/10.1145/3534678.3539043},
  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
  year = {2022},
  isbn = {9781450393850},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {3743–3751},
  numpages = {9},
  location = {Washington DC, USA},
  series = {KDD '22}
}
```