|
--- |
|
license: apache-2.0 |
|
tags: |
|
- object-detection |
|
- vision |
|
datasets: |
|
- DocLayNet |
|
widget: |
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg |
|
example_title: Savanna |
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg |
|
example_title: Football Match |
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg |
|
example_title: Airport |
|
--- |
|
|
|
# Deformable DETR model trained on DocLayNet |
|
|
|
Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). |
|
|
|
## 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. |
|
|
|
 |
|
|
|
## Intended uses & limitations |
|
|
|
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=sensetime/deformable-detr) to look for all available |
|
Deformable DETR models. |
|
|
|
### How to use |
|
|
|
Here is how to use this model: |
|
|
|
```python |
|
from transformers import AutoImageProcessor, DeformableDetrForObjectDetection |
|
import torch |
|
from PIL import Image |
|
import requests |
|
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
|
image = Image.open(requests.get(url, stream=True).raw) |
|
|
|
processor = AutoImageProcessor.from_pretrained("facebook/deformable-detr-detic") |
|
model = DeformableDetrForObjectDetection.from_pretrained("facebook/deformable-detr-detic") |
|
|
|
inputs = processor(images=image, return_tensors="pt") |
|
outputs = model(**inputs) |
|
|
|
# convert outputs (bounding boxes and class logits) to COCO API |
|
# let's only keep detections with score > 0.7 |
|
target_sizes = torch.tensor([image.size[::-1]]) |
|
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[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 results |
|
|
|
This model achieves 57.1 box mAP on DocLayNet. |
|
|
|
## Training data |
|
|
|
The Deformable DETR model was trained on DocLayNet. It was introduced in the paper [DocLayNet: A Large Human-Annotated Dataset for |
|
Document-Layout Analysis](https://arxiv.org/abs/2206.01062) by Pfitzmann et al. and first released in [this repository](https://github.com/DS4SD/DocLayNet). |
|
|
|
### 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} |
|
} |
|
``` |
