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--- |
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license: apache-2.0 |
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tags: |
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- object-detection |
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- vision |
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datasets: |
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- coco |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg |
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example_title: Savanna |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg |
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example_title: Football Match |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg |
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example_title: Airport |
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--- |
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# Deformable DETR model with ResNet-50 backbone |
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Deformable DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Zhu et al. and first released in [this repository](https://github.com/fundamentalvision/Deformable-DETR). |
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Disclaimer: The team releasing Deformable DETR did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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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. |
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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. |
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## Intended uses & limitations |
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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. |
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### How to use |
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Here is how to use this model: |
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```python |
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from transformers import AutoImageProcessor, DeformableDetrForObjectDetection |
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import torch |
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from PIL import Image |
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import requests |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") |
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model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr") |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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# convert outputs (bounding boxes and class logits) to COCO API |
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# let's only keep detections with score > 0.7 |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] |
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): |
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box = [round(i, 2) for i in box.tolist()] |
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print( |
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f"Detected {model.config.id2label[label.item()]} with confidence " |
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f"{round(score.item(), 3)} at location {box}" |
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) |
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``` |
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This should output: |
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``` |
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Detected cat with confidence 0.856 at location [342.19, 24.3, 640.02, 372.25] |
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Detected remote with confidence 0.739 at location [40.79, 72.78, 176.76, 117.25] |
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Detected cat with confidence 0.859 at location [16.5, 52.84, 318.25, 470.78] |
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``` |
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Currently, both the feature extractor and model support PyTorch. |
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## Training data |
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The Deformable DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2010.04159, |
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doi = {10.48550/ARXIV.2010.04159}, |
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url = {https://arxiv.org/abs/2010.04159}, |
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author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, |
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keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection}, |
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publisher = {arXiv}, |
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year = {2020}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
