Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- object-detection
+- vision
+datasets:
+- coco
+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 with ResNet-50 backbone, with box refinement and two stage
+
+Deformable DEtection TRansformer (DETR), with box refinement and two stage 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). 
+
+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.
+
+## 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 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 AutoFeatureExtractor, 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)
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("SenseTime/deformable-detr-with-box-refine-two-stage")
+model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-with-box-refine-two-stage")
+
+inputs = feature_extractor(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 = feature_extractor.post_process(outputs, 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()]
+    # let's only keep detections with score > 0.7
+    if score > 0.7:
+        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 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. 
+
+### 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}
+}
+```