readme: ---
  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, single scale
  
  Deformable DEtection TRansformer (DETR), single scale 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 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("SenseTime/deformable-detr-single-scale")
  model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale")
  
  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}"
      )
  ```
  
  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}
  }
  ```