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
  ---
  
  # Conditional DETR model with ResNet-50 backbone
  
  Conditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Meng et al. and first released in [this repository](https://github.com/Atten4Vis/ConditionalDETR). 
  
  
  ## Model description
  
  The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101.
  
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg)
  
  ## Intended uses & limitations
  
  You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=microsoft/conditional-detr) to look for all available Conditional DETR models.
  
  ### How to use
  
  Here is how to use this model:
  
  ```python
  from transformers import AutoImageProcessor, ConditionalDetrForObjectDetection
  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("microsoft/conditional-detr-resnet-50")
  model = ConditionalDetrForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50")
  
  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}"
      )
  ```
  This should output:
  ```
  Detected remote with confidence 0.833 at location [38.31, 72.1, 177.63, 118.45]
  Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0]
  Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95]
  ```
  
  Currently, both the feature extractor and model support PyTorch. 
  
  ## Training data
  
  The Conditional 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
  @inproceedings{MengCFZLYS021,
    author    = {Depu Meng and
                 Xiaokang Chen and
                 Zejia Fan and
                 Gang Zeng and
                 Houqiang Li and
                 Yuhui Yuan and
                 Lei Sun and
                 Jingdong Wang},
    title     = {Conditional {DETR} for Fast Training Convergence},
    booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV}
                 2021, Montreal, QC, Canada, October 10-17, 2021},
  }
  ```