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+*This model was published in HF papers on 2020-10-08 and contributed to Hugging Face Transformers on 2022-09-14.*
+
+
+# Deformable DETR
+
+[Deformable DETR](https://huggingface.co/papers/2010.04159) improves on the original [DETR](./detr) by using a deformable attention module. This mechanism selectively attends to a small set of key sampling points around a reference. It improves training speed and improves accuracy.
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
+alt="drawing" width="600"/>
+
+<small> Deformable DETR architecture. Taken from the <a href="https://huggingface.co/papers/2010.04159">original paper</a>.</small>
+
+You can find all the available Deformable DETR checkpoints under the [SenseTime](https://huggingface.co/SenseTime) organization.
+
+> [!TIP]
+> This model was contributed by [nielsr](https://huggingface.co/nielsr).
+>
+> Click on the Deformable DETR models in the right sidebar for more examples of how to apply Deformable DETR to different object detection and segmentation tasks.
+
+The example below demonstrates how to perform object detection with the [`Pipeline`] and the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    "object-detection",
+    model="SenseTime/deformable-detr",
+    device_map=0
+)
+
+pipeline("http://images.cocodataset.org/val2017/000000039769.jpg")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import requests
+import torch
+from PIL import Image
+
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr")
+model = AutoModelForObjectDetection.from_pretrained("SenseTime/deformable-detr", device_map="auto")
+
+# prepare image for the model
+inputs = image_processor(images=image, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
+
+for result in results:
+    for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
+        score, label = score.item(), label_id.item()
+        box = [round(i, 2) for i in box.tolist()]
+        print(f"{model.config.id2label[label]}: {score:.2f} {box}")
+```
+
+</hfoption>
+</hfoptions>
+
+## Resources
+
+- Refer to this set of [notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Deformable-DETR) for inference and fine-tuning [`DeformableDetrForObjectDetection`] on a custom dataset.
+
+## DeformableDetrImageProcessor
+
+[[autodoc]] DeformableDetrImageProcessor
+    - preprocess
+    - post_process_object_detection
+
+## DeformableDetrImageProcessorPil
+
+[[autodoc]] DeformableDetrImageProcessorPil
+    - preprocess
+    - post_process_object_detection
+
+## DeformableDetrConfig
+
+[[autodoc]] DeformableDetrConfig
+
+## DeformableDetrModel
+
+[[autodoc]] DeformableDetrModel
+    - forward
+
+## DeformableDetrForObjectDetection
+
+[[autodoc]] DeformableDetrForObjectDetection
+    - forward