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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
