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*This model was published in HF papers on 2024-07-24 and contributed to Hugging Face Transformers on 2026-05-07.*

<div style="float: right;">
 <div class="flex flex-wrap space-x-1">
  <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
 </div>
</div>

# RF-DETR

[RF-DETR](https://github.com/roboflow/rf-detr) proposes a Receptive Field Detection Transformer (DETR) architecture
designed to compete with and surpass the dominant YOLO series for real-time object detection. It achieves a new
state-of-the-art balance between speed (latency) and accuracy (mAP) by combining recent transformer advances with
efficient design choices.

The RF-DETR architecture is characterized by its simple and efficient structure: a DINOv2 Backbone, a Projector, and a
shallow DETR Decoder.
It enhances the DETR architecture for efficiency and speed using the following core modifications:

1. **DINOv2 Backbone**: Uses a powerful DINOv2 backbone for robust feature extraction.
2. **Group DETR Training**: Utilizes Group-Wise One-to-Many Assignment during training to accelerate convergence.
3. **Richer Input**: Aggregates multi-level features from the backbone and uses a C2f Projector (similarly to YOLOv8) to
   pass multi-scale features.
4. **Faster Decoder**: Employs a shallow 3-layer DETR decoder with deformable cross-attention for lower latency.
5. **Optimized Queries**: Uses a mixed-query scheme combining learnable content queries and generated spatial queries.

You can find all the available RF-DETR checkpoints under the [Roboflow](https://huggingface.co/Roboflow)
organization.
The original code can be found [here](https://github.com/roboflow/rf-detr).

Thanks to the weight conversion mapping, RfDetr is compatible with models from the original
[rf-detr](https://github.com/roboflow/rf-detr) library as well as models that you trained using the
[Roboflow](https://roboflow.com/) platform. This means you can use Roboflow platform to train your model and use
`RfDetr` in `transformers` to import the weights and deploy your model anywhere.


> [!TIP]
>
> Click on the RF-DETR models in the right sidebar for more examples of how to apply RF-DETR to different object
> detection 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
import torch

pipeline = pipeline("object-detection", model="Roboflow/rf-detr-medium", device_map="auto")

pipeline("http://images.cocodataset.org/val2017/000000039769.jpg")
```

</hfoption>
<hfoption id="AutoModel">

```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import httpx
from io import BytesIO
import torch

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(BytesIO(httpx.get(url).content))

image_processor = AutoImageProcessor.from_pretrained("Roboflow/rf-detr-medium")
model = AutoModelForObjectDetection.from_pretrained("Roboflow/rf-detr-medium", 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>

### Visualizing results with supervision

You can use the [supervision](https://github.com/roboflow/supervision) library to visualize detection and segmentation results. Install it with `pip install supervision`.

<hfoptions id="supervision">
<hfoption id="Object Detection">

```python
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import supervision as sv

import httpx
from io import BytesIO
import torch

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(BytesIO(httpx.get(url).content))

image_processor = AutoImageProcessor.from_pretrained("Roboflow/rf-detr-medium")
model = AutoModelForObjectDetection.from_pretrained("Roboflow/rf-detr-medium", device_map="auto")

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.height, image.width]), threshold=0.3
)[0]

detections = sv.Detections.from_transformers(
    transformers_results=results, id2label=model.config.id2label
)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated_image = image.copy()
annotated_image = box_annotator.annotate(annotated_image, detections)
annotated_image = label_annotator.annotate(annotated_image, detections)

sv.plot_image(annotated_image)
```

</hfoption>
<hfoption id="Instance Segmentation">

```python
from transformers import AutoImageProcessor, AutoModelForInstanceSegmentation
from PIL import Image
import supervision as sv
import httpx
from io import BytesIO
import torch

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(BytesIO(httpx.get(url).content))

image_processor = AutoImageProcessor.from_pretrained("Roboflow/rf-detr-seg-medium")
model = AutoModelForInstanceSegmentation.from_pretrained("Roboflow/rf-detr-seg-medium", device_map="auto")

inputs = image_processor(images=image, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

results = image_processor.post_process_instance_segmentation(
    outputs, target_sizes=[image.size[::-1]], threshold=0.3
)[0]

detections = sv.Detections.from_transformers(
    transformers_results=results, id2label=model.config.id2label
)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator()

annotated_image = image.copy()
annotated_image = mask_annotator.annotate(annotated_image, detections)
annotated_image = label_annotator.annotate(annotated_image, detections)

sv.plot_image(annotated_image)
```

</hfoption>
</hfoptions>

## Resources


- Scripts for finetuning [`RfDetrForObjectDetection`] with [`Trainer`]
  or [Accelerate](https://huggingface.co/docs/accelerate/index) can be
  found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
- See also: [Object detection task guide](../tasks/object_detection).

## RfDetrConfig

[[autodoc]] RfDetrConfig

## RfDetrDinov2Config

[[autodoc]] RfDetrDinov2Config

## RfDetrImageProcessor

[[autodoc]] RfDetrImageProcessor
    - preprocess
    - post_process_object_detection
    - post_process_instance_segmentation

## RfDetrModel

[[autodoc]] RfDetrModel
    - forward

## RfDetrForObjectDetection

[[autodoc]] RfDetrForObjectDetection
    - forward

## RfDetrForInstanceSegmentation

[[autodoc]] RfDetrForInstanceSegmentation
    - forward

## RfDetrDinov2Backbone

[[autodoc]] RfDetrDinov2Backbone
    - forward