RF-DETR: SOTA Real-Time Object Detection Model

RF-DETR is a real-time, transformer-based object detection model developed by Roboflow and released under the Apache 2.0 license.

RF-DETR-N outperforms YOLO11-N by 10 mAP points on the Microsoft COCO benchmark while running faster at inference. On RF100-VL, RF-DETR achieves state-of-the-art results, with RF-DETR-M beating YOLO11-M by an average of 5 mAP points across aerial datasets including drone, satellite, and radar.

News

• 2025/09/02: RF-DETR fine-tuning YouTube tutorial released. Learn step-by-step how to fine-tune RF-DETR on your custom dataset.
• 2025/07/23: Released three new checkpoints for RF-DETR: Nano, Small, and Medium.
• 2025/05/16: Added optimize_for_inference method, improving native PyTorch inference speed by up to 2x depending on platform.
• 2025/04/03: Introduced early stopping, gradient checkpointing, metric saving, training resume, TensorBoard, and W&B logging.
• 2025/03/20: Released RF-DETR real-time object detection model. Code and checkpoints for RF-DETR-Large and RF-DETR-Base are available.

Results

RF-DETR achieves state-of-the-art performance on both the Microsoft COCO and the RF100-VL benchmarks.

| Architecture | COCO AP₅₀ | COCO AP_50:95 | RF100VL AP₅₀ | RF100VL AP_50:95 | Latency (ms) | Params (M) | |:------------:|:--------------------:|:--------------------------:|:--------------------------:|:---------------------------:|:---------------:|:------------:| | RF-DETR-N | 67.6 | 48.4 | 84.1 | 57.1 | 2.32 | 30.5 | | RF-DETR-S | 72.1 | 53.0 | 85.9 | 59.6 | 3.52 | 32.1 | | RF-DETR-M | 73.6 | 54.7 | 86.6 | 60.6 | 4.52 | 33.7 | | YOLO11-N | 52.0 | 37.4 | 81.4 | 55.3 | 2.49 | 2.6 | | YOLO11-S | 59.7 | 44.4 | 82.3 | 56.2 | 3.16 | 9.4 | | YOLO11-M | 64.1 | 48.6 | 82.5 | 56.5 | 5.13 | 20.1 | | YOLO11-L | 65.3 | 50.2 | x | x | 6.65 | 25.3 | | YOLO11-X | 66.5 | 51.2 | x | x | 11.92 | 56.9 | | LW-DETR-T | 60.7 | 42.9 | x | x | 1.91 | 12.1 | | LW-DETR-S | 66.8 | 48.0 | 84.5 | 58.0 | 2.62 | 14.6 | | LW-DETR-M | 72.0 | 52.6 | 85.2 | 59.4 | 4.49 | 28.2 | | D-FINE-N | 60.2 | 42.7 | 83.6 | 57.7 | 2.12 | 3.8 | | D-FINE-S | 67.6 | 50.7 | 84.5 | 59.9 | 3.55 | 10.2 | | D-FINE-M | 72.6 | 55.1 | 84.6 | 60.2 | 5.68 | 19.2 |

See our benchmark notes in the RF-DETR documentation.

We are actively working on RF-DETR Large and X-Large models using the same techniques we used to achieve the strong accuracy that RF-DETR Medium attains. This is why RF-DETR Large and X-Large is not yet reported on our pareto charts and why we haven't benchmarked other models at similar sizes. Check back in the next few weeks for the launch of new RF-DETR Large and X-Large models.

Installation

To install RF-DETR, install the rfdetr package in a Python>=3.9 environment with pip:

bash pip install rfdetr

Inference

The easiest path to deployment is using Roboflow's Inference package.

The code below lets you run rfdetr-base on an image:

```python import os import supervision as sv from inference import get_model from PIL import Image from io import BytesIO import requests

url = "https://media.roboflow.com/dog.jpeg" image = Image.open(BytesIO(requests.get(url).content))

model = get_model("rfdetr-base")

predictions = model.infer(image, confidence=0.5)[0]

detections = sv.Detections.from_inference(predictions)

labels = [prediction.class_name for prediction in predictions.predictions]

annotatedimage = image.copy() annotatedimage = sv.BoxAnnotator(color=sv.ColorPalette.ROBOFLOW).annotate(annotatedimage, detections) annotatedimage = sv.LabelAnnotator(color=sv.ColorPalette.ROBOFLOW).annotate(annotated_image, detections, labels) ```

Predict

You can also use the .predict method to perform inference during local development. The .predict() method accepts various input formats, including file paths, PIL images, NumPy arrays, and torch tensors. Please ensure inputs use RGB channel order. For torch.Tensor inputs specifically, they must have a shape of (3, H, W) with values normalized to the [0..1) range. If you don't plan to modify the image or batch size dynamically at runtime, you can also use .optimize_for_inference() to get up to 2x end-to-end speedup, depending on platform.

```python import io import requests import supervision as sv from PIL import Image from rfdetr import RFDETRBase from rfdetr.util.cococlasses import COCOCLASSES

model = RFDETRBase()

model.optimizeforinference()

url = "https://media.roboflow.com/notebooks/examples/dog-2.jpeg"

image = Image.open(io.BytesIO(requests.get(url).content)) detections = model.predict(image, threshold=0.5)

labels = [ f"{COCOCLASSES[classid]} {confidence:.2f}" for classid, confidence in zip(detections.classid, detections.confidence) ]

annotatedimage = image.copy() annotatedimage = sv.BoxAnnotator().annotate(annotatedimage, detections) annotatedimage = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

sv.plotimage(annotatedimage) ```

Train a Model

You can fine-tune an RF-DETR Nano, Small, Medium, and Base model with a custom dataset using the rfdetr Python package.

Read our training tutorial to get started

Documentation

Visit our documentation website to learn more about how to use RF-DETR.

License

Both the code and the weights pretrained on the COCO dataset are released under the Apache 2.0 license.

Acknowledgements

Our work is built upon LW-DETR, DINOv2, and Deformable DETR. Thanks to their authors for their excellent work!

Citation

If you find our work helpful for your research, please consider citing the following BibTeX entry.

bibtex @software{rf-detr, author = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei}, license = {Apache-2.0}, title = {RF-DETR}, howpublished = {\url{https://github.com/roboflow/rf-detr}}, year = {2025}, note = {SOTA Real-Time Object Detection Model} }

Contribute

We welcome and appreciate all contributions! If you notice any issues or bugs, have questions, or would like to suggest new features, please open an issue or pull request. By sharing your ideas and improvements, you help make RF-DETR better for everyone.