RF-DETR is a real-time transformer architecture for object detection and instance segmentation developed by Roboflow. Built on a DINOv2 vision transformer backbone, RF-DETR delivers state-of-the-art accuracy and latency trade-offs on Microsoft COCO and RF100-VL.

RF-DETR uses a DINOv2 vision transformer backbone and supports both detection and instance segmentation in a single, consistent API. All core models and code are released under the Apache 2.0 license.

To install RF-DETR, install the rfdetr package in a Python>=3.10 environment with pip.

pip install rfdetr

pip install https://github.com/roboflow/rf-detr/archive/refs/heads/develop.zip

RF-DETR achieves state-of-the-art results in both object detection and instance segmentation, with benchmarks reported on Microsoft COCO and RF100-VL. The charts and tables below compare RF-DETR against other top real-time models across accuracy and latency for detection and segmentation. All latency numbers were measured on an NVIDIA T4 using TensorRT, FP16, and batch size 1. For full benchmarking methodology and reproducibility details, see roboflow/sab.

RF-DETR provides multiple model sizes, ranging from Nano to 2XLarge. To use a different model size, replace the class name in the code snippet below with another class from the table.

import requests
import supervision as sv
from PIL import Image
from rfdetr import RFDETRMedium
from rfdetr.util.coco_classes import COCO_CLASSES

model = RFDETRMedium()

image = Image.open(requests.get('https://media.roboflow.com/dog.jpg', stream=True).raw)
detections = model.predict(image, threshold=0.5)

labels = [
    f"{COCO_CLASSES[class_id]}"
    for class_id
    in detections.class_id
]

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

import requests
import supervision as sv
from PIL import Image
from inference import get_model

model = get_model("rfdetr-medium")

image = Image.open(requests.get('https://media.roboflow.com/dog.jpg', stream=True).raw)
predictions = model.infer(image, confidence=0.5)[0]
detections = sv.Detections.from_inference(predictions)

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

RF-DETR supports instance segmentation with model sizes from Nano to 2XLarge. To use a different model size, replace the class name in the code snippet below with another class from the table.

import requests
import supervision as sv
from PIL import Image
from rfdetr import RFDETRSegMedium
from rfdetr.util.coco_classes import COCO_CLASSES

model = RFDETRSegMedium()

image = Image.open(requests.get('https://media.roboflow.com/dog.jpg', stream=True).raw)
detections = model.predict(image, threshold=0.5)

labels = [
    f"{COCO_CLASSES[class_id]}"
    for class_id
    in detections.class_id
]

annotated_image = sv.MaskAnnotator().annotate(image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

import requests
import supervision as sv
from PIL import Image
from inference import get_model

model = get_model("rfdetr-seg-medium")

image = Image.open(requests.get('https://media.roboflow.com/dog.jpg', stream=True).raw)
predictions = model.infer(image, confidence=0.5)[0]
detections = sv.Detections.from_inference(predictions)

annotated_image = sv.MaskAnnotator().annotate(image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections)

RF-DETR supports training for both object detection and instance segmentation. You can train models in Google Colab or directly on the Roboflow platform. Below you will find a step-by-step video fine-tuning tutorial.

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

All source code and model weights are licensed under the Apache License 2.0. See LICENSE.core for details. RF-DETR XLarge and 2XLarge code and checkpoints are licensed under the Platform Model License 1.0. See LICENSE.platform for details. These models require a Roboflow account to run and fine-tune.

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

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

@misc{rf-detr,
    title={RF-DETR: Neural Architecture Search for Real-Time Detection Transformers},
    author={Isaac Robinson and Peter Robicheaux and Matvei Popov and Deva Ramanan and Neehar Peri},
    year={2025},
    eprint={2511.09554},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2511.09554},
}

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.