roboflow/rf-detr

RF-DETR is a real-time transformer architecture for object detection, instance segmentation, and keypoint detection (preview) developed by Roboflow. Built on a DINOv2 vision transformer backbone (a PE-Core-T backbone for Atto, Femto and Pico), 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 (PE-Core-T for Atto, Femto and Pico) and supports object detection, instance segmentation, and keypoint detection (preview) in a single, consistent API. The open-source rfdetr package and Apache-designated models are released under Apache 2.0, while Plus components (rfdetr_plus, including RF-DETR-Atto/Femto/Pico/XL/2XL detection models) are licensed under PML 1.0.

The published RF-DETR sizes were created with neural architecture search (NAS) — and the same NAS method is now available on the Roboflow platform, so you can discover the best architecture for your own dataset. Learn more in the NAS docs.

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 (RF100-VL for detection only). The charts and tables below compare RF-DETR against other top real-time models across accuracy and latency for detection and segmentation. All COCO accuracy numbers are measured in-house for every model shown, computed with pycocotools in SAB over the full 5,000-image val2017 split, so every row is directly comparable and may differ from vendor-reported figures. The sole exception is rows marked †, which are quoted from the original authors' paper and were not measured in SAB. All latency numbers were measured on an NVIDIA T4 using TensorRT, FP16, and batch size 1. Parameter counts are deployment (fused) nn.Module parameter counts (model.parameters(), not the raw tensor count of the saved checkpoint), except rows marked †, which are the authors' reported counts. For full benchmarking methodology and reproducibility details, see roboflow/sab.

Keypoint benchmarks report AP_50:95 (OKS-based); this is the standard COCO keypoint comparison metric.

Since the paper release, we have improved RF-DETR NAS training on the Roboflow platform even further. A single training run gives you every model size, with results that beat not only the open-source checkpoints but also our own paper NAS results. Try it now!

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 supervision as sv
from rfdetr import RFDETRMedium
from rfdetr.assets.coco_classes import COCO_CLASSES

model = RFDETRMedium()

detections = model.predict("https://media.roboflow.com/dog.jpg", threshold=0.5)

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

annotated_image = sv.BoxAnnotator().annotate(detections.metadata["source_image"], detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

Note: COCO_CLASSES works for COCO-pretrained models. For fine-tuned models, use detections.data["class_name"] instead — it resolves class names from the checkpoint and works for both COCO and custom datasets.

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)

△ Requires the rfdetr_plus extension: pip install rfdetr[plus]. See License for details.

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 supervision as sv
from rfdetr import RFDETRSegMedium
from rfdetr.assets.coco_classes import COCO_CLASSES

model = RFDETRSegMedium()

detections = model.predict("https://media.roboflow.com/dog.jpg", threshold=0.5)

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

annotated_image = sv.MaskAnnotator().annotate(detections.metadata["source_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 keypoint detection (preview) with RFDETRKeypointPreview, pretrained on COCO person keypoints.

from rfdetr import RFDETRKeypointPreview

model = RFDETRKeypointPreview()
key_points = model.predict("image.jpg", threshold=0.5)

RF-DETR supports training for object detection, instance segmentation, and keypoint detection (preview). 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.

Licensing is split by component:

• The open-source rfdetr package and Apache-designated model weights are licensed under Apache License 2.0. See LICENSE.
• Plus components, including the rfdetr_plus extension and RF-DETR-XL / RF-DETR-2XL detection models, are licensed under PML 1.0.

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.

@inproceedings{robinson2026rfdetr,
  title     = {RF-DETR: Real-Time Detection Transformer},
  author    = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei and Ramanan, Deva and Peri, Neehar},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2026},
  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.

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