RF-DETR: Real-Time SOTA Object Detection, Instance Segmentation, and Keypoint Detection
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, 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 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-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.
https://github.com/user-attachments/assets/add23fd1-266f-4538-8809-d7dd5767e8e6
Install
To install RF-DETR, install the rfdetr package in a Python>=3.10 environment with pip.
pip install rfdetr
By installing RF-DETR from source, you can explore the most recent features and enhancements that have not yet been officially released. Please note that these updates are still in development and may not be as stable as the latest published release.
pip install https://github.com/roboflow/rf-detr/archive/refs/heads/develop.zip
Benchmarks
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 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.
Detection
| Architecture | COCO AP50 | COCO AP50:95 | RF100VL AP50 | RF100VL AP50:95 | Latency (ms) | Params (M) | Resolution | License |
|---|
| RF-DETR-N | 67.6 | 48.4 | 85.0 | 57.7 | 2.3 | 30.5 | 384x384 | Apache 2.0 |
| RF-DETR-S | 72.1 | 53.0 | 86.7 | 60.2 | 3.5 | 32.1 | 512x512 | Apache 2.0 |
| RF-DETR-M | 73.6 | 54.7 | 87.4 | 61.2 | 4.4 | 33.7 | 576x576 | Apache 2.0 |
| RF-DETR-L | 75.1 | 56.5 | 88.2 | 62.2 | 6.8 | 33.9 | 704x704 | Apache 2.0 |
| RF-DETR-XL △ | 77.4 | 58.6 | 88.5 | 62.9 | 11.5 | 126.4 | 700x700 | PML 1.0 |
| RF-DETR-2XL △ | 78.5 | 60.1 | 89.0 | 63.2 | 17.2 | 126.9 | 880x880 | PML 1.0 |
| YOLO11-N | 52.0 | 37.4 |
Segmentation
| Architecture | COCO AP50 | COCO AP50:95 | Latency (ms) | Params (M) | Resolution | License |
|---|
| RF-DETR-Seg-N | 63.0 | 40.3 | 3.4 | 33.6 | 312x312 | Apache 2.0 |
| RF-DETR-Seg-S | 66.2 | 43.1 | 4.4 | 33.7 | 384x384 | Apache 2.0 |
| RF-DETR-Seg-M | 68.4 | 45.3 | 5.9 | 35.7 | 432x432 | Apache 2.0 |
| RF-DETR-Seg-L | 70.5 | 47.1 | 8.8 | 36.2 | 504x504 | Apache 2.0 |
| RF-DETR-Seg-XL | 72.2 | 48.8 | 13.5 | 38.1 | 624x624 | Apache 2.0 |
| RF-DETR-Seg-2XL | 73.1 | 49.9 | 21.8 | 38.6 | 768x768 | Apache 2.0 |
| YOLOv8-N-Seg | 45.6 | 28.3 | 3.5 | 3.4 | 640x640 | AGPL-3.0 |
| YOLOv8-S-Seg | 53.8 | 34.0 | 4.2 | 11.8 | 640x640 | AGPL-3.0 |
| YOLOv8-M-Seg |