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RF-DETR: Real-Time SOTA Detection and Segmentation Model

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.

Install

You can install and use rfdetr in a Python>=3.10 environment. For detailed installation instructions, including installing from source, and setting up a local development environment, check out our install page.

Installation

version python-version license downloads

pip install rfdetr

uv pip install rfdetr

For uv projects:

uv add rfdetr

Quickstart

  • Run Detection Models

    Load and run pre-trained RF-DETR detection models.

    Tutorial

  • Run Segmentation Models

    Load and run pre-trained RF-DETR-Seg segmentation models.

    Tutorial

  • Train Models

    Learn how to fine-tune RF-DETR models for detection and segmentation.

    Tutorial

Tutorials

  • Train RF-DETR on a Custom Dataset. Video

    End to end walkthrough of training RF-DETR on a custom dataset.

    Watch the video

  • Deploy RF-DETR to NVIDIA Jetson. Article

    Instructions for deploying RF-DETR on NVIDIA Jetson with Roboflow Inference.

    Read the tutorial

  • Train and Deploy RF-DETR with Roboflow

    Cloud training and hardware deployment workflow using Roboflow.

    Read the tutorial

Benchmarks

RF-DETR achieves the best accuracy–latency trade-off among real-time object detection and instance segmentation models — both on COCO and on the more demanding RF100-VL benchmark (domain adaptability). For detailed benchmark tables and methodology, check out our benchmarks page.

Detection

Pareto front – detection

Architecture COCO AP50 COCO AP50:95 RF100VL AP50 RF100VL AP50:95 Latency (ms) Params (M) Resolution
RF-DETR-N 67.6 48.4 85.0 57.7 2.3 30.5 384×384
RF-DETR-S 72.1 53.0 86.7 60.2 3.5 32.1 512×512
RF-DETR-M 73.6 54.7 87.4 61.2 4.4 33.7 576×576
RF-DETR-L 75.1 56.5 88.2 62.2 6.8 33.9 704×704
RF-DETR-XL 77.4 58.6 88.5 62.9 11.5 126.4 700×700
RF-DETR-2XL 78.5 60.1 89.0 63.2 17.2 126.9 880×880

Segmentation

Pareto front – segmentation

Architecture COCO AP50 COCO AP50:95 Latency (ms) Params (M) Resolution
RF-DETR-Seg-N 63.0 40.3 3.4 33.6 312×312
RF-DETR-Seg-S 66.2 43.1 4.4 33.7 384×384
RF-DETR-Seg-M 68.4 45.3 5.9 35.7 432×432
RF-DETR-Seg-L 70.5 47.1 8.8 36.2 504×504
RF-DETR-Seg-XL 72.2 48.8 13.5 38.1 624×624
RF-DETR-Seg-2XL 73.1 49.9 21.8 38.6 768×768