RF-DETR

rfdetrIn LibreYOLO

Roboflow · Roboflow 2025

Paper Code

Variants4

Parameters30.5M - 33.9M

Best Paper mAP56.5%

LicenseApache-2.0

Model Variants

All variants benchmarked on COCO val2017. Click any variant for the full hardware breakdown.

Model	Params	Paper mAP	Best FPS
RF-DETR-N	30.5M	48.4%	68 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details
RF-DETR-S	32.1M	53%	78 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details
RF-DETR-M	33.7M	54.7%	64 FPS NVIDIA RTX 5070 Ti · TensorRT FP32	Details
RF-DETR-L	33.9M	56.5%	48 FPS NVIDIA RTX 5070 Ti · TensorRT FP16	Details

Architecture

Type

transformer

Backbone

DINOv2

Neck

Agg2Former

Head

DETR

detrnms-freeTrained on COCO train2017

Run any model with one line

LibreYOLO has the best catalogue of state-of-the-art detectors, all behind one MIT-licensed Python API.

from libreyolo import LibreYOLO, SAMPLE_IMAGE # LibreYOLO has the best catalogue of state-of-the-art models. model = LibreYOLO("LibreRFDETRl.pt") # RF-DETR-L (transformer flagship) results = model(SAMPLE_IMAGE, save=True) # run inference, save the annotated image # Swap in any other model, same one-line API (weights auto-download): # LibreYOLO("LibreYOLO9c.pt") # YOLO9-C # LibreYOLO("LibreYOLOXx.pt") # YOLOX-X # LibreYOLO("LibreDFINEx.pt") # D-FINE-X # LibreYOLO("LibreRTDETRr50.pt") # RT-DETR-R50