Advancing State of the Art Object Detection (Again) with RF-DETR

In March 2025, RF-DETR was introduced as the first real-time object detection model to surpass 60+ mAP on the Microsoft COCO benchmark, with exceptional domain generalization on the RF100-VL benchmark. The RF-DETR family, now expanded with Nano, Small, and Medium models, is recognized for its speed and accuracy in object detection, outperforming other real-time models like YOLO and LW-DETR. These models, licensed under Apache 2.0, are designed for high-speed, accurate performance even on limited compute, and offer robust adaptability to various datasets. The RF-DETR models leverage transformer-based architecture, providing superior results in mAP50 and mAP50:95 across both COCO and RF100-VL benchmarks. They are available for training and deployment through Roboflow's platform and the RF-DETR open-source Python package. The ongoing development of RF-DETR includes plans for further enhancements based on community feedback, focusing on edge deployment, CoreML support, and additional features like segmentation and classification.