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RT-DETR: DETRs Beat YOLOs on Real-time Object Detection

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This is the official implementation of the paper "DETRs Beat YOLOs on Real-time Object Detection".

Updates!!!

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• [2023.08.15] Release rtdetr-r101 pretrained models on objects365. 56.2 mAP and 74 FPS.
• [2023.07.30] Release rtdetr-r50 pretrained models on objects365. 55.3 mAP and 108 FPS.
• [2023.07.28] Fix some bugs, and add some comments. 1, 2
• [2023.07.13] Upload training logs on coco
• [2023.05.17] Release RT-DETR-R18, RT-DETR-R34, RT-DETR-R50-m（example for scaled)
• [2023.04.17] Release RT-DETR-R50, RT-DETR-R101, RT-DETR-L, RT-DETR-X

Implementations

• rtdetr-paddle
• rtdetr-pytorch

Introduction

We propose a Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge. Our RT-DETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RT-DETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR-R50 by 2.2% AP in accuracy and by about 21 times in FPS.

Citation

If you use RT-DETR in your work, please use the following BibTeX entries:

@misc{lv2023detrs,
      title={DETRs Beat YOLOs on Real-time Object Detection},
      author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
      year={2023},
      eprint={2304.08069},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}