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

• [2023.11.07] Add pytorch rtdetr_r34vd for requests #107, #114
• [2023.11.05] upgrade the logic of remap_mscoco_category to facilitate training of custom datasets, see detils in Train custom data part. #81
• [2023.10.23] Add discussion for deployments, supported onnxruntime, TensorRT, openVINO
• [2023.10.12] Add tuning code for pytorch version, now you can tuning rtdetr based on pretrained weights
• [2023.09.19] Upload pytorch weights convert from paddle version
• [2023.08.24] Release rtdetr-18 pretrained models on objects365. 49.2 mAP and 217 FPS
• [2023.08.22] Upload rtdetr_pytorch source code. Please enjoy it ❤️
• [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

• rtdetr paddle: code, weights
• rtdetr pytorch: code, weights

Notes:

• COCO + Objects365 in the table means finetuned model on COCO using pretrained weights trained on Objects365.

We propose a Real-Time DEtection TRansformer (RT-DETR, aka RTDETR), 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.

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}
}