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

license prs issues issues arXiv emal

This is the official implementation of papers

Fig

🚀 Updates

  • [2024.10.10] Add sliced inference support for small object detecion. #468
  • [2024.09.23] Add ✅Regnet and DLA34 for RTDETR.
  • [2024.08.27] Add hubconf.py file to support torch hub.
  • [2024.08.22] Improve the performance of ✅ RT-DETRv2-S to 48.1 mAP (+1.6 compared to RT-DETR-R18).
  • [2024.07.24] Release ✅ RT-DETRv2!
  • [2024.02.27] Our work has been accepted to CVPR 2024!
  • [2024.01.23] Fix difference on data augmentation with paper in rtdetr_pytorch #84.
  • [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 RT-DETR-R18 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 RT-DETR-R101 pretrained models on objects365. 56.2 mAP and 74 FPS.
  • [2023.07.30] Release RT-DETR-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

Model Input shape Dataset $AP^{val}$ $AP^{val}_{50}$ Params(M) FLOPs(G) T4 TensorRT FP16(FPS)
RT-DETR-R18 640 COCO 46.5 63.8 20 60 217
RT-DETR-R34 640 COCO 48.9 66.8 31 92 161
RT-DETR-R50-m 640 COCO 51.3 69.6 36 100 145
RT-DETR-R50 640 COCO 53.1 71.3 42 136 108
RT-DETR-R101 640 COCO 54.3 72.7 76 259 74
RT-DETR-HGNetv2-L 640 COCO 53.0 71.6 32 110 114
RT-DETR-HGNetv2-X 640 COCO 54.8 73.1 67 234 74
RT-DETR-R18 640 COCO + Objects365 49.2 66.6 20 60 217
RT-DETR-R50 640 COCO + Objects365 55.3 73.4 42 136 108
RT-DETR-R101 640 COCO + Objects365 56.2 74.6 76 259 74
RT-DETRv2-S 640 COCO 48.1 (+1.6) 65.1 20 60 217
RT-DETRv2-M* 640 COCO 49.9 (+1.0) 67.5 31 92 161
RT-DETRv2-M 640 COCO 51.9 (+0.6) 69.9 36 100 145
RT-DETRv2-L 640 COCO 53.4 (+0.3) 71.6 42 136 108
RT-DETRv2-X 640 COCO 54.3 72.8 (+0.1) 76 259 74

Notes:

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

🦄 Performance

🏕️ Complex Scenarios

🌋 Difficult Conditions

Citation

If you use RT-DETR or RTDETRv2 in your work, please use the following BibTeX entries:

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

@misc{lv2024rtdetrv2improvedbaselinebagoffreebies,
      title={RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer}, 
      author={Wenyu Lv and Yian Zhao and Qinyao Chang and Kui Huang and Guanzhong Wang and Yi Liu},
      year={2024},
      eprint={2407.17140},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.17140}, 
}