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This is the official implementation of the paper "DETRs Beat YOLOs on Real-time Object Detection".
- [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
Model | Epoch | Input shape | Dataset | Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS) | ||
---|---|---|---|---|---|---|---|---|
RT-DETR-R18 | 6x | 640 | COCO | 46.5 | 63.8 | 20 | 60 | 217 |
RT-DETR-R34 | 6x | 640 | COCO | 48.9 | 66.8 | 31 | 92 | 161 |
RT-DETR-R50-m | 6x | 640 | COCO | 51.3 | 69.6 | 36 | 100 | 145 |
RT-DETR-R50 | 6x | 640 | COCO | 53.1 | 71.3 | 42 | 136 | 108 |
RT-DETR-R101 | 6x | 640 | COCO | 54.3 | 72.7 | 76 | 259 | 74 |
RT-DETR-HGNetv2-L | 6x | 640 | COCO | 53.0 | 71.6 | 32 | 110 | 114 |
RT-DETR-HGNetv2-X | 6x | 640 | COCO | 54.8 | 73.1 | 67 | 234 | 74 |
RT-DETR-R18 | 5x | 640 | COCO + Objects365 | 49.2 | 66.6 | 20 | 60 | 217 |
RT-DETR-R50 | 2x | 640 | COCO + Objects365 | 55.3 | 73.4 | 42 | 136 | 108 |
RT-DETR-R101 | 2x | 640 | COCO + Objects365 | 56.2 | 74.6 | 76 | 259 | 74 |
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}
}