lparolari / RT-DETR

[CVPR 2024] Official RT-DETR (RTDETR paddle pytorch), Real-Time DEtection TRansformer, DETRs Beat YOLOs on Real-time Object Detection. 🔥 🔥 🔥

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

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Fig

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

Updates!!!

• [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 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

Implementations

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

Model	Epoch	Input shape	Dataset	$AP^{val}$	$AP^{val}_{50}$	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.

Introduction

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.

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}
}