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petr

Results and Models

COCO

Model Backbone Lr schd mAP AP50 AP75 APM APL Config Download
PETR R-50 100e 68.8 87.5 76.3 62.7 77.7 config Google Drive | BaiduYun
PETR R-101 100e 70.0 88.5 77.5 63.6 79.4 config Google Drive | BaiduYun
PETR Swin-L 100e 73.1 90.7 80.9 67.2 81.7 config Google Drive | BaiduYun

CrowdPose

Model Backbone Lr schd Flip test mAP AP50 AP75 APE APM APH Config Download
PETR Swin-L 100e N 71.7 90.0 78.3 77.5 72.0 65.8 config Google Drive | BaiduYun
PETR Swin-L 100e Y 72.3 90.8 78.8 78.7 72.9 65.5 config Google Drive | BaiduYun

NOTE

  1. Swin-L are trained with batch size 16 due to GPU memory limitation.
  2. The performance is unstable. PETR may fluctuate about 0.2 mAP.

Citation

@inproceedings{shi2022end,
  title={End-to-End Multi-Person Pose Estimation With Transformers},
  author={Shi, Dahu and Wei, Xing and Li, Liangqi and Ren, Ye and Tan, Wenming},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11069--11078},
  year={2022}
}