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Deep high-resolution representation learning for human pose estimation

Introduction

@inproceedings{sun2019deep,
  title={Deep high-resolution representation learning for human pose estimation},
  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={5693--5703},
  year={2019}
}

Results and models

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x192 0.746 0.904 0.819 0.799 0.942 ckpt log
pose_hrnet_w32 384x288 0.760 0.906 0.829 0.810 0.943 ckpt log
pose_hrnet_w48 256x192 0.756 0.907 0.825 0.806 0.942 ckpt log
pose_hrnet_w48 384x288 0.767 0.910 0.831 0.816 0.946 ckpt log

Results on AIC val set.

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x192 0.675 0.957 0.751 0.703 0.961 ckpt log

Results on MPII val set.

Arch Input Size Mean Mean@0.1 ckpt log
pose_hrnet_w32 256x256 0.900 0.379 ckpt log
pose_hrnet_w48 256x256 0.900 0.383 ckpt log