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Aggregated residual transformations for deep neural networks

Introduction

[BACKBONE]

@inproceedings{xie2017aggregated,
  title={Aggregated residual transformations for deep neural networks},
  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1492--1500},
  year={2017}
}

Results and models

2d Human Pose Estimation

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_resnext_50 256x192 0.714 0.898 0.789 0.771 0.937 ckpt log
pose_resnext_50 384x288 0.724 0.899 0.794 0.777 0.935 ckpt log
pose_resnext_101 256x192 0.726 0.900 0.801 0.782 0.940 ckpt log
pose_resnext_101 384x288 0.743 0.903 0.815 0.795 0.939 ckpt log
pose_resnext_152 256x192 0.730 0.904 0.808 0.786 0.940 ckpt log
pose_resnext_152 384x288 0.742 0.902 0.810 0.794 0.939 ckpt log

Results on MPII val set

Arch Input Size Mean [email protected] ckpt log
pose_resnext_152 256x256 0.887 0.294 ckpt log