README.md

Shufflenet v2: Practical guidelines for efficient cnn architecture design

Introduction

[BACKBONE]

@inproceedings{ma2018shufflenet,
  title={Shufflenet v2: Practical guidelines for efficient cnn architecture design},
  author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={116--131},
  year={2018}
}

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_shufflenetv2	256x192	0.599	0.854	0.663	0.664	0.899	ckpt	log
pose_shufflenetv2	384x288	0.636	0.865	0.705	0.697	0.909	ckpt	log

Results on MPII val set

Arch	Input Size	Mean	[email protected]	ckpt	log
pose_shufflenetv2	256x256	0.828	0.205	ckpt	log