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Shufflenet: An extremely efficient convolutional neural network for mobile devices

Introduction

[BACKBONE]

@inproceedings{zhang2018shufflenet,
  title={Shufflenet: An extremely efficient convolutional neural network for mobile devices},
  author={Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={6848--6856},
  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_shufflenetv1 256x192 0.585 0.845 0.650 0.651 0.894 ckpt log
pose_shufflenetv1 384x288 0.622 0.859 0.685 0.684 0.901 ckpt log

Results on MPII val set

Arch Input Size Mean Mean@0.1 ckpt log
pose_shufflenetv1 256x256 0.823 0.228 ckpt log