README.md

Squeeze-and-excitation networks

Introduction

@inproceedings{hu2018squeeze,
  title={Squeeze-and-excitation networks},
  author={Hu, Jie and Shen, Li and Sun, Gang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7132--7141},
  year={2018}
}

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_seresnet_50	256x192	0.728	0.900	0.809	0.784	0.940	ckpt	log
pose_seresnet_50	384x288	0.748	0.905	0.819	0.799	0.941	ckpt	log
pose_seresnet_101	256x192	0.734	0.904	0.815	0.790	0.942	ckpt	log
pose_seresnet_101	384x288	0.753	0.907	0.823	0.805	0.943	ckpt	log
pose_seresnet_152*	256x192	0.730	0.899	0.810	0.786	0.940	ckpt	log
pose_seresnet_152*	384x288	0.753	0.906	0.823	0.806	0.945	ckpt	log

Note that * means without imagenet pre-training.