ce2p

Introduction

Official Repo

Code Snippet

CE2P (AAAI'2019)

@inproceedings{ruan2019devil,
    title={Devil in the details: Towards accurate single and multiple human parsing},
    author={Ruan, Tao and Liu, Ting and Huang, Zilong and Wei, Yunchao and Wei, Shikui and Zhao, Yao},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={33},
    pages={4814--4821},
    year={2019}
}

Results

PASCAL VOC

Backbone	Pretrain	Crop Size	Schedule	Train/Eval Set	mIoU	Download
R-50-D8	ImageNet-1k-224x224	512x512	LR/POLICY/BS/EPOCH: 0.01/poly/16/60	trainaug/val	75.69%	cfg | model | log
R-50-D16	ImageNet-1k-224x224	512x512	LR/POLICY/BS/EPOCH: 0.01/poly/16/60	trainaug/val	74.58%	cfg | model | log
R-101-D8	ImageNet-1k-224x224	512x512	LR/POLICY/BS/EPOCH: 0.01/poly/16/60	trainaug/val	77.77%	cfg | model | log
R-101-D16	ImageNet-1k-224x224	512x512	LR/POLICY/BS/EPOCH: 0.01/poly/16/60	trainaug/val	76.84%	cfg | model | log

LIP

Backbone	Pretrain	Crop Size	Schedule	Train/Eval Set	mIoU	Download
R-50-D8	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	52.42%	cfg | model | log
R-50-D16	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	51.98%	cfg | model | log
R-101-D8	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	54.79%	cfg | model | log
R-101-D16	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	54.02%	cfg | model | log

CIHP

Backbone	Pretrain	Crop Size	Schedule	Train/Eval Set	mIoU	Download
R-50-D8	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	61.15%	cfg | model | log
R-50-D16	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	60.15%	cfg | model | log
R-101-D8	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	63.83%	cfg | model | log
R-101-D16	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	62.25%	cfg | model | log

ATR

Backbone	Pretrain	Crop Size	Schedule	Train/Eval Set	mIoU	Download
R-50-D8	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	78.02%	cfg | model | log
R-50-D16	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	77.62%	cfg | model | log
R-101-D8	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	78.57%	cfg | model | log
R-101-D16	ImageNet-1k-224x224	473x473	LR/POLICY/BS/EPOCH: 0.01/poly/32/150	train/val	78.25%	cfg | model | log

More

You can also download the model weights from following sources:

• BaiduNetdisk: https://pan.baidu.com/s/1gD-NJJWOtaHCtB0qHE79rA with access code s757