We apologize to close this GitHub due to some issue. Our code will be released in this link after security check. I guess next week. However if you have questions, please mail to me. I am happy to wait for your question mail. [email protected]

• python 3
• pytorch >= 0.4.1
• torchvision==0.2.1
• opencv-python==3.4.2.17
• numpy
• tensorboardX
• visdom

ExtremeC3Net (paper)

Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo, Nojun Kwak.

"ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules"

• config file : extremeC3Net.json
• Param : 0.038 M
• Flop : 0.128 G
• IoU : 94.98

SINet (will be soon)

Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo, Nicolas Monet, Nojun Kwak

SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder

• config file : SINet.json
• Param : 0.087 M
• Flop : 0.064 G
• IoU : 95.29

• Train

Download datasets

1 . ExtremeC3Net

python main.py --c ExtremeC3Net.json

2 . SINet (soon)

python main.py --c SINet.json

will be soon

We make augmented dataset from Baidu fashion dataset.

The original Baidu dataset link is here

EG1800 dataset link what I used in here

Our augmented dataset will be open again. We use all train and val dataset for training segmentation model.

If our works is useful to you, please add two papers.

@article{park2019extremec3net,
  title={ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules},
  author={Park, Hyojin and Sj{\"o}sund, Lars Lowe and Yoo, YoungJoon and Kwak, Nojun},
  journal={arXiv preprint arXiv:1908.03093},
  year={2019}
}

SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
( Soon )

We are grateful to Clova AI, NAVER with valuable discussions.

I also appreciate my co-authors Lars Lowe Sjösund and YoungJoon Yoo from Clova AI, NAVER, and Nicolas Monet from NAVER LABS Europe.