Unofficial Tensorflow 2 implementation of SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
- python 3
- opencv-python
- numpy
- tensorflow >=2.3.1
- albumentations
SINet (paper) Accepted in WACV2020
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo, Nicolas Monet, Jihwan Bang, Nojun Kwak
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
- Preparing dataset
if you use custom dataset, fix the code and parms in data_loader.py(DatasetLoader's "Load" call).
- Train
nohup python3 distributed_train.py>train.log 2>&1 &- View Log
tail -f train.log- TensorBoard
tensorboard --logdir ./training/@article{park2019sinet,
title={SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder},
author={Park, Hyojin and Sj{\"o}sund, Lars Lowe and Monet, Nicolas and Yoo, YoungJoon and Kwak, Nojun},
journal={arXiv preprint arXiv:1911.09099},
year={2019}
}
@article{heo2020adamp,
title={Slowing Down the Weight Norm Increase in Momentum-based Optimizers},
author={Heo, Byeongho and Chun, Sanghyuk and Oh, Seong Joon and Han, Dongyoon and Yun, Sangdoo and Uh, Youngjung and Ha, Jung-Woo},
year={2020},
journal={arXiv preprint arXiv:2006.08217},
}Try to provide you with a relatively concise and standardized TensorFlow 2 custom training code example.
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