- python 3.6
- pytorch >= 0.4.1
- torchvision>=0.2.1
- opencv-python>=3.4.2.17
- numpy
- tensorflow>=1.13.0
- visdom
Our SINET is included Qualcomm AI hub!!!!! The model can support Pixel, Galaxy, Xiaomi and so on. Please download in here
Hyojin Park, Youngjoon Yoo, Geonseok Seo, Dongyoon Han, Sangdoo Yun, Nojun Kwak " C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation " (paper)
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" (paper)
| Model | # of Param(M) | # of Flop(G) | size for Flop | IoU( val ) | IoU (test) | server link |
|---|---|---|---|---|---|---|
| C3Net[2,3,7,13] | 0.19 | 3.15 | 512*1024 | 66.87 | 64.78 | link |
| C3NetV2[2,4,8,16] | 0.18 | 2.66 | 512*1024 | 66.28 | 65.48 | link |
| SINet | 0.12 | 1.22 | 1024*2048 | 68.22 | 66.46 | link |
- C3NetV2 has same encoder structure with C3Net, but uses bilinear upsampling for a decodder structure.
- SINet is accepted in WACV2020.
Once you train the model, my code automatically export format for Cityscape Testserver from best training model. I used P-40 GPU for training. C3 and C3_V2 require 2 GPU and SINet needs 1 GPU. Train validation txt is for datalodaer function here
python main_multiscale.py -c C3.json
python main_multiscale.py -c C3_V2.json
python main_Auxloss.py -c SINet.jsonIf our works is useful to you, please add two papers.
@article{park2018concentrated,
title={Concentrated-Comprehensive Convolutions for lightweight semantic segmentation},
author={Park, Hyojin and Yoo, Youngjoon and Seo, Geonseok and Han, Dongyoon and Yun, Sangdoo and Kwak, Nojun},
journal={arXiv preprint arXiv:1812.04920},
year={2018}
}
@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}
}
We are grateful to Clova AI, NAVER with valuable discussions.
I also appreciate my co-authors YoungJoon Yoo, Dongyoon Han, Sangdoo Yun and Lars Lowe Sjösund from Clova AI, NAVER, Nicolas Monet from NAVER LABS Europe and Jihwan Bang from Search Solutions, Inc
I refer ESPNet code for constructing my experiments and also appreciate Sachin Mehta for valuable comments. Sachin Mehta is ESPNet and ESPNetV2 author.
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