Pytorch implementation of Conditional image-to-image translation [1] (CVPR 2018)
python train.py --dataset dataset
The following shows basic folder structure.
├── data
├── dataset # not included in this repo
├── trainA
├── aaa.png
├── bbb.jpg
└── ...
├── trainB
├── ccc.png
├── ddd.jpg
└── ...
├── testA
├── eee.png
├── fff.jpg
└── ...
└── testB
├── ggg.png
├── hhh.jpg
└── ...
├── train.py # training code
├── utils.py
├── networks.py
└── name_results # results to be saved here
InputA - InputB - A2B - B2A (this repo) |
[1] Lin, Jianxin, et al. "Conditional image-to-image translation." The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(July 2018). 2018.
(Full paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_Conditional_Image-to-Image_Translation_CVPR_2018_paper.pdf)