CycleGAN-pytorch

the pytorch version of cyclegan

Requirments

• CUDA 8.0+
• pytorch 0.3.1
• torchvision

Datasets

• Download a cycleGAN dataset (e.g.maps):

bash ./datasets/download_cyclegan_dataset.sh maps

Train a model:

python cycleGAN.py --data_root 'your data directory'   

Result examples

epoch-199

From top to bottom： A-->fake_B-->recon_A + ident_B; B-->fake_A-->recon_B + ident_A

Reference

[1]Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

@inproceedings{CycleGAN2017,
  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
  year={2017}
}   

Personal-Blog

teeyohuang