CycleGAN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.

Table of contents

• CycleGAN-PyTorch
  • Overview
  • Table of contents
  • Download weights
  • Download datasets
  • Train CycleGAN-apple2orange
  • Resume train CycleGAN-apple2orange
  • Result
  • Contributing
  • Credit
    • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Download weights

• Google Driver
• Baidu Driver

Download datasets

• Official Driver
• Baidu Driver

Please refer to README.md in the data directory for the method of making a dataset.

Train CycleGAN-apple2orange

python3 train.py --config_path ./configs/CYCLEGAN.yaml

Resume train CycleGAN-apple2orange

Modify the ./configs/CYCLEGAN.yaml file.

• line 43: LOAD_RESUME change to True.
• line 44: RESUME_G_A_MODEL_WEIGHTS_PATH change to samples/CycleGAN-apple2orange/g_A_epoch_XXX.pth.tar.
• line 45: RESUME_G_B_MODEL_WEIGHTS_PATH change to samples/CycleGAN-apple2orange/g_B_epoch_XXX.pth.tar.
• line 46: RESUME_D_A_MODEL_WEIGHTS_PATH change to samples/CycleGAN-apple2orange/d_A_epoch_XXX.pth.tar.
• line 47: RESUME_D_B_MODEL_WEIGHTS_PATH change to samples/CycleGAN-apple2orange/d_B_epoch_XXX.pth.tar.

python3 train.py --config_path ./configs/CYCLEGAN.yaml

Result

InputA --> StyleB --> RecoveryA

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.

I look forward to seeing what the community does with these models!

Credit

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros

Abstract
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G:X→Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping F:Y→X and introduce a cycle consistency loss to push F(G(X))≈X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.

[Paper]) [Code(PyTorch)]

@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}
}