This repository contains an op-for-op PyTorch reimplementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
Please refer to README.md
in the data
directory for the method of making a dataset.
python3 train.py --config_path ./configs/CYCLEGAN.yaml
Modify the ./configs/CYCLEGAN.yaml
file.
- line 43:
LOAD_RESUME
change toTrue
. - line 44:
RESUME_G_A_MODEL_WEIGHTS_PATH
change tosamples/CycleGAN-apple2orange/g_A_epoch_XXX.pth.tar
. - line 45:
RESUME_G_B_MODEL_WEIGHTS_PATH
change tosamples/CycleGAN-apple2orange/g_B_epoch_XXX.pth.tar
. - line 46:
RESUME_D_A_MODEL_WEIGHTS_PATH
change tosamples/CycleGAN-apple2orange/d_A_epoch_XXX.pth.tar
. - line 47:
RESUME_D_B_MODEL_WEIGHTS_PATH
change tosamples/CycleGAN-apple2orange/d_B_epoch_XXX.pth.tar
.
python3 train.py --config_path ./configs/CYCLEGAN.yaml
InputA --> StyleB --> RecoveryA
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!
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.
@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}
}