Skip to content

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

Notifications You must be signed in to change notification settings

xiaowei-hu/CycleGAN-tensorflow

Folders and files

NameName
Last commit message
Last commit date

Latest commit

bd19873 · Feb 24, 2018

History

50 Commits
Aug 19, 2017
May 24, 2017
Dec 21, 2017
Apr 17, 2017
Aug 19, 2017
Oct 18, 2017
Aug 19, 2017
May 18, 2017
Feb 24, 2018
Feb 24, 2018

Repository files navigation

CycleGAN

Tensorflow implementation for learning an image-to-image translation without input-output pairs. The method is proposed by Jun-Yan Zhu in Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkssee. For example in paper:

Update Results

The results of this implementation:

  • Horses -> Zebras

  • Zebras -> Horses

You can download the pretrained model from this url and extract the rar file to ./checkpoint/.

Prerequisites

  • tensorflow r1.1
  • numpy 1.11.0
  • scipy 0.17.0
  • pillow 3.3.0

Getting Started

Installation

git clone https://github.com/xhujoy/CycleGAN-tensorflow
cd CycleGAN-tensorflow

Train

  • Download a dataset (e.g. zebra and horse images from ImageNet):
bash ./download_dataset.sh horse2zebra
  • Train a model:
CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=horse2zebra
  • Use tensorboard to visualize the training details:
tensorboard --logdir=./logs

Test

  • Finally, test the model:
CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=horse2zebra --phase=test --which_direction=AtoB

Training and Test Details

To train a model,

CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=/path/to/data/ 

Models are saved to ./checkpoints/ (can be changed by passing --checkpoint_dir=your_dir).

To test the model,

CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=/path/to/data/ --phase=test --which_direction=AtoB/BtoA

Datasets

Download the datasets using the following script:

bash ./download_dataset.sh dataset_name
  • facades: 400 images from the CMP Facades dataset.
  • cityscapes: 2975 images from the Cityscapes training set.
  • maps: 1096 training images scraped from Google Maps.
  • horse2zebra: 939 horse images and 1177 zebra images downloaded from ImageNet using keywords wild horse and zebra.
  • apple2orange: 996 apple images and 1020 orange images downloaded from ImageNet using keywords apple and navel orange.
  • summer2winter_yosemite: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper.
  • monet2photo, vangogh2photo, ukiyoe2photo, cezanne2photo: The art images were downloaded from Wikiart. The real photos are downloaded from Flickr using combination of tags landscape and landscapephotography. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
  • iphone2dslr_flower: both classe of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.

Reference

About

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published