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Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf
| Date | Stars |
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| 2026-07-24 | 713 |
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| 2026-08-08 | 712 |
| 2026-09-20 | 712 |
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<!-- <img src='imgs/horse2zebra.gif' align="right" width=384> <br><br><br> --> # CycleGAN Tensorflow implementation for learning an image-to-image translation **without** input-output pairs. The method is proposed by [Jun-Yan Zhu](https://people.eecs.berkeley.edu/~junyanz/) in [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkssee](https://arxiv.org/pdf/1703.10593.pdf). For example in paper: <img src="imgs/teaser.jpg" width="1000px"/> <!-- ## Applications ### Monet Paintings to Photos <img src="imgs/painting2photo.jpg" width="1000px"/> ### Collection Style Transfer <img src="imgs/photo2painting.jpg" width="1000px"/> ### Object Transfiguration <img src="imgs/objects.jpg" width="1000px"/> ### Season Transfer <img src="imgs/season.jpg" width="1000px"/> ### Photo Enhancement: iPhone photo to DSLR photo <img src="imgs/photo_enhancement.jpg" width="1000px"/> --> ## Update Results The results of this implementation: - Horses -> Zebras <br> <img src="imgs/n02381460_510.jpg" width="200px"/> <img src="imgs/AtoB_n02381460_510.jpg" width="200px"/> <img src="imgs/n02381460_4530.jpg" width="200px"/> <img src="imgs/AtoB_n02381460_4530.jpg" width="200px"/> <img src="imgs/n02381460_4660.jpg" width="200px"/> <img src="imgs/AtoB_n02381460_4660.jpg" width="200px"/> <img src="imgs/n02381460_8980.jpg" width="200px"/> <img src="imgs/AtoB_n02381460_8980.jpg" width="200px"/> - Zebras -> Horses <br> <img src="imgs/n02391049_1760.jpg" width="200px"/> <img src="imgs/BtoA_n02391049_1760.jpg" width="200px"/> <img src="imgs/n02391049_3070.jpg" width="200px"/> <img src="imgs/BtoA_n02391049_3070.jpg" width="200px"/> <img src="imgs/n02391049_5100.jpg" width="200px"/> <img src="imgs/BtoA_n02391049_5100.jpg" width="200px"/> <img src="imgs/n02391049_7150.jpg" width="200px"/> <img src="imgs/BtoA_n02391049_7150.jpg" width="200px"/> You can download the pretrained model from [this url](https://1drv.ms/u/s!AroAdu0uts_gj5tA93GnwyfRpvBIDA) 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 - Install tensorflow from https://github.com/tensorflow/tensorflow - Clone this repo: ```bash git clone https://github.com/xhujoy/CycleGAN-tensorflow cd CycleGAN-tensorflow ``` ### Train - Download a dataset (e.g. zebra and horse images from ImageNet): ```bash bash ./download_dataset.sh horse2zebra ``` - Train a model: ```bash CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=horse2zebra ``` - Use tensorboard to visualize the training details: ```bash tensorboard --logdir=./logs ``` ### Test - Finally, test the model: ```bash CUDA_VISIBLE_DEVICES=0 python main.py --dataset_dir=horse2zebra --phase=test --which_direction=AtoB ``` ## Training and Test Details To train a model, ```bash 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, ```bash 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 bash ./download_dataset.sh dataset_name ``` - `facades`: 400 images from the [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/). - `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/). - `maps`: 1096 training images scraped from Google Maps. - `horse2zebra`: 939 horse images and 1177 zebra images downloaded from [ImageNet](http://www.image-net.org/) using keywords `wild horse` and `zebra`. - `apple2orange`: 996 apple images and 1020 orange images downloaded from [ImageNet](http://www.image-net.org/) 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`, `vangogh
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matched fp:50cb5e70498fb97d, topic:tensorflow