https://github.com/blade6570/learningimage-to-imagetranslationusingpairedandunpairedtrainingsamples

Learning image-to-image translation using paired and unpaired training samples
https://github.com/blade6570/learningimage-to-imagetranslationusingpairedandunpairedtrainingsamples

cityscapes cyclegan gans image-generation image-translation mapillary-vistas-dataset paired-data pix2pix semi-supervised-gan

Last synced: 6 months ago
JSON representation

Learning image-to-image translation using paired and unpaired training samples

• Host: GitHub
• URL: https://github.com/blade6570/learningimage-to-imagetranslationusingpairedandunpairedtrainingsamples
• Owner: Blade6570
• License: other
• Created: 2018-05-11T11:31:47.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2021-05-25T08:51:06.000Z (over 4 years ago)
• Last Synced: 2025-05-12T23:44:41.086Z (6 months ago)
• Topics: cityscapes, cyclegan, gans, image-generation, image-translation, mapillary-vistas-dataset, paired-data, pix2pix, semi-supervised-gan
• Language: Python
• Homepage: https://tutvision.github.io/Learning-image-to-image-translation-using-paired-and-unpaired-training-samples/
• Size: 1.18 MB
• Stars: 20
• Watchers: 2
• Forks: 3
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
## Learning image-to-image translation using paired and unpaired trainingsamples ##

[**Project**](https://tutvision.github.io/Learning-image-to-image-translation-using-paired-and-unpaired-training-samples/) | [**Arxiv**](https://arxiv.org/pdf/1805.03189.pdf) | [**ACCV-2018**](http://accv2018.net/)

***

![O](https://github.com/Blade6570/Learningimage-to-imagetranslationusingpairedandunpairedtrainingsamples/blob/master/teaser.png?raw=true "Comparision with other methods")

This is the part of implementation for the  "Learning image-to-image translation using paired and unpaired training samples" (https://arxiv.org/pdf/1805.03189.pdf). **_This paper is accepted in ACCV 2018_**. 

 **Prerequisites**

 1. Python 3.5.4

 2. Pytorch 0.3.1

 3. Visdom and dominate

 

 **Training**

 1. Downlaod cityscapes datasets as in pix2pix and cyclegan as sugggested in [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) 

 2. Create a folder name *datasets* with the subfolder structures as given in this repo.

 3. Keep the paired data in *train*-subfolder and unpaired data in *trainA* and *trainB* subfolders.

 4. Then run: *python train.py --dataroot ./datasets --model cycle_gan --dataset_mode unaligned --which_model_netG resnet_9blocks --which_direction AtoB --super_epoch 50 --super_epoch_start 0 --super_mode aligned --super_start 1 --name mygan_70 --no_dropout*

 

**Testing**

 1. Downlaod cityscapes test data as in cyclegan as sugggested in [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) 

 2. Keep the test data in *testA* and *testB* subfolders within *datasets* folder.

 3. Then run: *python test.py --dataroot ./datasets --model cycle_gan --dataset_mode unaligned --which_model_netG resnet_9blocks --which_direction AtoB --name mygan_70 --how_many 100*

 

 **Training Tips:**

 1. With less paired data, increase the --super_epoch value for better results. 

 2. With No paired data, set --super_start 0. 

 3. For no unpaired data, set --super_epoch and --niter to same value. We have not included the VGG loss in the training script (Commented part). We will update this soon. *For any help, please contact us at:  soumya.tripathy@tuni.fi*

 

 **If you are using this implementation for your research work then please cite us as:** 

```

#Citation 

@article{tripathy+kannala+rahtu,

  title={Learning image-to-image translation using paired and unpaired training samples},

  author={Tripathy, Soumya and Kannala, Juho and Rahtu, Esa},

  journal={arXiv preprint arXiv:1805.03189},

  year={2018}

}

```

```

Related Work

1. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio "Generative Adversarial Networks", in NIPS 2014. 

2. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. "Image-to-Image Translation with Conditional Adversarial Networks", in CVPR 2017.

3. J. Y. Zhu, T. Park, P. Isola, and A. A. Efros. "Unpaired image-to-image translation using cycle-consistent adversarial networks",

```

**NOTE:** Code borrows heavily from [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)