https://github.com/matlab-deep-learning/image-domain-conversion-using-cyclegan

MATLAB example of deep learning for image domain conversion
https://github.com/matlab-deep-learning/image-domain-conversion-using-cyclegan

cyclegan deep-neural-networks deeplearning example image-conversion matlab matlab-deep-learning

Last synced: 3 months ago
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MATLAB example of deep learning for image domain conversion

• Host: GitHub
• URL: https://github.com/matlab-deep-learning/image-domain-conversion-using-cyclegan
• Owner: matlab-deep-learning
• License: other
• Created: 2020-06-11T09:00:45.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-06-16T06:24:01.000Z (over 5 years ago)
• Last Synced: 2025-05-07T19:47:39.913Z (6 months ago)
• Topics: cyclegan, deep-neural-networks, deeplearning, example, image-conversion, matlab, matlab-deep-learning
• Language: MATLAB
• Homepage: https://www.mathworks.com/products/deep-learning.html
• Size: 44.6 MB
• Stars: 17
• Watchers: 4
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: license.txt

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README

          
# Image domain conversion using CycleGAN

This example shows how to convert images from one domain into another using CycleGAN

CycleGAN is a GAN model that is generally used for the following purposes.

   -  Style transfer (images and paintings) 

   -  Season conversion 

   -  Day / night conversion 

   -  Object transformation 

The difference from Pix2Pix, which also perform image-image conversion, is that CycleGAN uses unsupervised learning, so there is no need for a paired image dataset.

In this example, even with unsupervised learning, you can see the model convert the images by understanding whether the fruit was a whole one or a cut one.

![result image](https://github.com/matlab-deep-learning/Image-domain-conversion-using-CycleGAN/raw/master/pics_for_doc/image_6.png)

![result image](https://github.com/matlab-deep-learning/Image-domain-conversion-using-CycleGAN/raw/master/pics_for_doc/image_7.png)

## **Requirements**

- [MATLAB](https://jp.mathworks.com/products/matlab.html)

- [Deep Learning Toolbox](https://jp.mathworks.com/products/deep-learning.html)

- [Image Processing Toolbox](https://jp.mathworks.com/products/image.html)

- [Parallel Computing Toolbox](https://jp.mathworks.com/products/parallel-computing.html)

MATLAB version should be R2019b and later 

## **Usage**

The repository provides the following files:

-	CycleGANExample.mlx — Example showing how to train the CycleGAN model

-	generator.m — Function to create a CycleGAN generator network

-	discriminator.m — Function to create a CycleGAN discriminator network

-	cycleGanImageDatastore.m — Datastore to prepare batches of images for training

-  cycleGAN_1000.mat -  Pretrained model that converts apples to oranges and vice-versa

To run, open CycleGANExample.mlx and run the script. You can train the model or use the pretrained model by setting the doTraining flag to false. 

# **Reference**

[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhu_Unpaired_Image-To-Image_Translation_ICCV_2017_paper.pdf)

 (Jun-Yan Zhu.etc, 2017)

Copyright 2019-2020 The MathWorks, Inc.

**[Download a free MATLAB trial for Deep Learning](https://www.mathworks.com/products/deep-learning.html)**

[![View Image domain conversion using CycleGAN on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://jp.mathworks.com/matlabcentral/fileexchange/76986-image-domain-conversion-using-cyclegan)