https://github.com/shure-dev/ai-2022-genart

AI-2022-genart
https://github.com/shure-dev/ai-2022-genart

Last synced: about 2 months ago
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AI-2022-genart

• Host: GitHub
• URL: https://github.com/shure-dev/ai-2022-genart
• Owner: shure-dev
• License: other
• Created: 2022-10-31T14:44:02.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2022-11-08T13:03:30.000Z (about 2 years ago)
• Last Synced: 2023-11-05T09:21:33.532Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 21.7 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# GAN for generating art

> Nov.2022 Artificial Intelligence, Home assignment

![Test Image 1](results/results/real.png)

![Test Image 2](results/results/2monet.png)

![Test Image 3](results/results/3ukiyoe.png)

![Test Image 4](results/results/4ceznne.png)

![Test Image 5](results/results/5vangogh.png)

## Members

- Max Grönlund

- Joona Hytönen 

- Ville Juuti

- Juho Kemppainen

- Yusuke Mikami

- Simo Turunen

## How to run

1. Go file "AI-2022-genart/Joensuu-cycleGAN.ipynb"

2. Run notebook "AI-2022-genart/Joensuu-cycleGAN.ipynb"

## Abstract

Aim of this group project was to implement General adversarial networks (GAN) model architecture 

in our work where we suppose to transform pictures of our city (Joensuu) taken by us into art style like. 

GAN concept based on combination of two separated neural network. We used technique named CycleGAN image-to-image translation. 

This technique provides several different ways to manipulate images. We used the one which 

translates real photographs into specific art style. 

We used Deepnote for collaborator purposes. We pulled original CycleGAN code 

from Github repository to our Deepnote notebook, then for image transformation 

we first implemented pre-trained art-style models like Monet, Ukiyoe, Cezanne and Vangogh and 

then trained models with training data proposed by CycleGAN developing team. Image manipulation of 

real photographs was succeeded. The number of real images is 14. Collection of real Images and results

can be found in the 6. Experiments and results section.