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Generating Monet Paintings via CycleGAN

This repository contains code to train a CycleGAN and analyze the results.

Data

The dataset used was downloaded from Kaggle and contains two directories: Monet and Photo.
The Monet directory contains 300 Monet paintings sized 256x256 in JPEG format.
The Photo directory contains 7028 photos sized 256x256 in JPEG format.

The notebook "GenerateMonetFromExcistingModel.ipynb" uses google drive to upload the data. In order for that notebook to run smoothly, uploading a zip of the generate.zip folder to your google drive is necessary. Here is a link to the folder: https://drive.google.com/file/d/1NZMds1WLYqjvS8Qrhb3fIaOAhrWGK7XJ/view?usp=sharing
The runbook generates Monets from 4 different pictures using a pre-existing model (the best model) for two different epoch numbers (epoch 2 and epoch 100). These results are then combined into one .png image called "GeneratedMonets.png".

Running main.py will train a model
To tweak parameters you can select a subset of hyperparamters, by chaning the index in the training step.
To monitor performance you can add your own Weight and Biasis api-key.
6 types of images will be generated during training:
- Original photos
- Generated Monet
- Reconstructed
- Original Monet
- Generated photo
- Reconstructed Monet

Data for the best model can be downloaded: https://drive.google.com/file/d/115vhzf6M-uzOwk8RGOGQh5i3a4YOnCUZ/view?usp=share_link
- The data has already been pre-processed (so this step can be skipped in the analysis)
The analysis conists of:
- A RGB comparison of images from a previous training with 3 different metrics (MSE,PSNR,SSIM)
- A grey-scale comparison of images from a previous training
- A comparison of original photos and reconstructed photos

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