CycleGAN

This repository contains a re-implementation of the CycleGAN architecture found in [1].

Experiment tracking was done using Weights and Biases (wandb), selected runs are made public here.

Generating a Van Gogh painting from a photograph and converting it back to a photograph.

Installation and Running

Download the zip file containing preconfigured directories and datasets from here.
Extract the zip file (and all other zip files within) and place contents inside the root directory.
Set up a virtual environment and install the dependencies using the requirements.txt file by running
```
pip install -r requirements.txt
```
Amend the configurations dictionary as required in the file train.py.
Train a model by choosing a relevant GPU device with the following command
```
CUDA_VISIBLE_DEVICES=[GPU device] python src/train.py
```
To run without a GPU use,
```
python src/train.py
```
Run evaluations by adjusting the configurations dictionary in evaluate.py according to the required settings and by running,
```
python src/evaluate.py
```
To compute metrics specify the desired dataset in metrics.py file and then run,
```
python src/metrics.py
```
Currently metrics can only be computed on a CUDA enabled GPU.

Pre-trained models can be downloaded from here.