This repository contains the GAN (Generative Adversarial Network) programs that we implemented for the TCIRRP dataset as part of mini project in university.
Note
The base paper of our implementation can be found at https://ieeexplore.ieee.org/document/9717263.
Tip
Our modified dataset for the paper's implementation can be found at https://www.kaggle.com/datasets/kbdharun/tcirrp-dataset.
In our implementation of the base paper, we have implemented the dataset on three conventional GAN models and one novel GAN model using Keras. They are namely:
- CycleGAN (Unpaired images are passed as input for the model.) (Code)
- Pix2Pix (Paired images are passed as input for the model.) (Code)
- Res-Pix2Pix (Paired images are passed as input for the model.) (Code)
- TCR-GAN (Paired images are passed as input for the model.) (Novel GAN model, a modification of Res-Pix2Pix) (Code)
Caution
While, there is a pre-built image with the project's dependencies installed. It is suggested to use a Conda environment instead for the dependencies as the image is unreliable for Notebook runs.
Python and packages in the requirements.txt file installed.
Note
You can install all the required packages using the command pip install -r requirements.txt.
If you are using conda to manage your environments, you can create a new environment for this repository with the command conda create -n mini and activate it with the command conda activate mini.
Tip
For faster environment solving in Conda, I would suggesting using the libmamba solver. You can set it as the default solver using the command conda config --set solver libmamba.
Then, you can install all the required packages using the command conda install --file requirements.txt.