Pavement Crack Image Data Generation

This project uses Deep Convolutional Generative Adversarial Networks (DCGAN), Wasserstein GAN (WGAN), WGAN with Gradient Penalty (WGAN_GP), and Wasserstein Conditional GAN (cGAN) with Gradient (C_WGAN_GP), Penalty to generate synthetic images of pavement cracks. These images can be used to augment existing datasets, improve the robustness of machine learning models, and facilitate research in pavement maintenance and repair.

Prerequisites

What things you need to install the software and how to install them:

pip install -r requirements.txt

Installing A step by step series of examples that tell you how to get a development environment running:

git clone https://github.com/a-shahrestani/Data-Generator.git
cd Data-Generator
pip install -r requirements.txt

Each of the models are located in the folder with their name in the src directory.