GAN_Clothing_mnist

This repository contains an implementation of a Generative Adversarial Network (GAN) trained on the Clothing MNIST dataset. The GAN is designed to generate new images of clothing items such as t-shirts.

Repository Files

• .gitattributes: Specifies Git attributes for the repository.
• .gitignore: Lists files and directories to be ignored by Git version control.
• README.md: This file, providing an overview of the repository.
• cgan_generator.h5: Pre-trained generator model for the GAN in HDF5 format.
• create_generator_model.py: Python script to create and train the generator model.
• generated_images.png: Sample of generated images from the GAN.
• generated_tshirts.png: Sample of generated t-shirt images.
• img_generator_ui.py: Python script for a user interface to generate images using the trained model.
• install_requirements.py: Python script to install the required dependencies.
• requirements.txt: Text file listing the required Python libraries and their versions.

Downloading the Repository

To download this repository, you have a few options:

Option 1: Clone the Repository

You can clone this repository using the following command:

git clone https://github.com/masanbasa3k/GAN_Clothing_mnist.git

Option 2: Download as ZIP

You can download the repository as a ZIP file by clicking on the green "Code" button at the top of the repository page and selecting "Download ZIP". After downloading, extract the ZIP file to your desired location.

Usage

To use this repository, follow these steps:

1. Install the required dependencies by running python install_requirements.py.
2. Execute the create_generator_model.py script to train the GAN model or use the pre-trained cgan_generator.h5 model.
3. Use the img_generator_ui.py script to interact with the trained model and generate new clothing images.
4. View the generated images in the generated_images.png and generated_tshirts.png files.

Contributing

Contributions to this repository are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.