FashionMNIST DCGAN

Project Overview

This project implements a Deep Convolutional Generative Adversarial Network (DCGAN) to generate realistic images of clothing items using the FashionMNIST dataset.

Folder Structure

• FashionMNIST_DCGAN.ipynb: Contains the complete code and implementation for the DCGAN model, including data preprocessing, model architecture, training loop, and result visualization.
• Images/: Directory to store sample generated images during training.

Setup Instructions

Prerequisites

Ensure you have Python 3 installed. The project also requires several Python packages which can be installed using the following command:

pip install -r requirements.txt

Running the Notebook

Clone the Repository:

git clone https://github.com/yourusername/FashionMNIST_DCGAN.git
cd FashionMNIST_DCGAN

Launch Jupyter Notebook:

jupyter notebook FashionMNIST_DCGAN.ipynb

Results and Observations

Discriminator and Generator Losses:

Discriminator losses gradually increase as it becomes harder to distinguish between real and fake images.

Generator losses decrease as it becomes better at generating realistic images.

Image Quality Progression:

Epoch 10: Blurry and lacking detail.

Epoch 30: Improved clarity and texture.

Epoch 50: Significant enhancements with detailed and realistic images.

Sample Generated Images

Conclusion

The project successfully demonstrates the ability of a DCGAN to generate realistic images of clothing items from the FashionMNIST dataset. Further training and model refinement can yield even better results.