Fashion Texture Generation using GANs

Table of Contents

1. Project Structure
2. Requirements
3. Dataset
4. Model Architecture
5. Training Process
6. Results
7. Usage
8. Acknowledgements

Project Structure

• GenAI_project.ipynb - Main code for training the GAN.
• generated_images/ - Folder where generated sample images from each epoch will be saved.
• final_generated_images.png - Sample of generated images after the last training epoch.
• README.md - Project documentation.

Requirements

Dataset

• Resolution: 28x28 pixels, scaled to range [-1, 1].
• Classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot.

Model Architecture

1. Generator:
   • Takes a random vector (latent vector z) of size 100 as input.
   • Passes through fully connected layers with ReLU activations to produce a 28x28x3 image.
   • Uses Tanh activation for the output, resulting in values between [-1, 1].
2. Discriminator:
   • Takes an image (real or generated) as input.
   • Uses fully connected layers with LeakyReLU activations to classify images as real or fake.
   • Final output is a probability from a Sigmoid activation.

Training Process

1. Loading Data:
   • The dataset is loaded, converted to tensor, and normalized to [-1, 1].
   • DataLoader is set up for batching.
2. Training Loop:
   • Discriminator is trained first to distinguish real images from generated ones, computing a loss for real images and for generated (fake) images.
   • Generator is then trained to improve the realism of generated images, using feedback from the discriminator.
   • Both networks are optimized with Adam optimizers.
3. Logging and Saving:
   • Losses for both generator and discriminator are tracked.
   • Sample images are saved after every 5 epochs.

Results

1. Load the Fashion-MNIST dataset.
2. Train the GAN model.
3. Save generated samples in generated_images/ after every 5 epochs.
4. Save a final set of generated images as final_generated_images.png.

Acknowledgements

• Hugging Face for providing easy access to datasets and models.
• PyTorch and the deep learning community for extensive GAN tutorials.