mjahmadee/vae

Variational Autoencoder

Variational Autoencoders for Image Classification 🤖👚

This repository contains implementations of Variational Autoencoders (VAE) and their application in image classification tasks, primarily focusing on the Fashion MNIST dataset.

Features 🌟

• Implements Variational Autoencoders (VAE) for generating and reconstructing images.
• Utilizes TensorFlow and Keras for building and training models.
• Supports dimensionality reduction for improving image classification using K-Nearest Neighbors (KNN).
• Includes detailed performance evaluation with confusion matrices and classification reports.
• Provides visualizations of training losses, latent spaces, and generated images.

Setup and Installation 🛠️

1. Clone the repository.
2. Install the necessary dependencies using pip install -r requirements.txt.
3. Ensure TensorFlow with GPU support is installed if GPU processing is desired.

Datasets 📁

The primary dataset used is Fashion MNIST, which includes 60,000 training images and 10,000 testing images of 10 fashion categories.

Training the Model 🚀

• Execute the VAE training script to learn latent representations of images.
• The model automatically performs image reconstruction and generation.

Image Classification 🧪

• Use the encoded representations from VAE as features for training a KNN classifier.
• Evaluate the classifier's performance using the test dataset and calculate various metrics like accuracy, precision, recall, and F1-score.

Results and Evaluation 📊

• Check the output directory for training logs, model checkpoints, and generated images.
• Review the classification reports and confusion matrices to understand model performance.

Contributing 🤝

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

License 📜

This project is available under the MIT License. See the LICENSE file for more details.

Acknowledgements 🙌

• TensorFlow and Keras documentation for providing extensive guides and API documentation.
• Fashion MNIST dataset creators for providing a benchmark dataset for image classification tasks.

For more details, please visit the GitHub repository.