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Variational Autoencoders for Image Classification 🤖👚

Python TensorFlow Machine Learning

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.