Initial Release

Autoencoder Project - Release 1.0.0

Release Date: October 13, 2023.

Highlights:

• Introduction of an end-to-end autoencoder training pipeline for 64x64 images.
• Efficient data handling using PyTorch's DataLoader for streamlined batching and preprocessing.
• Visual reconstruction comparison, showcasing original, encoded, and decoded images side-by-side.

Features:

• End-to-end Training: Seamlessly load data, train an autoencoder model, evaluate its performance, and visualize its reconstructions with a simple command.
• Modular Structure: Organized structure with separate modules for model definitions, data loading, and training utilities, making the project expandable and maintainable.
• Visualization Capabilities: After training, the model's capability to encode and decode is demonstrated with a visual comparison of original and reconstructed images.
• Model Saving & Loading: Easily save trained model weights to a file and reload them for later use, avoiding the need to retrain frequently.

Usage:

1. Clone the repository and navigate to the project directory.
2. Install the necessary dependencies using pip install -r requirements.txt.
3. Adjust data paths and settings in settings/settings.py based on your dataset.
4. Run the main script with python run.py.
5. Post-training, visualize the reconstructed results displayed, showcasing original, encoded, and decoded images.
6. Trained models are saved automatically to a predefined path for future usage.