Autoencoder Project

A simple implementation of an autoencoder using PyTorch.

This project aims to provide a foundational structure to understand, train, and evaluate autoencoders on 64x64 images.

Features

• Train various autoencoder architectures: standard, convolutional, variational, and convolutional variational autoencoder on your dataset of images.
• Visualize the reconstructions of the autoencoder.
• Evaluate the model on a separate validation set.
• Checkpointing: Ability to save and resume training from checkpoints.
• Save and load trained model weights.
• Utilize custom datasets by simply pointing to your directory.

Getting Started

Prerequisites

• Python 3.x
• PyTorch
• torchvision
• PIL
• matplotlib

Installation

1. Clone the repository:

git clone https://github.com/renan-siqueira/autoencoder-project.git

2. Navigate to the project directory and install the required libraries:

cd autoencoder-project
pip install -r requirements.txt

Usage

1. Modify settings/settings.py to point to your training and validation dataset.
2. To train the autoencoder, simply run:

python run.py

By default, this will train a new model. If you wish to use a pre-trained model, modify the main method in run.py.