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README.md

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# Autoencoder Project
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A simple implementation of an autoencoder using PyTorch.
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A simple implementation of an autoencoder using PyTorch.
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This project aims to provide a basic framework for understanding, training and evaluating autoencoders on any image size.
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This project aims to provide a basic framework for understanding, training, and evaluating autoencoders on any image size.
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## Features
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- Checkpointing: Ability to save and resume training from checkpoints.
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- Save and load trained model weights.
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- Utilize custom datasets by simply pointing to your directory.
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- Test all available autoencoder architectures with a single command.
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## Getting Started
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## Usage
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1. Modify the `settings/settings.py` file to point to your training and validation dataset.
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2. Modify the `json/params.json` file to reflext your training preferences.
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3. To train the autoencoder, simply run:
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2. Modify the `json/params.json` file to reflect your training preferences.
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### Training
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To train the autoencoder as per your configurations, simply run:
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```bash
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python run.py
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```
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### Testing
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To run a test routine for all the available autoencoder architectures, use:
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```bash
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python run.py --test
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```
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This will train each autoencoder for a few epochs and provide an overview of their performances.

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