Merge pull request #5 from renan-siqueira/documentation/DocFiles

Documentation

Author: renan-siqueira

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 renan-siqueira
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1 +1,47 @@
-# Autoencoder Project
+# 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 an autoencoder on your dataset of images.
+- Visualize the reconstructions of the autoencoder.
+- Evaluate the model on a separate validation set.
+- Save and load model functionality.
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.x
+- PyTorch
+- torchvision
+- PIL
+- matplotlib
+
+### Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/renan-siqueira/autoencoder-project.git
+```
+2. Navigate to the project directory and install the required libraries:
+
+```bash
+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:
+
+```bash
+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.