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README.md

AutoEncoders Tutorial

This repository contains code for building a convolutional autoencoder to compress the FossilNET dataset. This can generally be useful for storing and transporting the images, or for speeding up the processing of the images.

LOGO

Created by
Sara Althubaiti1 and Sakhaa Alsaedi.1
King Abdullah University of Science and Technology (KAUST)1

Dataset 🐚

We will be using the FossilNET Image dataset that had been collected and curated by Matt Hall and consists of 3000 colour images at 224x224 resolution, split over 10 classes:

  • Ammonites
  • Bivalues
  • Corals
  • Dinosaurs
  • Echinoderms
  • Fishes
  • Forams
  • Gastropods
  • Plants
  • Trilobites

Getting the data

Utilize the dataloader_fossilnet.py script in the Linear_AutoEncoders.ipynb and Convolutional_AutoEncoders.ipynb notebooks.

Dependencies

Google Colab provides all the necessary dependencies for running the code in this repository. You do not need to install any additional packages.

Scripts

The repository includes the following files:

  • 📙Linear_AutoEncoders.ipynb: The code for implementing simple/linear autoencoder.
  • 📙Convolutional_AutoEncoders.ipynb: The code for implementing convolutional autoencoder.
  • 📄dataloader_fossilnet.py: The code for loading the FossilNET dataset. This file provides a function that can be used to load the dataset into Linear_AutoEncoders.ipynb and Convolutional_AutoEncoders.ipynb notebooks.