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Curiosity Mastcam Novelty Detection

This repository contains the code for the novelty detection project using the Curiosity Mastcam images.

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Requirements

  • Python 3.11+

Install the required packages by running:

pip install -r requirements_linux_cpu.txt

or

pip install -r requirements_linux_gpu.txt

Usage

Train

Example usage (run from the root directory):

python3 main.py --lr 1e-6 --model VAE --epochs 1 --device cuda

Arguments:

  • --model - model to use (one of GAN, VAE, FLOW)
  • --epochs - number of epochs to train
  • --lr - learning rate
  • --device - device to use (eg.cpu or cuda)

Test

Example usage (run from the root directory):

python3 ./main_test.py --model VAE --batch 1 --device cpu --load ./vae/model/vae_model-250-epoch.pth --save results/vae

Arguments:

  • --model - model to use (one of GAN, VAE, FLOW)
  • --batch - batch size
  • --device - device to use (eg.cpu or cuda)
  • --load - path to the model to load
  • --save - path to save the results

Dataset

Multispectral images of Mars taken by the Curiosity rover. The dataset is divided into four parts: train_typical, validation_typical, test_typical, and test_novel.

Source:

To download the dataset run:

cd dataset
curl -O https://zenodo.org/records/3732485/files/test_novel.zip
curl -O https://zenodo.org/records/3732485/files/test_typical.zip
curl -O https://zenodo.org/records/3732485/files/train_typical.zip
curl -O https://zenodo.org/records/3732485/files/validation_typical.zip
unzip test_novel.zip
unzip test_typical.zip
unzip train_typical.zip
unzip validation_typical.zip