numbat

A transformer based approach to learning in the context of offworld terrain images. The approach leverages self-distillation with no supervision; it learns largely from unsupervised images.

usage

Replicate the environment then inspect main.py to see how the high level loading/training/testing procedures in the respective 'routines' files are used.

environment

This repository has been tested with CUDA 11.1 and the library versions listed in env.yml. Recreate the environment using conda with the following command:

conda env create --name numbat --file=env.yml

The env.yml file was written using the following command:

conda env export | grep -v "^prefix: " > env.yml

data

The datasets used here are publically available offworld terrain image datasets.

mars32k

The mars32k dataset is an unlabelled set of terrain images available here https://dominikschmidt.xyz/mars32k/. It contains images taken with Curiosity's Mastcam camera.

It can be downloaded and unzipped as required using the following commands:

cd data
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1yeLkE1p5oeCqa5pA7tc0tI4eoyvjZc5X' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1yeLkE1p5oeCqa5pA7tc0tI4eoyvjZc5X" -O mars32k.zip && rm -rf /tmp/cookies.txt
unzip mars32k.zip

msl

The msl dataset is a labelled classification dataset available here https://zenodo.org/record/1049137#.YKLKXahKiPq. It contains train, test, and validation sets according to their Martian day of acquisition.

HiRISE

The Mars Orbital Image HiRISE dataset is a labelled classification dataset available here https://zenodo.org/record/4002935#.YKwVMahKiPo.

improvements

• Multi-GPU parallelisation: https://pytorch.org/tutorials/beginner/dist_overview.html.