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README.md

Crop Classification

Task-Informed Meta-Learning applied to crop classification, using the cropharvest dataset.

This repository mirrors the structure of the cropharvest benchmarks - specifically, we train a meta-model in the TIML Learner, and use the state dictionary saved by the learner in a finetuning loop.

Getting started

Anaconda running python 3.6 is used as the package manager. To get set up with an environment, install Anaconda from the link above, and (from this directory) run

conda env create -f environment.yml

This will create an environment named timl-classification with all the necessary packages to run the code. To activate this environment, run

conda activate timl-classification

The main script to train the models is then deep_learning.py, with the model configurations controlled by the config

The trained TIML model is available on Zenodo.