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deepsatmodels's Introduction

Repository for training land cover recognition models for satellite imagery

Setting up a python environment

  • Follow the instruction in https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html for downloading and installing Miniconda

  • Open a terminal in the code directory

  • Create an environment using the .yml file:

    conda env create -f deepsatmodels_env.yml

  • Activate the environment:

    source activate deepsatmodels

  • Install required version of torch:

    conda install pytorch torchvision torchaudio cudatoolkit=10.1 -c pytorch-nightly

Initial steps for setting up experiments

  • Add the base directory and paths to train and evaluation path files in "data/datasets.yaml".
  • For each experiment we use a separate ".yaml" configuration file. Examples files are provided in "configs". The default values filled in these files correspond to parameters used in the experiments presented in respective studies.
  • Modify .yaml config files accordingly to train with your own data.

deepsatmodels's People

Contributors

michaeltrs avatar

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