Skip to content

Latest commit

 

History

History
62 lines (49 loc) · 2.64 KB

README_CSCL.md

File metadata and controls

62 lines (49 loc) · 2.64 KB

Context-self contrastive pretraining for crop type semantic segmentation (IEEE Transactions on Geoscience and Remote Sensing)

Experiments

Initial steps

  • 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 providedided in "configs". The default values filled in these files correspond to parameters used in the experiments presented in the paper.

  • activate "deepsatmodels" python environment:

    conda activate deepsatmodels

Model training

Modify respective .yaml config files accordingly to define the save directory or loading a pre-trained model from pre-trained checkpoints.

Randomly initialized "UNet3D" model

python train_and_eval/segmentation_training.py --config_file configs/**/UNet3D.yaml --gpu_ids 0,1

Randomly initialized "UNet2D-CLSTM" model

python train_and_eval/segmentation_training.py --config_file configs/**/UNet2D_CLSTM.yaml --gpu_ids 0,1

CSCL-pretrained "UNet2D-CLSTM" model

  • model pre-training

     python train_and_eval/segmentation_cscl_training.py --config_file configs/**/UNet2D_CLSTM_CSCL.yaml --gpu_ids 0,1

  • copy the path to the pre-training save directory in CHECKPOINT.load_from_checkpoint. This will load the latest saved model. To load a specific checkpoint copy the path to the .pth file

     python train_and_eval/segmentation_training.py --config_file configs/**/UNet2D_CLSTM.yaml --gpu_ids 0,1

Randomly initialized "UNet3Df" model

python train_and_eval/segmentation_training.py --config_file configs/**/UNet3Df.yaml --gpu_ids 0,1

CSCL-pretrained "UNet3Df" model

  • model pre-training

     python train_and_eval/segmentation_cscl_training.py --config_file configs/**/UNet3Df_CSCL.yaml --gpu_ids 0,1

  • copy the path to the pre-training save directory in CHECKPOINT.load_from_checkpoint. This will load the latest saved model. To load a specific checkpoint copy the path to the .pth file

     python train_and_eval/segmentation_training.py --config_file configs/**/UNet3Df.yaml --gpu_ids 0,1

BibTex

If you incorporate any data or code from this repository into your project, please acknowledge the source by citing the following work:

@ARTICLE{9854891,
  author={Tarasiou, Michail and Güler, Riza Alp and Zafeiriou, Stefanos},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Context-Self Contrastive Pretraining for Crop Type Semantic Segmentation}, 
  year={2022},
  volume={60},
  number={},
  pages={1-17},
  doi={10.1109/TGRS.2022.3198187}}