kephale / train-resunet-copick / 0.0.6

Train 3D ResUNet for Segmentation with Copick Dataset

Train a 3D ResUNet network using the Copick dataset for segmentation.
Tags
imagingcryoetPythonnapari
Citation
Solution written by
Kyle Harrington
Zhuowen Zhao
License of solution
MIT
Source Code

Arguments

--copick_config_path
Path to the Copick configuration file (default value: PARAMETER_VALUE)
--train_run_names
Names of the runs in the Copick project for training (default value: PARAMETER_VALUE)
--val_run_names
Names of the runs in the Copick project for validation (default value: PARAMETER_VALUE)
--tomo_type
Tomogram type in the Copick project (default value: PARAMETER_VALUE)
--user_id
User ID for the Copick project (default value: PARAMETER_VALUE)
--session_id
Session ID for the Copick project (default value: PARAMETER_VALUE)
--segmentation_type
Segmentation type in the Copick project (default value: PARAMETER_VALUE)
--voxel_spacing
Voxel spacing for the Copick project (default value: PARAMETER_VALUE)
--lr
Learning rate for the ResUNet training (default value: 0.0001)
--logdir
Output directory name in the current working directory. Default is checkpoints (default value: checkpoints)

Usage instructions

Please follow this link for details on how to install and run this solution.