About
Solution Catalog
Documentation
Tutorial
cellcanvas album catalog
sharing cellcanvas tools
kephale
/
train-unet-copick
/ 0.0.3
Train 3D UNet for Segmentation with Copick Dataset
Train a 3D UNet network using the Copick dataset for segmentation.
Tags
imaging
cryoet
Python
napari
Citation
Morphospaces team.
https://github.com/morphometrics/morphospaces
Solution written by
Kyle Harrington
Zhuowen Zhao
License of solution
MIT
Source Code
View on GitHub
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 UNet 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.