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train-model
/ 0.0.3
Train Random Forest on Copick Painted Segmentation Data
A solution that trains a Random Forest model using Copick painted segmentation data and exports the trained model.
Tags
imaging
cryoet
Python
napari
Solution written by
Kyle Harrington
License of solution
MIT
Source Code
View on GitHub
Arguments
--copick_config_path
Path to the Copick configuration JSON file. (default value: PARAMETER_VALUE)
--painting_segmentation_name
Name for the painting segmentation. (default value: PARAMETER_VALUE)
--session_id
Session ID for the segmentation. (default value: PARAMETER_VALUE)
--user_id
User ID for segmentation creation. (default value: PARAMETER_VALUE)
--voxel_spacing
Voxel spacing used to scale pick locations. (default value: PARAMETER_VALUE)
--n_estimators
Number of trees in the Random Forest. (default value: PARAMETER_VALUE)
--model_output_path
Path for the output joblib file containing the trained Random Forest model (default value: PARAMETER_VALUE)
Usage instructions
Please follow
this link
for details on how to install and run this solution.