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/
mock-annotation
/ 0.0.12
Mock Annotation and XGBoost Training on Copick Data
A solution that creates mock annotations based on multilabel segmentation, trains XGBoost models in steps, and generates predictions.
Tags
imaging
cryoet
Python
napari
cellcanvas
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)
--voxel_spacing
Voxel spacing used to scale pick locations. (default value: PARAMETER_VALUE)
--tomo_type
Tomogram type to use for each tomogram. (default value: PARAMETER_VALUE)
--embedding_name
Name of the embedding features to use. (default value: PARAMETER_VALUE)
--input_session_id
Session ID for the input segmentation. (default value: PARAMETER_VALUE)
--input_user_id
User ID for the input segmentation. (default value: PARAMETER_VALUE)
--input_label_name
Name of the input label segmentation. (default value: PARAMETER_VALUE)
--num_annotation_steps
Number of annotation steps to perform. (default value: PARAMETER_VALUE)
--run_name
Name of the run to process. (default value: PARAMETER_VALUE)
--output_dir
Directory to save trained models. (default value: PARAMETER_VALUE)
--random_seed
Random seed for reproducibility. (default value: 17171)
--device
Device to use for XGBoost training (cpu or cuda). (default value: cpu)
Usage instructions
Please follow
this link
for details on how to install and run this solution.