cellcanvas / mock-annotation-torch / 0.0.3

Mock Annotation and PyTorch Training on Copick Data

A solution that creates mock annotations based on multilabel segmentation, trains a PyTorch segmentation model in steps, and generates predictions.
Tags
imagingcryoetPythonnaparicellcanvas
Solution written by
Kyle Harrington
License of solution
MIT
Source Code

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)

Usage instructions

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