cellcanvas / mock-annotation-evaluation / 0.0.3

Model Evaluation on Copick Data

Evaluates segmentation models from the mock-annotation solution on Copick data, generating metrics like IoU and F1, and saves the predicted segmentation into the Copick project.
Tags
imagingcryoetPythonnapari
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_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)
--run_name
Name of the run to process. (default value: PARAMETER_VALUE)
--model_dir
Directory of trained model files. (default value: PARAMETER_VALUE)
--output_dir
Directory to save evaluation results. (default value: PARAMETER_VALUE)
--output_user_id
User ID for the output predicted segmentation. (default value: PARAMETER_VALUE)
--output_label_name
Name of the output predicted segmentation. (default value: PARAMETER_VALUE)

Usage instructions

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