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copick inference easymode

easymode

Segment tomograms using easymode pretrained models.

Plugin command — copick-easymode

This command is provided by the copick-easymode plugin, not copick core. Install it to make this command available:

pip install git+https://github.com/copick/copick-easymode.git
pip install --no-deps git+https://github.com/mgflast/easymode.git

See the plugin system guide for details.

Usage

copick inference easymode [OPTIONS]

Description

Segment copick tomograms using easymode pretrained models.

This command runs inference using easymode's pretrained segmentation models on tomograms stored in a copick project and saves the results as copick segmentations. Each requested feature is written back as its own single-label segmentation at the input tomogram's voxel size; with --add-objects (on by default) each feature is also registered as a pickable object in the config.

Available models include: ribosome, membrane, microtubule, actin, cytoplasm, mitochondrion, nucleus, nuclear_envelope, npc, and more.

URI Format

Tomograms:     type@voxel_spacing
Segmentations: name:user_id/session_id@voxel_spacing

Options

Option Type Default Description
-c, --config path — Path to the configuration file.
--model, -m text required Comma-separated list of models/features to run (e.g., 'ribosome,membrane').
--tomogram, -t text required Tomogram URI in format 'type@voxel_size' (e.g., 'wbp@10.0').
--run, -r text "" Run name or comma-separated list of runs. Empty = all runs.
--threshold float 0.5 Probability threshold for binarizing segmentation (0.0-1.0).
--batch-size integer 1 Batch size for inference.
--tta integer 4 Test-time augmentation level (1-16). Higher = better but slower.
--gpus text — Comma-separated GPU IDs (e.g., '0,1'). Default: all available.
--session-id text 1 Session ID for the annotation.
--user-id text copick User ID for the annotation.
--overwrite / --no-overwrite boolean flag False Overwrite existing segmentations.
--add-objects / --no-add-objects boolean flag True Add object definitions to config if missing.
--debug / --no-debug boolean flag False Enable debug logging.

Examples

# Segment ribosomes in all runs
copick inference easymode -c config.json -m ribosome -t wbp@10.0

# Segment multiple features
copick inference easymode -c config.json -m ribosome,membrane -t wbp@10.0

# Segment specific runs with GPU selection
copick inference easymode -c config.json -m membrane -t wbp@10.0 --run run001,run002 --gpus 0,1

# High quality inference with TTA
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --tta 16 --batch-size 2

# Skip adding object definitions to config
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --no-add-objects

See also

Notes

A CUDA GPU is strongly recommended; the weights download automatically on first use.

Acknowledgements

This command uses pretrained models from easymode by Mart G.F. Last. Docs: https://mgflast.github.io/easymode Repo: https://github.com/mgflast/easymode If you use these models in your research, please cite the easymode authors.

Tutorial

See Segmenting cellular features with easymode for an end-to-end walkthrough — creating a project from a CZ cryoET Data Portal dataset and running copick inference easymode to segment membranes, ribosomes and more.

Please cite easymode

copick inference easymode only wraps the tool — the pretrained models and method are the work of the easymode authors. If easymode contributes to your research, please cite the original work in addition to copick:

So-Last, M. G. F., Burt, A., Hale, T., & Allegretti, M. (2026). Easymode: general pretrained networks for cellular cryo-ET enable flexible approaches to subtomogram averaging. bioRxiv.