Easymode pretrained segmentation integration for copick CLI.
Preprint | easymode docs | easymode repo
This plugin provides CLI commands to run easymode pretrained segmentation models on tomograms stored in copick projects. For more information about easymode, visit the documentation. If you use this plugin, please cite the easymode preprint (see Citation).
easymode 1.0.0 is not published to PyPI (only older 0.0.x releases are), so it is installed
from GitHub. Its packaging metadata also (incorrectly) pins numpy<2 / tensorflow<2.12, which
conflicts with copick's numpy>=2 — even though easymode runs fine on numpy>=2. To avoid that
conflict, install copick-easymode and its dependencies first (this brings in copick and
numpy>=2), then install easymode from GitHub with --no-deps so its bad pins are ignored:
git clone https://github.com/copick/copick-easymode.git
cd copick-easymode
# 1. Install copick-easymode + dependencies (copick, numpy>=2, tensorflow>=2.16, easymode's runtime deps)
pip install -e .
# 2. Install easymode from GitHub WITHOUT dependency resolution.
# --no-deps keeps your numpy>=2 stack intact, and also upgrades over any older easymode
# (e.g. a 0.0.x already installed from PyPI).
pip install --no-deps git+https://github.com/mgflast/easymode.gitVerify the install:
python -c "import numpy, easymode, importlib.metadata as m; print('numpy', numpy.__version__, '| easymode', m.version('easymode'))"
# expected: numpy 2.x | easymode 1.0.0After installation, the copick inference easymode command becomes available:
# Basic usage - 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,microtubule -t wbp@10.0
# Segment specific runs
copick inference easymode -c config.json -m membrane -t wbp@10.0 --run run001,run002
# Use specific GPUs
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --gpus 0,1
# High quality with test-time augmentation
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --tta 16
# Don't add object definitions to config
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --no-add-objects
# Overwrite existing segmentations
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --overwriteThe following pretrained segmentation models are available:
| Model | Description |
|---|---|
ribosome |
Ribosome particles |
membrane |
Cellular membranes |
microtubule |
Microtubules |
actin |
Actin filaments |
cytoplasm |
Cytoplasm region |
mitochondrion |
Mitochondria |
nucleus |
Nuclear region |
nuclear_envelope |
Nuclear envelope |
npc |
Nuclear pore complex |
cytoplasmic_granule |
Cytoplasmic granules |
mitochondrial_granule |
Mitochondrial granules |
prohibitin |
Prohibitin complexes |
tric |
TRiC/CCT chaperonin |
vault |
Vault particles |
void |
Void/empty regions |
| Option | Description |
|---|---|
-c, --config |
Path to copick configuration file (or set COPICK_CONFIG env var) |
-m, --model |
Comma-separated list of models to run (required) |
-t, --tomogram |
Tomogram URI as type@voxel_size e.g., wbp@10.0 (required) |
-r, --run |
Run name(s) to process, comma-separated. Empty = all runs |
--gpus |
Comma-separated GPU IDs. Default: all available |
--tta |
Test-time augmentation level 1-16. Higher = better but slower. Default: 4 |
--batch-size |
Batch size for inference. Default: 1 |
--add-objects/--no-add-objects |
Add object definitions to config if missing. Default: enabled |
--overwrite/--no-overwrite |
Overwrite existing segmentations. Default: disabled |
--user-id |
User ID for created segmentations. Default: copick |
--session-id |
Session ID for created segmentations. Default: 1 |
--debug/--no-debug |
Enable debug logging |
When --add-objects is enabled (default), the plugin automatically adds object definitions to your copick config for any segmented features that don't already exist. These are added with minimal defaults:
is_particle: False (segmentation target)label: Auto-assigned (next available integer)color: Auto-assigned
You can edit the config file afterward to add additional metadata like emdb_id, pdb_id, radius, etc.
Segmentations are stored in the copick project at:
{overlay_root}/ExperimentRuns/{run_name}/VoxelSpacing{voxel_size:.3f}/Segmentations/
Each segmentation is stored as a zarr array with OME-Zarr metadata.
- Python >= 3.10, < 3.13
- copick >= 1.24.1
- numpy >= 2.0.2
- TensorFlow >= 2.16
- easymode (installed separately from GitHub — see Installation)
This plugin runs the pretrained easymode models. If you use it in your research, please cite the easymode preprint:
So-Last, M. G. F., Hale, T., Burt, A., & Allegretti, M. (2026). Easymode: general pretrained networks for cellular cryo-ET enable flexible approaches to subtomogram averaging. bioRxiv. https://www.biorxiv.org/content/10.64898/2026.05.19.726344v1
GPLv3 License