CellClass — ML Cell Classifier

cellclass is the machine-learning training pipeline for the OmeroScreen ecosystem. It trains lightweight convolutional neural networks to classify single-cell crops generated by the Training Widget and deploys the resulting TorchScript models back into the omero-screen pipeline for automated inference.

The package follows a four-step workflow:

1. Create dataset — normalise and package .npy crop files into a single .npz archive.
2. Write a training batch file — one run per line with model and hyperparameter flags.
3. Train — run all jobs locally or on a SLURM cluster; results log to Weights & Biases.
4. Extract model — export the best checkpoint to a TorchScript .pt + .json sidecar for inference.

Note

Every command is documented, option by option, in the generated CLI reference. The pages in this guide explain why and when to use them.