Deploying Models for Inference

Once you have extracted a TorchScript model you can use it in two ways:

  1. omero-screen pipeline — automatic inference during plate analysis.
  2. omero-screen-napari — interactive classification in the Gallery / Classifier Selector widget.

Exported model files

cellclass extract best_run.json --save produces two files:

shufflenet2x1_0_c2_l2.pt    # TorchScript model (architecture + weights)
shufflenet2x1_0_c2_l2.json  # Sidecar: channel names, class labels, input size

The filename encodes the architecture, number of input channels (c2), and number of class labels (l2). Both files must be kept together.

omero-screen pipeline

The pipeline loads classifiers from OMERO, not from local files. Publish the .pt together with its .json sidecar once:

omero-screen models publish shufflenet2x1_0_c2_l2.pt

This attaches both files to your Classifiers project in OMERO. To publish a retrained model under the same name, add --replace. A new name (for example, with a version) keeps the old model available.

Then run the pipeline with the model name, without .pt:

omero-screen <plate_id> --inference shufflenet2x1_0_c2_l2

Multiple models can be chained (several names after --inference, or colon-separated in OMERO_SCREEN_INFERENCE_MODEL):

omero-screen <plate_id> --inference mitosis_model ppase_model

Downloaded models are cached in ~/.cache/omero_screen/ and fetched again if the published file changes.

Gallery images showing example cells per predicted class are attached as PNG figures to the OMERO plate. Configure gallery output with:

export OMERO_SCREEN_INFERENCE_GALLERY_WIDTH=10   # grid size (default: 10)
export OMERO_SCREEN_INFERENCE_BATCH_SIZE=100     # inference batch size (default: 100)

See the pipeline configuration for the full list of variables.

omero-screen-napari

Copy the .pt + .json pair to the location specified when creating the classifier in the Setup Training Widget. The Classifier Selector widget will load the model and display per-class statistics alongside the database annotation counts.

Verifying a deployed model

Before committing to a production run, evaluate the model on your full dataset:

cellclass test -s shufflenet2x1_0_c2_l2.pt ~/data/mitosis-rpe/rois.npz

This prints precision, recall, and F1 per class, plus overall accuracy, on the held-out test split. A model is ready for deployment when test accuracy is ≥ 90 % and no class has recall below 0.85.