# 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.

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# 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.

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# omero-screen pipeline

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

``` bash
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`:

``` bash
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`):

``` bash
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:

``` bash
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](../../user-guide/installation.md#configure) for the full list of variables.

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# omero-screen-napari

Copy the `.pt` + `.json` pair to the location specified when creating the classifier in the [Setup Training Widget](../../napari/user-guide/training_widget.md). The [Classifier Selector](../../napari/user-guide/session_manager.md) widget will load the model and display per-class statistics alongside the database annotation counts.

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# Verifying a deployed model

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

``` bash
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.