The Model stage

Supercharge your workflow with Model stages.

With Model stages you can use a model that has been trained in V7, or one of your own external models, to pre-annotate the files that will be sent to the next workflow stage.

Model stages can be used to create small efficiency gains like tagging data that should be labelled/archived with a classifier, or, with a well-trained enough model, take the place of a human annotation stage entirely.

To set up a Model stage:

1. Drag and drop the Model stage into your workflow template.

2. Plug the stage into the previous workflow stage (where the files will be flowing from) and the next workflow stage in sequence (where the model-labelled files will be flowing to).

3. To configure the stage:

• Select the trained/registered model that you will be using to run inference on your data (If you haven’t trained a model in V7, or registered your own external model check out the following guides: Train a neural network in V7 ; Register an external model)
• Map the classes that the model was trained on to corresponding classes of the labels that will be created
• Toggle Auto On/Off. If enabled, your model will turn on whenever an inference request is made, and turn off whenever more than 1 hour has passed since the last request.

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Self-Assignment

As an automated stage, the Model Stage requires files to be assigned to it in order to be triggered.

As a result, if a Model Stage is the first stage after the Dataset Stage in a workflow, it will be necessary for a User, Admin, or Workforce Manager to assign files to the stage manually to kick off the workflow. Workers will not be able to self-assign files until they have passed through the Model Stage.