How it works

The Model-assisted labeling (MAL) workflow allows you to import computer-generated predictions (or simply annotations created outside of Labelbox) and load them as pre-labels on an asset. Using MAL for pre-labeling can be useful for speeding up the labeling process and supporting human labeling efforts.

Imported annotations will appear when the asset is opened in the Editor as long as the following conditions are met:

• Model-assisted labeling is on

• The imported annotations are assigned to a Data Row

• The asset has not already been labeled in the Labelbox Editor

Currently, if a labeler skips or submits a label without making any changes first, the timer will not start and the duration time recorded will be 0 seconds. This logic may be revised in a future update.

Follow these instructions for importing annotations as pre-labels via the Model-assisted labeling workflow.

Step 1: Prepare your JSON file

Your file should be in newline delimited (NDJSON) format. This means that each newline in your NDJSON file should be the entirety of a JSON object.

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Note

If you import an annotation to a Data Row and there is already an imported annotation with the same uuid on that Data Row, the latest import will override the previous one.

Make sure the annotations are in the proper format. Use the chart below to determine whether an annotation type is supported for the data type you are labeling.

Step 2: Import your annotations

Use the Python SDK to create an import job for your annotations. Use the tutorials at the top of this page to learn how to import annotations for each data type.

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Note

Before you begin a new import job to import annotations to a Data Row, make sure there are no existing MAL annotations on the Data Row. Duplicate import jobs may result in unexpected behavior.