Freeform Text

Write open-ended text annotations for your images in any format, including JSON, plain text, or custom schemas for flexible VLM training.

Before You Start

Freeform text annotations give you full control over the annotation format for each image. Instead of predefined structures like bounding boxes or question-answer pairs, you write any text you want in a free-form editor. This is useful for custom JSON schemas, detailed descriptions, multi-attribute labels, or any format your training pipeline requires. This guide walks through creating freeform text annotations in Datature Vi.


Open the annotator

Go to your dataset, then click the Annotate tab. Click any image thumbnail in the bottom strip to load it onto the canvas.

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Open the Annotator tab

Open the Annotator tab

From the Dataset Overview page, click the Annotator tab to open the labeling interface.

You should see
Dataset Overview showing image and annotation count

Your annotations are ready when you see annotation count matching the image count in the Dataset Overview.


Keyboard shortcuts

Keyboard shortcuts

Key
Action

Annotation guidelines

These guidelines produce annotations that train well. Consistency across your dataset matters more than the format you choose.

JSON example:

{
  "description": "A solar panel array on a residential rooftop",
  "condition": "good",
  "panel_count": 12,
  "issues": ["minor dust accumulation on panels 3 and 7"],
  "orientation": "south-facing"
}

Plain text example:

  Description: A solar panel array on a residential rooftop
  Condition: Good
  Panel count: 12
  Issues: Minor dust accumulation on panels 3 and 7
  Orientation: South-facing

Edit or delete annotations

To edit an annotation: Click in the Freeform panel text area and make your changes. Changes save automatically.

To clear an annotation: Select all text in the editor and delete it. The annotation is removed when the text is empty.

Deletions Cannot Be Undone

Deleted annotations cannot be recovered. Export your dataset regularly as a backup.


Chain-of-thought reasoning


Next steps


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