LabelBlend AI Studio logoLabelBlend AI StudioVerma AgentiX

AI annotation

Annotate With AI. Review With Control.

AI assistance in LabelBlend proposes segmentation masks and labelled regions. You decide what becomes part of the dataset — nothing is committed without confirmation.

Try LabelBlendWatch the SAM2 tutorial

Before · During · After

Three states of the same annotation

The value of AI assistance is the first pass. The value of the workspace is what happens next.

Before

Raw image

An unlabelled image loaded from your dataset folder. No annotations exist yet.

During

AI segmentation

SAM2 proposes a mask for the object you clicked. The proposal is visible on the canvas and attached to a class.

After

Validated annotation

You confirm, adjust vertices, reassign the class, or delete the proposal. Only confirmed annotations reach the export.

SAM2

AI-Assisted Segmentation with SAM2.

Download a SAM2 checkpoint through the application, connect it, and use clicks to generate mask proposals. The checkpoint and its config file are both selectable, so you can work with the model size that fits your GPU.

  1. Download checkpoint→
  2. Connect→
  3. Click object→
  4. Add object→
  5. Confirm
AI-generated annotations should be reviewed before use in production or research datasets. Segmentation quality depends on the image, the object, the model checkpoint and the settings you choose — LabelBlend does not claim universal accuracy.
LabelBlend for Fine-Tuning
SAM2 assisted mask segmentation in LabelBlend
Mask workspace with model connection status, class list and labelled objects.
LabelBlend for Fine-Tuning
Text-driven multi-class labeling in LabelBlend
Prompt workspace with box and text thresholds and automation controls.

Text-driven labeling

Describe What You Need. Automate the Repetition.

Write the classes you are looking for as short lowercase phrases and let the prompt workflow propose labelled regions across your images.

Prompt

car. person. traffic light.

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Multi-class labeling across the image set

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Reviewed, annotated dataset

  • · Box threshold and text threshold control how confident a proposal must be.
  • · Proposals are committed explicitly, not applied silently.
  • · Results vary by phrasing, image content and scene complexity.

Manual vs. AI-assisted

Neither approach replaces the other. Most projects use both.

Manual annotation

  • Full control over every vertex and class
  • Predictable output for unusual or ambiguous objects
  • No model download or GPU requirement
  • Slower on large image sets

AI-assisted annotation

  • Fast first pass on clearly separable objects
  • Mask proposals instead of manual polygon drawing
  • Requires model download and suitable hardware
  • Always followed by human review

Put AI assistance behind your own review process

Download for WindowsRead the documentation