AI-assisted Annotation
Use interactive and batch assists inside human-controlled quality gates.
Unitlab combines interactive assistance, prompt-based labeling, temporal tracking, batch automation, and model stages inside the same ontology and quality workflow. Generated labels remain editable proposals until a human reviews and routes them.
Watch AI-assisted annotation
Unitlab’s assists accelerate proposal creation; they do not change who is accountable for the label. Use the smallest assist that matches the task, correct every proposal, and measure systematic errors before expanding volume.
Before you make the change
Define the class, geometry, output, acceptance rule, and human owner.
Choose a representative calibration cohort that exposes model weaknesses.
Know the model or assist version and the route for empty, malformed, low-confidence, or failed output.
Magic Touch creates an editable seed mask; Find Similar proposes matching instances on the current image or frame for explicit review.
Understand the product behavior
Unitlab combines local assistance inside the workbench with Model stages inside workflows. Assisted results remain proposals until they are reviewed and accepted through the annotation and quality process.
Magic Touch
Magic Touch is available in image and video toolbars as an interactive mask-oriented tool. When no compatible class is selected, it opens a Create Class dialog configured for a Mask class, with name, color, and numeric hotkey fields.
The correct production loop is:
Select the target mask class.
Guide the proposal on a representative object.
Inspect boundaries, holes, thin structures, and occlusion.
Correct with manual tools.
Review the final mask under the same policy as a fully manual label.
Prompt Auto-Labeling and Detect all objects
A class-aware auto-labeling panel contains:
current class;
prompt textbox;
prompt initially populated from the class name;
reset prompt to class name;
Detect all objects.
Detect all objects provides prompt-based, class-aware detection within the current image or video frame.
Detect all objects is available on images and individual video frames. It uses shortcut S, opens a Create annotations popup with a SAM 3 badge, and requires an active class with bounding-box, polygon, mask, or cuboid geometry. The prompt defaults to the class name and accepts up to 300 characters. Each call is scoped to the current image or current frame, can return up to a configured maximum number of candidates, and consumes one AI-inference quota unit when successful.
Annotators review proposed objects before accepting them. Prompt changes can alter the candidate distribution even when the ontology class name remains the same, so teams can standardize prompts in their labeling instructions.
The prompt scopes the current inference request; the ontology class still governs the accepted annotation.
Find Similar
Find Similar is a contextual header action, not a drawing tool. It becomes available when the selected object is a box, polygon, or mask. It searches the current image or current video frame, exposes a confidence threshold, removes strong overlaps with existing annotations, and holds new predictions for review before commitment. It does not search across the full dataset and does not consume AI-inference quota.
Find Similar can return multiple candidates at the selected confidence threshold, with Clear and Accept all actions available. Seed quality, visual repetition, clutter, scale, and threshold affect the result, so operators review each candidate set in context before acceptance.
Auto-Tracking and interpolation
Auto-Tracking predicts later frames with machine learning; interpolation fills geometry between keyframes. Both results remain visible on the timeline for human review and correction.
Review identity, geometry drift, entrances, exits, occlusion, and reappearance across the complete timeline.
Magic Crop
Magic Crop runs model-assisted labeling inside a user-selected crop region. It is useful when the relevant objects occupy one part of a larger surface and whole-image detection would create unnecessary proposals. Each call uses AI-inference quota and still requires review before acceptance.
AI captioning
AI-assisted captioning can propose text for the current item. The proposal should be reviewed against the item-property or captioning ontology rather than treated as an automatically accepted description.
Batch auto-annotation
Unitlab supports batch auto-annotation for model-assisted labeling. Batch jobs apply model assistance across a selected data scope, while human review remains part of the annotation and workflow process.
Model stages
A Model stage can be placed between human stages in a workflow. Model output can move forward to annotation or review, while rejected outcomes return through the configured correction route. Inference behavior follows the connected model’s input, output, and class mapping configuration.
A production model stage must expose its output to annotation, review, rejection, and correction rather than silently marking predictions complete.
Introduce an assist safely
Decisions that affect production
Interactive vs batch
Use interactive assists for expert-in-the-loop correction; use batch only after the same behavior is calibrated.
Confidence
Use as a triage or routing signal, not a substitute for the quality contract.
Class mapping
Validate every model-to-ontology mapping before production.
Failure
Route failed or empty output visibly; never let it look like completed annotation.
A faster proposal that increases reviewer correction or hides failure is not a production improvement.
Continue the operating flow
Keep assisted work inside the same ontology and workflow.
Review systematic error cohorts in Project Data.
Version material model or mapping changes.
Explore related Unitlab capabilities: Unitlab’s data annotation platform · multimodal data annotation