Detect Anything (SAM 1–SAM 3)

Choose and operate Unitlab’s SAM-assisted segmentation, class-prompt detection, and supported geometry workflows.

Unitlab combines interactive segmentation, class-prompted detection, and temporal propagation under one governed annotation workflow. Choose the smallest operation that produces the geometry you need, then review the proposals before moving the item forward.

Magic Touch and repeated-object assistance in the Unitlab Workbench

Current Unitlab product visual: one assisted selection becomes a set of editable object proposals.

The current Workbench exposes SAM 1 and SAM 3 for interactive segmentation. Detect all objects is the SAM 3 class-prompt operation. Temporal propagation belongs to Bidirectional Auto-Tracking; SAM 2 is not a selectable Detect-all generation in the current UI.

Choose the operation

Goal	Operation	Current output
Isolate one object from a point or guided region	Magic Touch with SAM 1 or SAM 3	Editable segmentation mask
Detect every visible instance of one class	Detect all objects with SAM 3	Bounding box, polygon, mask, or cuboid
Find more objects that resemble a confirmed example	Find Similar	Bounding box, polygon, mask, or cuboid proposals
Describe the target in natural language	Prompt Labeling	Class-bound SAM 3 proposals on the current image or frame
Propagate an object through a sequence	Bidirectional Auto-Tracking	Frame- or slice-aware object track

Supported scope

Detect all objects is available for an image and for the current video frame. Select an ontology class whose geometry is one of:

• bounding box;

• polygon;

• segmentation mask;

• cuboid / 3D box.

The operation is class-aware. Unitlab writes accepted output to the active class, so the prompt does not replace ontology governance.

Before you start

• Open an image or a video frame in the Annotation Workbench.

• Confirm the intended class exists in the project ontology.

• Choose the geometry required by the downstream model.

• Read the project Instructions for inclusion, exclusion, truncation, and occlusion policy.

• Start on a representative item before processing dense or unusual scenes.

Detect all objects

1

Select the class

Open Classes and select the exact ontology class. Detect all is unavailable when the active class uses an unsupported geometry.

2

Open Auto-labeling

Select the wand action in the vertical toolbar or press S. You can also open the class action for Detect all objects of this class.

3

Set the prompt

The prompt begins with the class name. Keep it when the ontology name is visually specific, or replace it with a clearer description. The current input accepts up to 300 characters.

4

Run detection

Select Detect all objects. Unitlab evaluates the current image or current frame and returns candidate instances in the active geometry.

5

Review and correct

Inspect false positives, misses, overlap, truncation, small objects, and boundary quality. Correct accepted geometry with the standard Workbench tools.

Geometry guidance

Geometry	Use it when	Review closely
Bounding box	Coarse localization is sufficient	Tightness, truncation, overlap, tiny-object misses
Polygon	Boundary shape matters but a raster mask is not required	Vertex placement, holes, self-intersection
Mask	Pixel membership drives training or measurement	Leakage, holes, thin structures, touching instances
Cuboid / 3D box	Orientation and spatial extent are part of the label	Vanishing direction, depth edges, ground contact

SAM selection

Use SAM 1 when a stable interactive mask is sufficient and the operator wants a familiar click-guided segmentation path. Use SAM 3 for the current concept-aware segmentation and class-prompted Detect-all experience. Validate either choice on the same representative sample before standardizing it for a team.

A model proposal is not ground truth. A qualified annotator must validate class, geometry, attributes, relations, temporal identity, and workflow outcome.

Quality checklist

• The active ontology class is correct.

• Every proposal follows the project’s inclusion and occlusion policy.

• Duplicate and strongly overlapping proposals are removed.

• Small, partially visible, and edge-of-frame instances were inspected.

• Geometry was corrected at the zoom level required by Instructions.

• Required properties and relations are complete before submission.

Troubleshooting

Symptom	Check
Wand action is unavailable	Confirm the item is an image or video and the active class is box, polygon, mask, or cuboid
No objects are returned	Use a more concrete prompt, verify the object is visible, and test another representative frame
Too many unrelated objects	Narrow the prompt with object type, visual context, or distinguishing state
Boundary quality is insufficient	Switch to mask or polygon output and correct with brush, eraser, or vertex tools
Results drift across time	Detect on a reliable frame, then use bidirectional tracking and review the timeline

Related guides

• Prompt Labeling

• Find Similar

• Bidirectional Auto-Tracking

• Image Annotation

• Video Annotation