SAM 3 Multi-Instance Auto-Labeling
Labeling multiple instances of the same object class in a single image (e.g., vehicles in a parking lot, players on a field, or products on a shelf) can be extremely repetitive and time-consuming. With SAM 3 Multi-Instance Auto-Labeling (powered by Meta AI’s Segment Anything Model 3), Labellerr allows you to label all similar objects in an image using just a single visual prompt. SAM 3 intelligently identifies and labels all matching instances across the entire image canvas, dramatically reducing manual effort and accelerating your annotation pipeline.Key Features
- Single-Prompt Auto-Labeling: Draw a single bounding box or polygon outline, and the system automatically segment-labels all other similar objects in the image.
- Advanced Segmentation Power: Powered by SAM 3’s high-precision, zero-shot segmentation capabilities.
- Consistency: Maintains uniform labeling standards and boundary shapes across all detected object instances.
- Multi-Instance Speedup: Annotate dozens or hundreds of similar objects in seconds rather than hours.
Step-by-Step Guide: Using SAM 3 Multi-Instance Auto-Labeling
1. Initialize the Prompt
- Open your image annotation project and choose the appropriate target label.
- Draw a bounding box or polygon (using SAM) annotation on your object to serve as the visual prompt.
2. Activate Similar Object Detection
- Select the created annotation on the canvas.
- Press I on your keyboard to trigger SAM 3 auto-annotation.
- The model will process the visual signature and automatically annotate all similar objects across the image.
3. Review and Save
- Inspect the generated annotations.
- Manually refine any boundaries if necessary, and click save to apply the updates.
Future Roadmap
- Negative & Incremental Prompting: Add support to exclude specific false-positives or add refined suggestions to improve detection accuracy.
- Granular Controls: Introduce individual accept/reject controls for each detected instance (e.g., “accept all”, “reject all”).
- Latency Optimization: Reduce inference and processing times for faster annotation cycles.
- Video Support: Extend similar object tracking to video sequences for automated frame-by-frame propagation.