> ## Documentation Index
> Fetch the complete documentation index at: https://docs.labellerr.com/llms.txt
> Use this file to discover all available pages before exploring further.

# SAM 3 Multi-Instance Auto-Labeling

> Learn how Labellerr's SAM 3 Multi-Instance Auto-Labeling allows you to label all similar objects in an image with a single visual prompt to speed up annotation workflows.

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## Visual Prompt based Multi-Labeling

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## Text Prompt based Multi-Labeling

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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.

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## 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.

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## 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.

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## 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.