Segment Anything 2 Model

Class: SegmentAnything2BlockV1

Source: inference.core.workflows.core_steps.models.foundation.segment_anything2.v1.SegmentAnything2BlockV1

Run Segment Anything 2, a zero-shot instance segmentation model, on an image.

** Dedicated inference server required (GPU recomended) **

You can use pass in boxes/predictions from other models to Segment Anything 2 to use as prompts for the model. If you pass in box detections from another model, the class names of the boxes will be forwarded to the predicted masks. If using the model unprompted, the model will assign integers as class names / ids.

Type identifier

Use the following identifier in step "type" field: roboflow_core/segment_anything@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
version	str	Model to be used. One of hiera_large, hiera_small, hiera_tiny, hiera_b_plus.	✅
threshold	float	Threshold for predicted masks scores.	✅
multimask_output	bool	Flag to determine whether to use sam2 internal multimask or single mask mode. For ambiguous prompts setting to True is recomended..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Segment Anything 2 Model in version v1.

• inputs: Detections Filter, Grid Visualization, Detections Stitch, Detections Classes Replacement, Circle Visualization, Path Deviation, Time in Zone, Template Matching, Model Monitoring Inference Aggregator, QR Code Generator, Roboflow Dataset Upload, Image Slicer, Dot Visualization, Gaze Detection, Overlap Filter, Single-Label Classification Model, Blur Visualization, Slack Notification, Perspective Correction, Roboflow Dataset Upload, Anthropic Claude, Background Color Visualization, OpenAI, Florence-2 Model, Keypoint Detection Model, Object Detection Model, Llama 3.2 Vision, Bounding Rectangle, Dynamic Crop, Crop Visualization, OCR Model, EasyOCR, Trace Visualization, Velocity, Keypoint Detection Model, Image Threshold, Triangle Visualization, Reference Path Visualization, Model Comparison Visualization, Detection Offset, Polygon Visualization, Identify Changes, Corner Visualization, Image Slicer, Florence-2 Model, Cosine Similarity, Image Blur, Moondream2, SIFT Comparison, Bounding Box Visualization, Byte Tracker, Dynamic Zone, Stitch OCR Detections, Keypoint Visualization, Detections Transformation, Image Convert Grayscale, Byte Tracker, Detections Combine, YOLO-World Model, Line Counter Visualization, Clip Comparison, SIFT, Icon Visualization, Stability AI Inpainting, VLM as Detector, Google Vision OCR, Polygon Zone Visualization, OpenAI, Line Counter, Webhook Sink, Camera Calibration, Instance Segmentation Model, CogVLM, Time in Zone, Mask Visualization, Camera Focus, Twilio SMS Notification, Detections Consensus, SIFT Comparison, Stability AI Outpainting, Classification Label Visualization, Multi-Label Classification Model, LMM, Image Preprocessing, Time in Zone, Color Visualization, Morphological Transformation, JSON Parser, Depth Estimation, LMM For Classification, Detections Stabilizer, Ellipse Visualization, Instance Segmentation Model, Stability AI Image Generation, Segment Anything 2 Model, VLM as Classifier, VLM as Detector, Email Notification, Halo Visualization, Stitch Images, Local File Sink, Roboflow Custom Metadata, Absolute Static Crop, Google Gemini, Object Detection Model, VLM as Classifier, Image Contours, Pixelate Visualization, PTZ Tracking (ONVIF).md), CSV Formatter, Path Deviation, Label Visualization, Detections Merge, OpenAI, Byte Tracker, Identify Outliers, Contrast Equalization, Relative Static Crop
• outputs: Detections Filter, Detections Combine, Distance Measurement, Icon Visualization, Stability AI Inpainting, Detections Stitch, Detections Classes Replacement, Path Deviation, Model Monitoring Inference Aggregator, Time in Zone, Circle Visualization, Roboflow Dataset Upload, Dot Visualization, Line Counter, Overlap Filter, Blur Visualization, Perspective Correction, Time in Zone, Roboflow Dataset Upload, Background Color Visualization, Florence-2 Model, Mask Visualization, Bounding Rectangle, Detections Consensus, Dynamic Crop, Crop Visualization, Trace Visualization, Time in Zone, Color Visualization, Velocity, Triangle Visualization, Detections Stabilizer, Ellipse Visualization, Model Comparison Visualization, Segment Anything 2 Model, Detection Offset, Polygon Visualization, Corner Visualization, Halo Visualization, Florence-2 Model, Size Measurement, Roboflow Custom Metadata, Line Counter, Bounding Box Visualization, Pixelate Visualization, Byte Tracker, Dynamic Zone, PTZ Tracking (ONVIF).md), Path Deviation, Label Visualization, Detections Merge, Byte Tracker, Detections Transformation, Byte Tracker

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Segment Anything 2 Model in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • boxes (Union[object_detection_prediction, instance_segmentation_prediction, keypoint_detection_prediction]): Bounding boxes (from another model) to convert to polygons.
  • version (string): Model to be used. One of hiera_large, hiera_small, hiera_tiny, hiera_b_plus.
  • threshold (float): Threshold for predicted masks scores.
  • multimask_output (boolean): Flag to determine whether to use sam2 internal multimask or single mask mode. For ambiguous prompts setting to True is recomended..

• output

  • predictions (instance_segmentation_prediction): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.

Example JSON definition of step Segment Anything 2 Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/segment_anything@v1",
    "images": "$inputs.image",
    "boxes": "$steps.object_detection_model.predictions",
    "version": "hiera_large",
    "threshold": 0.3,
    "multimask_output": true
}