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:
Image Threshold,Email Notification,Corner Visualization,Roboflow Dataset Upload,Object Detection Model,Stitch OCR Detections,Gaze Detection,Stability AI Image Generation,Time in Zone,Grid Visualization,Dynamic Crop,Image Slicer,Image Preprocessing,Instance Segmentation Model,SIFT,Line Counter Visualization,Detections Combine,Trace Visualization,Halo Visualization,ByteTrack Tracker,Roboflow Custom Metadata,Pixelate Visualization,Circle Visualization,S3 Sink,Detections Classes Replacement,Keypoint Detection Model,Twilio SMS Notification,Halo Visualization,SIFT Comparison,Anthropic Claude,OC-SORT Tracker,Polygon Visualization,Detections Consensus,Cosine Similarity,Identify Changes,Crop Visualization,Roboflow Dataset Upload,Mask Visualization,Detection Offset,Heatmap Visualization,Webhook Sink,Detections List Roll-Up,Google Vision OCR,Florence-2 Model,Florence-2 Model,VLM As Classifier,Overlap Filter,Anthropic Claude,OpenAI,VLM As Detector,OpenAI,PTZ Tracking (ONVIF),Bounding Rectangle,Background Color Visualization,Template Matching,Anthropic Claude,Background Subtraction,SIFT Comparison,Multi-Label Classification Model,Keypoint Visualization,Time in Zone,Detections Filter,Stitch OCR Detections,LMM,Detections Merge,Detections Transformation,Identify Outliers,SAM 3,Motion Detection,Dynamic Zone,Seg Preview,Single-Label Classification Model,Object Detection Model,Roboflow Vision Events,VLM As Classifier,Detections Stitch,Triangle Visualization,Google Gemini,Path Deviation,Image Slicer,Image Contours,Model Comparison Visualization,Stability AI Outpainting,Stitch Images,Image Blur,Ellipse Visualization,OpenAI,Time in Zone,Depth Estimation,EasyOCR,Absolute Static Crop,JSON Parser,CogVLM,Google Gemini,Velocity,Relative Static Crop,Morphological Transformation,LMM For Classification,Detection Event Log,Dot Visualization,GLM-OCR,Model Monitoring Inference Aggregator,Keypoint Detection Model,Image Convert Grayscale,Icon Visualization,QR Code Generator,Detections Stabilizer,Camera Focus,SAM 3,OCR Model,Text Display,Reference Path Visualization,Instance Segmentation Model,Llama 3.2 Vision,CSV Formatter,SORT Tracker,Byte Tracker,Label Visualization,Classification Label Visualization,Byte Tracker,Segment Anything 2 Model,Polygon Zone Visualization,Stability AI Inpainting,Google Gemini,SAM 3,Perspective Correction,Camera Calibration,Qwen3.5-VL,Email Notification,Contrast Equalization,Line Counter,Path Deviation,Byte Tracker,Color Visualization,OpenAI,Local File Sink,Mask Area Measurement,YOLO-World Model,Twilio SMS/MMS Notification,Clip Comparison,Blur Visualization,Bounding Box Visualization,Camera Focus,Polygon Visualization,Moondream2,VLM As Detector,Slack Notification - outputs:
Corner Visualization,Ellipse Visualization,Roboflow Dataset Upload,Time in Zone,Time in Zone,Dynamic Crop,Velocity,Detections Combine,Trace Visualization,Detection Event Log,Halo Visualization,Dot Visualization,ByteTrack Tracker,Model Monitoring Inference Aggregator,Roboflow Custom Metadata,Pixelate Visualization,Circle Visualization,Icon Visualization,Detections Classes Replacement,Detections Stabilizer,Halo Visualization,Camera Focus,OC-SORT Tracker,Polygon Visualization,Detections Consensus,Crop Visualization,Roboflow Dataset Upload,Mask Visualization,Detection Offset,SORT Tracker,Heatmap Visualization,Byte Tracker,Byte Tracker,Label Visualization,Detections List Roll-Up,Florence-2 Model,Segment Anything 2 Model,Florence-2 Model,Stability AI Inpainting,Overlap Filter,Perspective Correction,Distance Measurement,PTZ Tracking (ONVIF),Bounding Rectangle,Background Color Visualization,Size Measurement,Time in Zone,Line Counter,Path Deviation,Detections Filter,Detections Merge,Detections Transformation,Byte Tracker,Line Counter,Color Visualization,Dynamic Zone,Roboflow Vision Events,Mask Area Measurement,Detections Stitch,Triangle Visualization,Blur Visualization,Bounding Box Visualization,Polygon Visualization,Path Deviation,Model Comparison Visualization
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[keypoint_detection_prediction,instance_segmentation_prediction,object_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
}