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:
Roboflow Dataset Upload,Line Counter Visualization,Mask Edge Snap,OCR Model,Image Slicer,Gaze Detection,Instance Segmentation Model,Color Visualization,Bounding Rectangle,Ellipse Visualization,Polygon Visualization,ByteTrack Tracker,Relative Static Crop,Byte Tracker,Detections Consensus,Detections Classes Replacement,Webhook Sink,Trace Visualization,Object Detection Model,Camera Focus,Stitch OCR Detections,Qwen 3.5 API,OpenAI,SAM 3,Image Threshold,Heatmap Visualization,SORT Tracker,Florence-2 Model,Halo Visualization,Detections Transformation,Path Deviation,GLM-OCR,Dot Visualization,S3 Sink,Path Deviation,Seg Preview,Twilio SMS Notification,Model Monitoring Inference Aggregator,Google Gemini,Roboflow Dataset Upload,Dynamic Zone,VLM As Classifier,Pixelate Visualization,Line Counter,Twilio SMS/MMS Notification,Polygon Zone Visualization,Motion Detection,Blur Visualization,Background Subtraction,Text Display,CSV Formatter,Stability AI Image Generation,Detections Merge,Perspective Correction,Overlap Filter,Anthropic Claude,Bounding Box Visualization,Velocity,Depth Estimation,Stability AI Inpainting,Polygon Visualization,SIFT,Roboflow Vision Events,VLM As Detector,Google Gemini,Label Visualization,Grid Visualization,Qwen3.5-VL,Contrast Equalization,Per-Class Confidence Filter,Triangle Visualization,Halo Visualization,Circle Visualization,Segment Anything 2 Model,Mask Visualization,OpenAI,MoonshotAI Kimi,Llama 3.2 Vision,Email Notification,Slack Notification,Detections Stitch,Detections Stabilizer,Object Detection Model,Stability AI Outpainting,Email Notification,Google Gemma API,Google Vision OCR,Identify Outliers,Image Preprocessing,Google Gemini,EasyOCR,Detections Combine,Object Detection Model,Cosine Similarity,SAM2 Video Tracker,Detection Event Log,Byte Tracker,OpenAI,Anthropic Claude,Time in Zone,Model Comparison Visualization,Roboflow Custom Metadata,YOLO-World Model,Detection Offset,Instance Segmentation Model,Single-Label Classification Model,Detections List Roll-Up,VLM As Classifier,Template Matching,Mask Area Measurement,Stitch Images,Qwen 3.6 API,SIFT Comparison,Morphological Transformation,Instance Segmentation Model,CogVLM,Crop Visualization,Camera Calibration,Florence-2 Model,Time in Zone,OC-SORT Tracker,SAM 3,Icon Visualization,Detections Filter,Local File Sink,Image Contours,JSON Parser,Keypoint Detection Model,Time in Zone,Reference Path Visualization,Anthropic Claude,Clip Comparison,VLM As Detector,LMM,Identify Changes,Classification Label Visualization,Image Slicer,Absolute Static Crop,Image Blur,Byte Tracker,Multi-Label Classification Model,Image Convert Grayscale,SAM 3,OpenAI,Corner Visualization,Dynamic Crop,Moondream2,Keypoint Visualization,Keypoint Detection Model,QR Code Generator,Camera Focus,LMM For Classification,Morphological Transformation,Keypoint Detection Model,Contrast Enhancement,Background Color Visualization,PTZ Tracking (ONVIF),Stitch OCR Detections,SIFT Comparison - outputs:
Detections Stabilizer,Detections Stitch,Roboflow Dataset Upload,Mask Edge Snap,Distance Measurement,Color Visualization,Detections Combine,SAM2 Video Tracker,Detection Event Log,Ellipse Visualization,Polygon Visualization,ByteTrack Tracker,Byte Tracker,Bounding Rectangle,Byte Tracker,Time in Zone,Detections Classes Replacement,Detections Consensus,Model Comparison Visualization,Trace Visualization,Camera Focus,Roboflow Custom Metadata,Detection Offset,Detections List Roll-Up,Size Measurement,Mask Area Measurement,Heatmap Visualization,SORT Tracker,Florence-2 Model,Halo Visualization,Detections Transformation,Crop Visualization,Florence-2 Model,Path Deviation,Time in Zone,Dot Visualization,OC-SORT Tracker,Path Deviation,Model Monitoring Inference Aggregator,Detections Filter,Icon Visualization,Roboflow Dataset Upload,Dynamic Zone,Pixelate Visualization,Line Counter,Time in Zone,Blur Visualization,Detections Merge,Perspective Correction,Overlap Filter,Line Counter,Velocity,Bounding Box Visualization,Byte Tracker,Stability AI Inpainting,Polygon Visualization,Roboflow Vision Events,Label Visualization,Corner Visualization,Dynamic Crop,Per-Class Confidence Filter,Triangle Visualization,Halo Visualization,Circle Visualization,Segment Anything 2 Model,Mask Visualization,Background Color Visualization,PTZ Tracking (ONVIF)
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,keypoint_detection_prediction,instance_segmentation_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
}