Instance Segmentation Model

v2

Class: RoboflowInstanceSegmentationModelBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.instance_segmentation.v2.RoboflowInstanceSegmentationModelBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Run inference on an instance segmentation model hosted on or uploaded to Roboflow.

You can query any model that is private to your account, or any public model available on Roboflow Universe.

You will need to set your Roboflow API key in your Inference environment to use this block. To learn more about setting your Roboflow API key, refer to the Inference documentation.

Type identifier

Use the following identifier in step "type" field: roboflow_core/roboflow_instance_segmentation_model@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
model_id	str	Roboflow model identifier..	✅
confidence	float	Confidence threshold for predictions..	✅
class_filter	List[str]	List of accepted classes. Classes must exist in the model's training set..	✅
iou_threshold	float	Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..	✅
max_detections	int	Maximum number of detections to return..	✅
class_agnostic_nms	bool	Boolean flag to specify if NMS is to be used in class-agnostic mode..	✅
max_candidates	int	Maximum number of candidates as NMS input to be taken into account..	✅
mask_decode_mode	str	Parameter of mask decoding in prediction post-processing..	✅
tradeoff_factor	float	Post-processing parameter to dictate tradeoff between fast and accurate..	✅
disable_active_learning	bool	Boolean flag to disable project-level active learning for this block..	✅
active_learning_target_dataset	str	Target dataset for active learning, if enabled..	✅

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 Instance Segmentation Model in version v2.

• inputs: Clip Comparison, Halo Visualization, Anthropic Claude, Image Blur, Email Notification, Camera Focus, Text Display, CSV Formatter, Contrast Equalization, Object Detection Model, Blur Visualization, VLM As Detector, Dimension Collapse, Corner Visualization, Dynamic Crop, Classification Label Visualization, Relative Static Crop, Roboflow Dataset Upload, Trace Visualization, Keypoint Detection Model, Multi-Label Classification Model, Camera Focus, Mask Visualization, SIFT, VLM As Classifier, Stitch OCR Detections, Background Color Visualization, PTZ Tracking (ONVIF).md), Polygon Visualization, Local File Sink, Florence-2 Model, Size Measurement, Stitch OCR Detections, Line Counter, Ellipse Visualization, Pixelate Visualization, SIFT Comparison, Roboflow Custom Metadata, Single-Label Classification Model, Identify Changes, LMM, Detections List Roll-Up, OpenAI, Circle Visualization, Dot Visualization, Identify Outliers, Twilio SMS Notification, SIFT Comparison, Absolute Static Crop, Morphological Transformation, Template Matching, Crop Visualization, Single-Label Classification Model, Google Gemini, Dynamic Zone, Keypoint Visualization, Polygon Visualization, Google Vision OCR, Icon Visualization, Multi-Label Classification Model, Llama 3.2 Vision, Color Visualization, Image Contours, Stitch Images, VLM As Classifier, OpenAI, LMM For Classification, Email Notification, VLM As Detector, Anthropic Claude, Distance Measurement, OpenAI, Bounding Box Visualization, Triangle Visualization, Background Subtraction, Grid Visualization, Model Comparison Visualization, Reference Path Visualization, Line Counter Visualization, EasyOCR, Halo Visualization, Detections Consensus, Slack Notification, OCR Model, Stability AI Inpainting, Instance Segmentation Model, Image Slicer, Line Counter, Detection Event Log, Google Gemini, Florence-2 Model, JSON Parser, Image Convert Grayscale, Stability AI Image Generation, Heatmap Visualization, Pixel Color Count, Polygon Zone Visualization, Image Slicer, Label Visualization, Google Gemini, Depth Estimation, Image Preprocessing, OpenAI, Object Detection Model, Stability AI Outpainting, Buffer, Webhook Sink, Image Threshold, Instance Segmentation Model, Anthropic Claude, CogVLM, Camera Calibration, Perspective Correction, QR Code Generator, Motion Detection, Keypoint Detection Model, Model Monitoring Inference Aggregator, Twilio SMS/MMS Notification, Roboflow Dataset Upload, Clip Comparison
• outputs: Halo Visualization, Qwen3-VL, Object Detection Model, Segment Anything 2 Model, Blur Visualization, Detections Stabilizer, Roboflow Dataset Upload, Corner Visualization, Dynamic Crop, Trace Visualization, Multi-Label Classification Model, Keypoint Detection Model, SAM 3, Qwen2.5-VL, Mask Visualization, Camera Focus, Path Deviation, Detections Combine, Background Color Visualization, Bounding Rectangle, PTZ Tracking (ONVIF).md), Polygon Visualization, Moondream2, Florence-2 Model, Size Measurement, Line Counter, Ellipse Visualization, Pixelate Visualization, Roboflow Custom Metadata, Byte Tracker, Single-Label Classification Model, SAM 3, Detections List Roll-Up, Dot Visualization, Circle Visualization, Byte Tracker, Velocity, Overlap Filter, Crop Visualization, Single-Label Classification Model, Dynamic Zone, Polygon Visualization, Icon Visualization, Multi-Label Classification Model, Color Visualization, Distance Measurement, SmolVLM2, Triangle Visualization, Detections Classes Replacement, Bounding Box Visualization, Model Comparison Visualization, Instance Segmentation Model, Halo Visualization, Detections Consensus, Time in Zone, Stability AI Inpainting, Line Counter, Detection Event Log, Florence-2 Model, Mask Area Measurement, Heatmap Visualization, Detections Filter, Label Visualization, Time in Zone, Detections Stitch, Time in Zone, Detections Merge, Object Detection Model, Webhook Sink, Instance Segmentation Model, Byte Tracker, Path Deviation, Perspective Correction, SAM 3, Detections Transformation, Keypoint Detection Model, Model Monitoring Inference Aggregator, Detection Offset, Roboflow Dataset Upload

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Instance Segmentation Model in version v2 has.

Bindings

• input

  • images (image): The image to infer on..
  • model_id (roboflow_model_id): Roboflow model identifier..
  • confidence (float_zero_to_one): Confidence threshold for predictions..
  • class_filter (list_of_values): List of accepted classes. Classes must exist in the model's training set..
  • iou_threshold (float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
  • max_detections (integer): Maximum number of detections to return..
  • class_agnostic_nms (boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..
  • max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
  • mask_decode_mode (string): Parameter of mask decoding in prediction post-processing..
  • tradeoff_factor (float_zero_to_one): Post-processing parameter to dictate tradeoff between fast and accurate..
  • disable_active_learning (boolean): Boolean flag to disable project-level active learning for this block..
  • active_learning_target_dataset (roboflow_project): Target dataset for active learning, if enabled..

• output

  • inference_id (inference_id): Inference identifier.
  • predictions (instance_segmentation_prediction): Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object.
  • model_id (roboflow_model_id): Roboflow model id.

Example JSON definition of step Instance Segmentation Model in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_instance_segmentation_model@v2",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "confidence": 0.3,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "iou_threshold": 0.4,
    "max_detections": 300,
    "class_agnostic_nms": true,
    "max_candidates": 3000,
    "mask_decode_mode": "accurate",
    "tradeoff_factor": 0.3,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}

v1

Class: RoboflowInstanceSegmentationModelBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.instance_segmentation.v1.RoboflowInstanceSegmentationModelBlockV1

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

Run inference on an instance segmentation model hosted on or uploaded to Roboflow.

You can query any model that is private to your account, or any public model available on Roboflow Universe.

You will need to set your Roboflow API key in your Inference environment to use this block. To learn more about setting your Roboflow API key, refer to the Inference documentation.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
model_id	str	Roboflow model identifier..	✅
confidence	float	Confidence threshold for predictions..	✅
class_filter	List[str]	List of accepted classes. Classes must exist in the model's training set..	✅
iou_threshold	float	Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..	✅
max_detections	int	Maximum number of detections to return..	✅
class_agnostic_nms	bool	Boolean flag to specify if NMS is to be used in class-agnostic mode..	✅
max_candidates	int	Maximum number of candidates as NMS input to be taken into account..	✅
mask_decode_mode	str	Parameter of mask decoding in prediction post-processing..	✅
tradeoff_factor	float	Post-processing parameter to dictate tradeoff between fast and accurate..	✅
disable_active_learning	bool	Boolean flag to disable project-level active learning for this block..	✅
active_learning_target_dataset	str	Target dataset for active learning, if enabled..	✅

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 Instance Segmentation Model in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Instance Segmentation Model in version v1 has.

Bindings

• input

  • images (image): The image to infer on..
  • model_id (roboflow_model_id): Roboflow model identifier..
  • confidence (float_zero_to_one): Confidence threshold for predictions..
  • class_filter (list_of_values): List of accepted classes. Classes must exist in the model's training set..
  • iou_threshold (float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..
  • max_detections (integer): Maximum number of detections to return..
  • class_agnostic_nms (boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..
  • max_candidates (integer): Maximum number of candidates as NMS input to be taken into account..
  • mask_decode_mode (string): Parameter of mask decoding in prediction post-processing..
  • tradeoff_factor (float_zero_to_one): Post-processing parameter to dictate tradeoff between fast and accurate..
  • disable_active_learning (boolean): Boolean flag to disable project-level active learning for this block..
  • active_learning_target_dataset (roboflow_project): Target dataset for active learning, if enabled..

• output

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

Example JSON definition of step Instance Segmentation Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_instance_segmentation_model@v1",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "confidence": 0.3,
    "class_filter": [
        "a",
        "b",
        "c"
    ],
    "iou_threshold": 0.4,
    "max_detections": 300,
    "class_agnostic_nms": true,
    "max_candidates": 3000,
    "mask_decode_mode": "accurate",
    "tradeoff_factor": 0.3,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}