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

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"
}