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

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

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"
}