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

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

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"
}