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

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