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

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

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"
}