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

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"
}