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