Instance Segmentation Model¶
v3¶
Class: RoboflowInstanceSegmentationModelBlockV3 (there are multiple versions of this block)
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@v3to 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_mode |
str |
How confidence thresholds are determined.. | ✅ |
custom_confidence |
float |
Custom 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 v3.
- inputs:
Stability AI Outpainting,Multi-Label Classification Model,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Corner Visualization,Ellipse Visualization,Object Detection Model,Roboflow Vision Events,Heatmap Visualization,Trace Visualization,VLM As Classifier,OpenAI,Email Notification,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,JSON Parser,Polygon Zone Visualization,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,QR Code Generator,GLM-OCR,Stitch Images,OpenRouter,Semantic Segmentation Model,Image Blur,Model Monitoring Inference Aggregator,Clip Comparison,Dynamic Zone,Detections List Roll-Up,Instance Segmentation Model,Google Gemini,Buffer,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Roboflow Custom Metadata,Keypoint Detection Model,Florence-2 Model,Local File Sink,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,Grid Visualization,VLM As Detector,Size Measurement,Multi-Label Classification Model,Object Detection Model,LMM,Image Convert Grayscale,Reference Path Visualization,Stitch OCR Detections,Keypoint Detection Model,SIFT Comparison,Identify Changes,Roboflow Dataset Upload,CSV Formatter,S3 Sink,SIFT Comparison,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Identify Outliers,Crop Visualization,Blur Visualization,Mask Visualization,Stability AI Image Generation,Qwen-VL,Stitch OCR Detections,Google Gemma API,Image Slicer,Qwen 3.5 API,Background Color Visualization,Slack Notification,Anthropic Claude,Dimension Collapse,Qwen 3.6 API,Webhook Sink,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Detection Event Log,CogVLM,Llama 3.2 Vision,Instance Segmentation Model,Qwen3.5-VL,Camera Focus,Instance Segmentation Model,Google Vision OCR,Google Gemini,Llama 3.2 Vision,Distance Measurement,Single-Label Classification Model,Twilio SMS Notification,Anthropic Claude,Image Slicer,Depth Estimation,OpenAI,Multi-Label Classification Model,Template Matching,Classification Label Visualization,PTZ Tracking (ONVIF),Florence-2 Model,MoonshotAI Kimi,MoonshotAI Kimi,Line Counter,Dot Visualization,Background Subtraction,Keypoint Detection Model,Roboflow Dataset Upload,Stability AI Inpainting,Semantic Segmentation Model,Line Counter,Label Visualization,Absolute Static Crop,Google Gemini,VLM As Classifier,Camera Calibration,Halo Visualization,Email Notification,OpenAI,Clip Comparison,Pixel Color Count,LMM For Classification,Text Display,Circle Visualization,Line Counter Visualization,OCR Model,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Triangle Visualization,Perspective Correction - outputs:
BoT-SORT Tracker,Qwen3-VL,Object Detection Model,Multi-Label Classification Model,SAM 3,Crop Visualization,Mask Visualization,Camera Focus,Blur Visualization,Corner Visualization,Ellipse Visualization,Qwen-VL,Roboflow Vision Events,Velocity,Object Detection Model,Heatmap Visualization,Qwen3.5,Trace Visualization,Path Deviation,OC-SORT Tracker,Background Color Visualization,Time in Zone,Webhook Sink,Color Visualization,Byte Tracker,Bounding Box Visualization,Detections Consensus,Model Comparison Visualization,Detection Event Log,Bounding Rectangle,Path Deviation,Byte Tracker,Instance Segmentation Model,Qwen3.5-VL,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,Instance Segmentation Model,GLM-OCR,SAM 3,Distance Measurement,Single-Label Classification Model,SORT Tracker,Semantic Segmentation Model,Detections Stabilizer,SmolVLM2,Dynamic Zone,Model Monitoring Inference Aggregator,Moondream2,Detections Stitch,Time in Zone,Detections List Roll-Up,Multi-Label Classification Model,Segment Anything 2 Model,Instance Segmentation Model,Pixelate Visualization,PTZ Tracking (ONVIF),Florence-2 Model,Time in Zone,Instance Segmentation Model,Line Counter,Dot Visualization,Polygon Visualization,Keypoint Detection Model,Roboflow Dataset Upload,Halo Visualization,Qwen2.5-VL,Per-Class Confidence Filter,Stability AI Inpainting,Roboflow Custom Metadata,Keypoint Detection Model,Line Counter,Semantic Segmentation Model,Detections Classes Replacement,Florence-2 Model,Label Visualization,Icon Visualization,Detection Offset,Overlap Filter,Detections Merge,SAM2 Video Tracker,Single-Label Classification Model,Detections Filter,SAM 3,ByteTrack Tracker,Halo Visualization,Size Measurement,Multi-Label Classification Model,Object Detection Model,Detections Transformation,Mask Area Measurement,Circle Visualization,Byte Tracker,Detections Combine,Keypoint Detection Model,Overlap Analysis,Roboflow Dataset Upload,Mask Edge Snap,Triangle Visualization,Perspective Correction
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Instance Segmentation Model in version v3 has.
Bindings
-
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence_mode(string): How confidence thresholds are determined..custom_confidence(float_zero_to_one): Custom 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 v3
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_instance_segmentation_model@v3",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence_mode": "<block_does_not_provide_example>",
"custom_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"
}
v3¶
Class: RoboflowInstanceSegmentationModelBlockV4 (there are multiple versions of this block)
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.
This version of block introduces breaking change in behaviour of mask construction - it uses
rle format instead polygon making it possible to retrieve
shapes of any kind from remote server.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/roboflow_instance_segmentation_model@v4to 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_mode |
str |
How confidence thresholds are determined.. | ✅ |
custom_confidence |
float |
Custom 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 v3.
- inputs:
Stability AI Outpainting,Multi-Label Classification Model,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Corner Visualization,Ellipse Visualization,Object Detection Model,Roboflow Vision Events,Heatmap Visualization,Trace Visualization,VLM As Classifier,OpenAI,Email Notification,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,JSON Parser,Polygon Zone Visualization,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,QR Code Generator,GLM-OCR,Stitch Images,OpenRouter,Semantic Segmentation Model,Image Blur,Model Monitoring Inference Aggregator,Clip Comparison,Dynamic Zone,Detections List Roll-Up,Instance Segmentation Model,Google Gemini,Buffer,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Roboflow Custom Metadata,Keypoint Detection Model,Florence-2 Model,Local File Sink,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,Grid Visualization,VLM As Detector,Size Measurement,Multi-Label Classification Model,Object Detection Model,LMM,Image Convert Grayscale,Reference Path Visualization,Stitch OCR Detections,Keypoint Detection Model,SIFT Comparison,Identify Changes,Roboflow Dataset Upload,CSV Formatter,S3 Sink,SIFT Comparison,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Identify Outliers,Crop Visualization,Blur Visualization,Mask Visualization,Stability AI Image Generation,Qwen-VL,Stitch OCR Detections,Google Gemma API,Image Slicer,Qwen 3.5 API,Background Color Visualization,Slack Notification,Anthropic Claude,Dimension Collapse,Qwen 3.6 API,Webhook Sink,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Detection Event Log,CogVLM,Llama 3.2 Vision,Instance Segmentation Model,Qwen3.5-VL,Camera Focus,Instance Segmentation Model,Google Vision OCR,Google Gemini,Llama 3.2 Vision,Distance Measurement,Single-Label Classification Model,Twilio SMS Notification,Anthropic Claude,Image Slicer,Depth Estimation,OpenAI,Multi-Label Classification Model,Template Matching,Classification Label Visualization,PTZ Tracking (ONVIF),Florence-2 Model,MoonshotAI Kimi,MoonshotAI Kimi,Line Counter,Dot Visualization,Background Subtraction,Keypoint Detection Model,Roboflow Dataset Upload,Stability AI Inpainting,Semantic Segmentation Model,Line Counter,Label Visualization,Absolute Static Crop,Google Gemini,VLM As Classifier,Camera Calibration,Halo Visualization,Email Notification,OpenAI,Clip Comparison,Pixel Color Count,LMM For Classification,Text Display,Circle Visualization,Line Counter Visualization,OCR Model,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Triangle Visualization,Perspective Correction - outputs:
BoT-SORT Tracker,Qwen3-VL,Object Detection Model,Multi-Label Classification Model,SAM 3,Crop Visualization,Mask Visualization,Camera Focus,Blur Visualization,Corner Visualization,Ellipse Visualization,Qwen-VL,Roboflow Vision Events,Velocity,Object Detection Model,Heatmap Visualization,Qwen3.5,Trace Visualization,Path Deviation,OC-SORT Tracker,Background Color Visualization,Time in Zone,Webhook Sink,Color Visualization,Byte Tracker,Bounding Box Visualization,Detections Consensus,Model Comparison Visualization,Detection Event Log,Bounding Rectangle,Path Deviation,Byte Tracker,Instance Segmentation Model,Qwen3.5-VL,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,Instance Segmentation Model,GLM-OCR,SAM 3,Distance Measurement,Single-Label Classification Model,SORT Tracker,Semantic Segmentation Model,Detections Stabilizer,SmolVLM2,Dynamic Zone,Model Monitoring Inference Aggregator,Moondream2,Detections Stitch,Time in Zone,Detections List Roll-Up,Multi-Label Classification Model,Segment Anything 2 Model,Instance Segmentation Model,Pixelate Visualization,PTZ Tracking (ONVIF),Florence-2 Model,Time in Zone,Instance Segmentation Model,Line Counter,Dot Visualization,Polygon Visualization,Keypoint Detection Model,Roboflow Dataset Upload,Halo Visualization,Qwen2.5-VL,Per-Class Confidence Filter,Stability AI Inpainting,Roboflow Custom Metadata,Keypoint Detection Model,Line Counter,Semantic Segmentation Model,Detections Classes Replacement,Florence-2 Model,Label Visualization,Icon Visualization,Detection Offset,Overlap Filter,Detections Merge,SAM2 Video Tracker,Single-Label Classification Model,Detections Filter,SAM 3,ByteTrack Tracker,Halo Visualization,Size Measurement,Multi-Label Classification Model,Object Detection Model,Detections Transformation,Mask Area Measurement,Circle Visualization,Byte Tracker,Detections Combine,Keypoint Detection Model,Overlap Analysis,Roboflow Dataset Upload,Mask Edge Snap,Triangle Visualization,Perspective Correction
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Instance Segmentation Model in version v3 has.
Bindings
-
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence_mode(string): How confidence thresholds are determined..custom_confidence(float_zero_to_one): Custom 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 v3
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_instance_segmentation_model@v4",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence_mode": "<block_does_not_provide_example>",
"custom_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"
}
v2¶
Class: RoboflowInstanceSegmentationModelBlockV2 (there are multiple versions of this block)
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:
Stability AI Outpainting,Multi-Label Classification Model,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Corner Visualization,Ellipse Visualization,Object Detection Model,Roboflow Vision Events,Heatmap Visualization,Trace Visualization,VLM As Classifier,OpenAI,Email Notification,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,JSON Parser,Polygon Zone Visualization,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,QR Code Generator,GLM-OCR,Stitch Images,OpenRouter,Semantic Segmentation Model,Clip Comparison,Image Blur,Dynamic Zone,Model Monitoring Inference Aggregator,Detections List Roll-Up,Instance Segmentation Model,Buffer,Google Gemini,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Roboflow Custom Metadata,Keypoint Detection Model,Florence-2 Model,Local File Sink,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,Grid Visualization,VLM As Detector,Size Measurement,Multi-Label Classification Model,Object Detection Model,LMM,Image Convert Grayscale,Reference Path Visualization,Stitch OCR Detections,Keypoint Detection Model,SIFT Comparison,Identify Changes,Roboflow Dataset Upload,CSV Formatter,S3 Sink,SIFT Comparison,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Identify Outliers,Crop Visualization,Blur Visualization,Mask Visualization,Stability AI Image Generation,Qwen-VL,Stitch OCR Detections,Google Gemma API,Image Slicer,Qwen 3.5 API,Background Color Visualization,Slack Notification,Anthropic Claude,Dimension Collapse,Qwen 3.6 API,Webhook Sink,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Detection Event Log,Llama 3.2 Vision,CogVLM,Instance Segmentation Model,Qwen3.5-VL,Camera Focus,Instance Segmentation Model,Google Vision OCR,Google Gemini,Llama 3.2 Vision,Distance Measurement,Single-Label Classification Model,Twilio SMS Notification,Anthropic Claude,Image Slicer,Depth Estimation,OpenAI,Multi-Label Classification Model,Template Matching,Classification Label Visualization,PTZ Tracking (ONVIF),Florence-2 Model,MoonshotAI Kimi,MoonshotAI Kimi,Line Counter,Dot Visualization,Background Subtraction,Keypoint Detection Model,Roboflow Dataset Upload,Stability AI Inpainting,Semantic Segmentation Model,Line Counter,Label Visualization,Absolute Static Crop,Google Gemini,VLM As Classifier,Camera Calibration,Halo Visualization,Email Notification,OpenAI,Clip Comparison,Pixel Color Count,LMM For Classification,Text Display,Circle Visualization,Line Counter Visualization,OCR Model,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Triangle Visualization,Perspective Correction - outputs:
BoT-SORT Tracker,Qwen3-VL,Object Detection Model,Multi-Label Classification Model,SAM 3,Crop Visualization,Mask Visualization,Camera Focus,Blur Visualization,Corner Visualization,Ellipse Visualization,Qwen-VL,Roboflow Vision Events,Velocity,Object Detection Model,Heatmap Visualization,Qwen3.5,Trace Visualization,Path Deviation,OC-SORT Tracker,Background Color Visualization,Time in Zone,Webhook Sink,Color Visualization,Byte Tracker,Bounding Box Visualization,Detections Consensus,Model Comparison Visualization,Detection Event Log,Bounding Rectangle,Path Deviation,Byte Tracker,Instance Segmentation Model,Qwen3.5-VL,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,Instance Segmentation Model,GLM-OCR,SAM 3,Distance Measurement,Single-Label Classification Model,SORT Tracker,Semantic Segmentation Model,Detections Stabilizer,SmolVLM2,Dynamic Zone,Model Monitoring Inference Aggregator,Moondream2,Detections Stitch,Time in Zone,Detections List Roll-Up,Multi-Label Classification Model,Segment Anything 2 Model,Instance Segmentation Model,Pixelate Visualization,PTZ Tracking (ONVIF),Florence-2 Model,Time in Zone,Instance Segmentation Model,Line Counter,Dot Visualization,Polygon Visualization,Keypoint Detection Model,Roboflow Dataset Upload,Halo Visualization,Qwen2.5-VL,Per-Class Confidence Filter,Stability AI Inpainting,Roboflow Custom Metadata,Keypoint Detection Model,Line Counter,Semantic Segmentation Model,Detections Classes Replacement,Florence-2 Model,Label Visualization,Icon Visualization,Detection Offset,Overlap Filter,Detections Merge,SAM2 Video Tracker,Single-Label Classification Model,Detections Filter,SAM 3,ByteTrack Tracker,Halo Visualization,Size Measurement,Multi-Label Classification Model,Object Detection Model,Detections Transformation,Mask Area Measurement,Circle Visualization,Byte Tracker,Detections Combine,Keypoint Detection Model,Overlap Analysis,Roboflow Dataset Upload,Mask Edge Snap,Triangle Visualization,Perspective Correction
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)
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:
Stability AI Outpainting,Multi-Label Classification Model,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Corner Visualization,Ellipse Visualization,Object Detection Model,Roboflow Vision Events,Heatmap Visualization,Trace Visualization,VLM As Classifier,OpenAI,Email Notification,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,JSON Parser,Polygon Zone Visualization,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,QR Code Generator,GLM-OCR,Stitch Images,OpenRouter,Semantic Segmentation Model,Clip Comparison,Image Blur,Dynamic Zone,Model Monitoring Inference Aggregator,Detections List Roll-Up,Instance Segmentation Model,Buffer,Google Gemini,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Roboflow Custom Metadata,Keypoint Detection Model,Florence-2 Model,Local File Sink,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,Grid Visualization,VLM As Detector,Size Measurement,Multi-Label Classification Model,Object Detection Model,LMM,Image Convert Grayscale,Reference Path Visualization,Stitch OCR Detections,Keypoint Detection Model,SIFT Comparison,Identify Changes,Roboflow Dataset Upload,CSV Formatter,S3 Sink,SIFT Comparison,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Identify Outliers,Crop Visualization,Blur Visualization,Mask Visualization,Stability AI Image Generation,Qwen-VL,Stitch OCR Detections,Google Gemma API,Image Slicer,Qwen 3.5 API,Background Color Visualization,Slack Notification,Anthropic Claude,Dimension Collapse,Qwen 3.6 API,Webhook Sink,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Detection Event Log,Llama 3.2 Vision,CogVLM,Instance Segmentation Model,Qwen3.5-VL,Camera Focus,Instance Segmentation Model,Google Vision OCR,Google Gemini,Llama 3.2 Vision,Distance Measurement,Single-Label Classification Model,Twilio SMS Notification,Anthropic Claude,Image Slicer,Depth Estimation,OpenAI,Multi-Label Classification Model,Template Matching,Classification Label Visualization,PTZ Tracking (ONVIF),Florence-2 Model,MoonshotAI Kimi,MoonshotAI Kimi,Line Counter,Dot Visualization,Background Subtraction,Keypoint Detection Model,Roboflow Dataset Upload,Stability AI Inpainting,Semantic Segmentation Model,Line Counter,Label Visualization,Absolute Static Crop,Google Gemini,VLM As Classifier,Camera Calibration,Halo Visualization,Email Notification,OpenAI,Clip Comparison,Pixel Color Count,LMM For Classification,Text Display,Circle Visualization,Line Counter Visualization,OCR Model,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Triangle Visualization,Perspective Correction - outputs:
Stability AI Outpainting,CLIP Embedding Model,SAM 3,Camera Focus,Seg Preview,Corner Visualization,Ellipse Visualization,Image Preprocessing,Roboflow Vision Events,Heatmap Visualization,Trace Visualization,OC-SORT Tracker,Time in Zone,OpenAI,Email Notification,Byte Tracker,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,YOLO-World Model,Polygon Zone Visualization,Byte Tracker,Dynamic Crop,Polygon Visualization,QR Code Generator,GLM-OCR,OpenRouter,Model Monitoring Inference Aggregator,Clip Comparison,Image Blur,Dynamic Zone,Cache Get,Detections Stitch,Time in Zone,Detections List Roll-Up,Segment Anything 2 Model,Instance Segmentation Model,Google Gemini,Pixelate Visualization,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Roboflow Custom Metadata,Keypoint Detection Model,Florence-2 Model,Local File Sink,Icon Visualization,Detection Offset,OpenAI,SAM2 Video Tracker,SAM 3,Detections Filter,ByteTrack Tracker,Size Measurement,Multi-Label Classification Model,LMM,Detections Transformation,Reference Path Visualization,Stitch OCR Detections,SIFT Comparison,Roboflow Dataset Upload,S3 Sink,Cache Set,BoT-SORT Tracker,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Mask Visualization,Stability AI Image Generation,Qwen-VL,Crop Visualization,Blur Visualization,Stitch OCR Detections,Google Gemma API,Velocity,Qwen 3.5 API,Path Deviation,Perception Encoder Embedding Model,Background Color Visualization,Slack Notification,Anthropic Claude,Qwen 3.6 API,Webhook Sink,Color Visualization,Bounding Box Visualization,Google Gemma,Detection Event Log,CogVLM,Path Deviation,Llama 3.2 Vision,Bounding Rectangle,Instance Segmentation Model,Instance Segmentation Model,Google Vision OCR,Google Gemini,SAM 3,Distance Measurement,Single-Label Classification Model,Llama 3.2 Vision,SORT Tracker,Twilio SMS Notification,Detections Stabilizer,Moondream2,Anthropic Claude,OpenAI,Depth Estimation,Classification Label Visualization,PTZ Tracking (ONVIF),MoonshotAI Kimi,Florence-2 Model,Time in Zone,MoonshotAI Kimi,Line Counter,Dot Visualization,Roboflow Dataset Upload,Stability AI Inpainting,Per-Class Confidence Filter,Semantic Segmentation Model,Line Counter,Detections Classes Replacement,Label Visualization,Overlap Filter,Detections Merge,Google Gemini,Email Notification,Halo Visualization,OpenAI,Pixel Color Count,LMM For Classification,Text Display,Mask Area Measurement,Circle Visualization,Line Counter Visualization,Byte Tracker,Detections Combine,Overlap Analysis,Twilio SMS/MMS Notification,Morphological Transformation,Mask Edge Snap,Triangle Visualization,Perspective Correction
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"
}