Instance Segmentation Model¶
v4¶
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 v4.
- inputs:
VLM As Classifier,Line Counter,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Image Stack,Anthropic Claude,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,LMM For Classification,Single-Label Classification Model,Perspective Correction,Corner Visualization,Clip Comparison,Roboflow Custom Metadata,Halo Visualization,Dynamic Zone,Keypoint Detection Model,Qwen-VL,JSON Parser,Email Notification,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Model Monitoring Inference Aggregator,Relative Static Crop,OpenRouter,OpenAI,PLC ModbusTCP,Florence-2 Model,VLM As Detector,OpenAI,Heatmap Visualization,OCR Model,Motion Detection,Blur Visualization,Dimension Collapse,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,Google Gemini,Clip Comparison,Google Gemini,PLC EthernetIP,Background Subtraction,Keypoint Visualization,CSV Formatter,Buffer,Webhook Sink,Stitch Images,Florence-2 Model,Current Time,Detections List Roll-Up,Contrast Equalization,OpenAI,VLM As Detector,Line Counter,Google Gemini,Triangle Visualization,Slack Notification,SIFT,Multi-Label Classification Model,Local File Sink,Image Contours,Keypoint Detection Model,VLM As Classifier,Pixel Color Count,GLM-OCR,Roboflow Asset Library Attributes,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Google Gemma API,Semantic Segmentation Model,Stitch OCR Detections,Image Threshold,Line Counter Visualization,Semantic Segmentation Model,Distance Measurement,Multi-Label Classification Model,Camera Calibration,QR Code Generator,Detection Event Log,S3 Sink,Microsoft SQL Server Sink,Google Vision OCR,Twilio SMS Notification,Image Blur,Morphological Transformation,Camera Focus,Roboflow Vision Events,Size Measurement,Stability AI Inpainting,PTZ Tracking (ONVIF),Classification Label Visualization,Stitch OCR Detections,Event Writer,Grid Visualization,Qwen3.5-VL,Mask Visualization,Llama 3.2 Vision,Reference Path Visualization,Image Slicer,Label Visualization,Identify Outliers,SIFT Comparison,OPC UA Writer Sink,Dot Visualization,Identify Changes,Dynamic Crop,Circle Visualization,Llama 3.2 Vision,Camera Focus,OpenAI-Compatible LLM,MoonshotAI Kimi,Single-Label Classification Model,CogVLM,Object Detection Model,Qwen 3.6 API,Detections Consensus,Bounding Box Visualization,Multi-Label Classification Model,LMM,OpenAI,PLC Reader,Image Convert Grayscale,Instance Segmentation Model,Roboflow Visual Search,EasyOCR,Roboflow Dataset Upload,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Instance Segmentation Model,Roboflow Dataset Upload,PLC Writer,Qwen 3.5 API,Anthropic Claude,Object Detection Model,MQTT Writer,Polygon Visualization,Model Comparison Visualization,Single-Label Classification Model - outputs:
Line Counter,Time in Zone,Semantic Segmentation Model,Trace Visualization,Path Deviation,Distance Measurement,Semantic Segmentation Model,Qwen2.5-VL,Multi-Label Classification Model,Detection Offset,ByteTrack Tracker,Detection Event Log,Per-Class Confidence Filter,Icon Visualization,Detections Transformation,Color Visualization,SmolVLM2,Single-Label Classification Model,Perspective Correction,Corner Visualization,Mask Area Measurement,Roboflow Custom Metadata,Detections Merge,Halo Visualization,Dynamic Zone,Detections Combine,Qwen-VL,Keypoint Detection Model,Roboflow Vision Events,Size Measurement,Halo Visualization,Stability AI Inpainting,PTZ Tracking (ONVIF),Object Detection Model,Bounding Rectangle,SAM2 Video Tracker,Event Writer,Qwen3.5-VL,Background Color Visualization,Mask Visualization,Byte Tracker,Ellipse Visualization,Velocity,Label Visualization,Byte Tracker,Dot Visualization,Polygon Visualization,Crop Visualization,Dynamic Crop,Path Deviation,Circle Visualization,Detections Stitch,BoT-SORT Tracker,Model Monitoring Inference Aggregator,Camera Focus,SAM3 Video Tracker,Segment Anything 2 Model,Florence-2 Model,Heatmap Visualization,Single-Label Classification Model,Detections Filter,Overlap Analysis,Qwen3.5,Blur Visualization,Object Detection Model,Instance Segmentation Model,SAM 3 Interactive,Detections Consensus,Webhook Sink,Byte Tracker,Bounding Box Visualization,Multi-Label Classification Model,SAM 3,Florence-2 Model,Instance Segmentation Model,Detections List Roll-Up,Mask Edge Snap,Qwen3-VL,Moondream2,Line Counter,Triangle Visualization,Overlap Filter,Roboflow Dataset Upload,SAM 3,Time in Zone,Detections Classes Replacement,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Roboflow Dataset Upload,SORT Tracker,Detections Stabilizer,Instance Segmentation Model,Track Class Lock,Multi-Label Classification Model,Object Detection Model,Time in Zone,Keypoint Detection Model,Polygon Visualization,GLM-OCR,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Single-Label Classification Model
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Instance Segmentation Model in version v4 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(Union[rle_instance_segmentation_prediction,instance_segmentation_prediction]): Prediction with detected bounding boxes and RLE-encoded segmentation masks in form of sv.Detections(...) object ifrle_instance_segmentation_predictionor Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_prediction.model_id(roboflow_model_id): Roboflow model id.
Example JSON definition of step Instance Segmentation Model in version v4
{
"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"
}
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.. | ✅ |
enforce_dense_masks_in_inference_models |
bool |
Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform.. | ✅ |
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:
VLM As Classifier,Line Counter,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Image Stack,Anthropic Claude,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,LMM For Classification,Single-Label Classification Model,Perspective Correction,Corner Visualization,Clip Comparison,Roboflow Custom Metadata,Halo Visualization,Dynamic Zone,Keypoint Detection Model,Qwen-VL,JSON Parser,Email Notification,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Model Monitoring Inference Aggregator,Relative Static Crop,OpenRouter,OpenAI,PLC ModbusTCP,Florence-2 Model,VLM As Detector,OpenAI,Heatmap Visualization,OCR Model,Motion Detection,Blur Visualization,Dimension Collapse,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,Google Gemini,Clip Comparison,Google Gemini,PLC EthernetIP,Background Subtraction,Keypoint Visualization,CSV Formatter,Buffer,Webhook Sink,Stitch Images,Florence-2 Model,Current Time,Detections List Roll-Up,Contrast Equalization,OpenAI,VLM As Detector,Line Counter,Google Gemini,Triangle Visualization,Slack Notification,SIFT,Multi-Label Classification Model,Local File Sink,Image Contours,Keypoint Detection Model,VLM As Classifier,Pixel Color Count,GLM-OCR,Roboflow Asset Library Attributes,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Google Gemma API,Semantic Segmentation Model,Stitch OCR Detections,Image Threshold,Line Counter Visualization,Semantic Segmentation Model,Distance Measurement,Multi-Label Classification Model,Camera Calibration,QR Code Generator,Detection Event Log,S3 Sink,Microsoft SQL Server Sink,Google Vision OCR,Twilio SMS Notification,Image Blur,Morphological Transformation,Camera Focus,Roboflow Vision Events,Size Measurement,Stability AI Inpainting,PTZ Tracking (ONVIF),Classification Label Visualization,Stitch OCR Detections,Event Writer,Grid Visualization,Qwen3.5-VL,Mask Visualization,Llama 3.2 Vision,Reference Path Visualization,Image Slicer,Label Visualization,Identify Outliers,SIFT Comparison,OPC UA Writer Sink,Dot Visualization,Identify Changes,Dynamic Crop,Circle Visualization,Llama 3.2 Vision,Camera Focus,OpenAI-Compatible LLM,MoonshotAI Kimi,Single-Label Classification Model,CogVLM,Object Detection Model,Qwen 3.6 API,Detections Consensus,Bounding Box Visualization,Multi-Label Classification Model,LMM,OpenAI,PLC Reader,Image Convert Grayscale,Instance Segmentation Model,Roboflow Visual Search,EasyOCR,Roboflow Dataset Upload,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Instance Segmentation Model,Roboflow Dataset Upload,PLC Writer,Qwen 3.5 API,Anthropic Claude,Object Detection Model,MQTT Writer,Polygon Visualization,Model Comparison Visualization,Single-Label Classification Model - outputs:
Line Counter,Time in Zone,Semantic Segmentation Model,Path Deviation,Trace Visualization,Distance Measurement,Semantic Segmentation Model,Qwen2.5-VL,Multi-Label Classification Model,Detection Offset,ByteTrack Tracker,Detection Event Log,Per-Class Confidence Filter,Icon Visualization,Detections Transformation,Color Visualization,SmolVLM2,Single-Label Classification Model,Perspective Correction,Corner Visualization,Mask Area Measurement,Roboflow Custom Metadata,Detections Merge,Halo Visualization,Dynamic Zone,Detections Combine,Qwen-VL,Keypoint Detection Model,Roboflow Vision Events,Size Measurement,Halo Visualization,Stability AI Inpainting,PTZ Tracking (ONVIF),Object Detection Model,Bounding Rectangle,SAM2 Video Tracker,Event Writer,Qwen3.5-VL,Mask Visualization,Byte Tracker,Background Color Visualization,Ellipse Visualization,Velocity,Label Visualization,Byte Tracker,Dot Visualization,Polygon Visualization,Crop Visualization,Dynamic Crop,Path Deviation,Circle Visualization,Detections Stitch,BoT-SORT Tracker,Model Monitoring Inference Aggregator,Camera Focus,SAM3 Video Tracker,Segment Anything 2 Model,Florence-2 Model,Heatmap Visualization,Single-Label Classification Model,Detections Filter,Overlap Analysis,Qwen3.5,Blur Visualization,Object Detection Model,Instance Segmentation Model,SAM 3 Interactive,Detections Consensus,Webhook Sink,Byte Tracker,Bounding Box Visualization,Multi-Label Classification Model,SAM 3,Florence-2 Model,Instance Segmentation Model,Detections List Roll-Up,Mask Edge Snap,Qwen3-VL,Moondream2,Line Counter,Triangle Visualization,Overlap Filter,Roboflow Dataset Upload,SAM 3,Time in Zone,Detections Classes Replacement,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Roboflow Dataset Upload,Detections Stabilizer,SORT Tracker,Instance Segmentation Model,Track Class Lock,Multi-Label Classification Model,Object Detection Model,Time in Zone,Keypoint Detection Model,Polygon Visualization,GLM-OCR,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Single-Label Classification Model
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..enforce_dense_masks_in_inference_models(boolean): Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
-
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",
"enforce_dense_masks_in_inference_models": true
}
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.. | ✅ |
enforce_dense_masks_in_inference_models |
bool |
Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform.. | ✅ |
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:
VLM As Classifier,Line Counter,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Image Stack,Anthropic Claude,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,LMM For Classification,Single-Label Classification Model,Perspective Correction,Corner Visualization,Clip Comparison,Roboflow Custom Metadata,Halo Visualization,Dynamic Zone,Keypoint Detection Model,Qwen-VL,JSON Parser,Email Notification,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Model Monitoring Inference Aggregator,Relative Static Crop,OpenRouter,OpenAI,PLC ModbusTCP,Florence-2 Model,VLM As Detector,OpenAI,Heatmap Visualization,Motion Detection,OCR Model,Blur Visualization,Dimension Collapse,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,Google Gemini,Clip Comparison,Google Gemini,PLC EthernetIP,Background Subtraction,Keypoint Visualization,Buffer,CSV Formatter,Webhook Sink,Stitch Images,Florence-2 Model,Current Time,Detections List Roll-Up,Contrast Equalization,OpenAI,Line Counter,VLM As Detector,Google Gemini,Triangle Visualization,Slack Notification,SIFT,Multi-Label Classification Model,Local File Sink,Image Contours,VLM As Classifier,Keypoint Detection Model,Pixel Color Count,GLM-OCR,Roboflow Asset Library Attributes,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Google Gemma API,Semantic Segmentation Model,Stitch OCR Detections,Image Threshold,Line Counter Visualization,Semantic Segmentation Model,Distance Measurement,Multi-Label Classification Model,Camera Calibration,QR Code Generator,Detection Event Log,S3 Sink,Microsoft SQL Server Sink,Twilio SMS Notification,Google Vision OCR,Image Blur,Morphological Transformation,Camera Focus,Size Measurement,Roboflow Vision Events,Stability AI Inpainting,PTZ Tracking (ONVIF),Classification Label Visualization,Stitch OCR Detections,Event Writer,Grid Visualization,Qwen3.5-VL,Mask Visualization,Llama 3.2 Vision,Reference Path Visualization,Image Slicer,Label Visualization,Identify Outliers,SIFT Comparison,OPC UA Writer Sink,Dot Visualization,Identify Changes,Dynamic Crop,Circle Visualization,Llama 3.2 Vision,Camera Focus,MoonshotAI Kimi,OpenAI-Compatible LLM,Single-Label Classification Model,CogVLM,Object Detection Model,Qwen 3.6 API,Detections Consensus,Bounding Box Visualization,Multi-Label Classification Model,LMM,OpenAI,PLC Reader,Image Convert Grayscale,Instance Segmentation Model,Roboflow Visual Search,EasyOCR,Roboflow Dataset Upload,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Instance Segmentation Model,Roboflow Dataset Upload,PLC Writer,Qwen 3.5 API,Anthropic Claude,Object Detection Model,MQTT Writer,Polygon Visualization,Model Comparison Visualization,Single-Label Classification Model - outputs:
Line Counter,Time in Zone,Semantic Segmentation Model,Path Deviation,Trace Visualization,Distance Measurement,Semantic Segmentation Model,Qwen2.5-VL,Multi-Label Classification Model,Detection Offset,ByteTrack Tracker,Detection Event Log,Per-Class Confidence Filter,Icon Visualization,Detections Transformation,Color Visualization,SmolVLM2,Single-Label Classification Model,Perspective Correction,Corner Visualization,Mask Area Measurement,Roboflow Custom Metadata,Detections Merge,Halo Visualization,Dynamic Zone,Detections Combine,Qwen-VL,Keypoint Detection Model,Roboflow Vision Events,Size Measurement,Halo Visualization,Stability AI Inpainting,PTZ Tracking (ONVIF),Object Detection Model,Bounding Rectangle,SAM2 Video Tracker,Event Writer,Qwen3.5-VL,Mask Visualization,Byte Tracker,Background Color Visualization,Ellipse Visualization,Velocity,Label Visualization,Byte Tracker,Dot Visualization,Polygon Visualization,Crop Visualization,Dynamic Crop,Path Deviation,Circle Visualization,Detections Stitch,BoT-SORT Tracker,Model Monitoring Inference Aggregator,Camera Focus,SAM3 Video Tracker,Segment Anything 2 Model,Florence-2 Model,Heatmap Visualization,Single-Label Classification Model,Detections Filter,Overlap Analysis,Qwen3.5,Blur Visualization,Object Detection Model,Instance Segmentation Model,SAM 3 Interactive,Detections Consensus,Webhook Sink,Byte Tracker,Bounding Box Visualization,Multi-Label Classification Model,SAM 3,Florence-2 Model,Instance Segmentation Model,Detections List Roll-Up,Mask Edge Snap,Qwen3-VL,Moondream2,Line Counter,Triangle Visualization,Overlap Filter,Roboflow Dataset Upload,SAM 3,Time in Zone,Detections Classes Replacement,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Roboflow Dataset Upload,Detections Stabilizer,SORT Tracker,Instance Segmentation Model,Track Class Lock,Multi-Label Classification Model,Object Detection Model,Time in Zone,Keypoint Detection Model,Polygon Visualization,GLM-OCR,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Single-Label Classification 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..enforce_dense_masks_in_inference_models(boolean): Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
-
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",
"enforce_dense_masks_in_inference_models": true
}
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.. | ✅ |
enforce_dense_masks_in_inference_models |
bool |
Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform.. | ✅ |
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:
VLM As Classifier,Line Counter,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Image Stack,Anthropic Claude,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,LMM For Classification,Single-Label Classification Model,Perspective Correction,Corner Visualization,Clip Comparison,Roboflow Custom Metadata,Halo Visualization,Dynamic Zone,Keypoint Detection Model,Qwen-VL,JSON Parser,Email Notification,Halo Visualization,Object Detection Model,Google Gemma,Background Color Visualization,Ellipse Visualization,Email Notification,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Absolute Static Crop,Image Preprocessing,Template Matching,Model Monitoring Inference Aggregator,Relative Static Crop,OpenRouter,OpenAI,PLC ModbusTCP,Florence-2 Model,VLM As Detector,OpenAI,Heatmap Visualization,Motion Detection,OCR Model,Blur Visualization,Dimension Collapse,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,Google Gemini,Clip Comparison,Google Gemini,PLC EthernetIP,Background Subtraction,Keypoint Visualization,Buffer,CSV Formatter,Webhook Sink,Stitch Images,Florence-2 Model,Current Time,Detections List Roll-Up,Contrast Equalization,OpenAI,Line Counter,VLM As Detector,Google Gemini,Triangle Visualization,Slack Notification,SIFT,Multi-Label Classification Model,Local File Sink,Image Contours,VLM As Classifier,Keypoint Detection Model,Pixel Color Count,GLM-OCR,Roboflow Asset Library Attributes,Image Slicer,Polygon Zone Visualization,Contrast Enhancement,Google Gemma API,Semantic Segmentation Model,Stitch OCR Detections,Image Threshold,Line Counter Visualization,Semantic Segmentation Model,Distance Measurement,Multi-Label Classification Model,Camera Calibration,QR Code Generator,Detection Event Log,S3 Sink,Microsoft SQL Server Sink,Twilio SMS Notification,Google Vision OCR,Image Blur,Morphological Transformation,Camera Focus,Size Measurement,Roboflow Vision Events,Stability AI Inpainting,PTZ Tracking (ONVIF),Classification Label Visualization,Stitch OCR Detections,Event Writer,Grid Visualization,Qwen3.5-VL,Mask Visualization,Llama 3.2 Vision,Reference Path Visualization,Image Slicer,Label Visualization,Identify Outliers,SIFT Comparison,OPC UA Writer Sink,Dot Visualization,Identify Changes,Dynamic Crop,Circle Visualization,Llama 3.2 Vision,Camera Focus,MoonshotAI Kimi,OpenAI-Compatible LLM,Single-Label Classification Model,CogVLM,Object Detection Model,Qwen 3.6 API,Detections Consensus,Bounding Box Visualization,Multi-Label Classification Model,LMM,OpenAI,PLC Reader,Image Convert Grayscale,Instance Segmentation Model,Roboflow Visual Search,EasyOCR,Roboflow Dataset Upload,Instance Segmentation Model,Pixelate Visualization,Keypoint Detection Model,Instance Segmentation Model,Roboflow Dataset Upload,PLC Writer,Qwen 3.5 API,Anthropic Claude,Object Detection Model,MQTT Writer,Polygon Visualization,Model Comparison Visualization,Single-Label Classification Model - outputs:
Line Counter,MoonshotAI Kimi,Stability AI Image Generation,Trace Visualization,Path Deviation,Anthropic Claude,Per-Class Confidence Filter,Icon Visualization,SIFT Comparison,Morphological Transformation,Color Visualization,LMM For Classification,Perspective Correction,Corner Visualization,Roboflow Custom Metadata,Detections Merge,Halo Visualization,Dynamic Zone,Qwen-VL,Keypoint Detection Model,Email Notification,Halo Visualization,Google Gemma,Background Color Visualization,Email Notification,Ellipse Visualization,Twilio SMS/MMS Notification,Text Display,Polygon Visualization,Crop Visualization,Image Preprocessing,Model Monitoring Inference Aggregator,OpenRouter,OpenAI,Florence-2 Model,OpenAI,Heatmap Visualization,Detections Filter,Perception Encoder Embedding Model,Blur Visualization,Depth Estimation,Instance Segmentation Model,Stability AI Outpainting,Anthropic Claude,YOLO-World Model,Google Gemini,Clip Comparison,Google Gemini,Keypoint Visualization,Webhook Sink,Byte Tracker,Florence-2 Model,Current Time,Detections List Roll-Up,Contrast Equalization,Mask Edge Snap,OpenAI,Moondream2,Line Counter,Google Gemini,Triangle Visualization,Slack Notification,Overlap Filter,Time in Zone,CLIP Embedding Model,Detections Stabilizer,Multi-Label Classification Model,Local File Sink,Pixel Color Count,GLM-OCR,Roboflow Asset Library Attributes,Polygon Zone Visualization,Google Gemma API,Time in Zone,Stitch OCR Detections,Line Counter Visualization,Semantic Segmentation Model,Distance Measurement,Image Threshold,QR Code Generator,Detection Offset,ByteTrack Tracker,Detection Event Log,Detections Transformation,S3 Sink,Microsoft SQL Server Sink,Mask Area Measurement,Twilio SMS Notification,Google Vision OCR,Image Blur,Detections Combine,Morphological Transformation,Roboflow Vision Events,Size Measurement,PTZ Tracking (ONVIF),Stability AI Inpainting,Classification Label Visualization,Stitch OCR Detections,Bounding Rectangle,SAM2 Video Tracker,Event Writer,Qwen3.5-VL,Llama 3.2 Vision,Mask Visualization,Byte Tracker,Reference Path Visualization,Velocity,Label Visualization,Byte Tracker,OPC UA Writer Sink,Dot Visualization,Cache Set,Dynamic Crop,Detections Stitch,Circle Visualization,Llama 3.2 Vision,Path Deviation,SAM3 Video Tracker,BoT-SORT Tracker,Camera Focus,Segment Anything 2 Model,OpenAI-Compatible LLM,MoonshotAI Kimi,Overlap Analysis,CogVLM,Object Detection Model,SAM 3 Interactive,Qwen 3.6 API,Detections Consensus,Bounding Box Visualization,LMM,OpenAI,SAM 3,Instance Segmentation Model,Roboflow Visual Search,Roboflow Dataset Upload,SAM 3,Cache Get,Instance Segmentation Model,Detections Classes Replacement,Pixelate Visualization,Instance Segmentation Model,Roboflow Dataset Upload,SORT Tracker,Track Class Lock,Qwen 3.5 API,Anthropic Claude,Time in Zone,MQTT Writer,Polygon Visualization,OC-SORT Tracker,SAM 3,Model Comparison Visualization,Single-Label Classification Model,Seg Preview
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..enforce_dense_masks_in_inference_models(boolean): Boolean flag to enforce dense masks when inference models backend is in use (irrelevant in other cases). Dense masks are faster to process, but require more memory. Users can't tweak this flag when running on Roboflow serverless platform..
-
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",
"enforce_dense_masks_in_inference_models": true
}