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:
Image Preprocessing,Single-Label Classification Model,Anthropic Claude,Image Slicer,Dynamic Crop,Bounding Box Visualization,Object Detection Model,Absolute Static Crop,SIFT Comparison,Stitch Images,Stitch OCR Detections,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Distance Measurement,Frame Delay,EasyOCR,Llama 3.2 Vision,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Auto Rotate on Edges,Keypoint Detection Model,Stability AI Outpainting,Model Comparison Visualization,Slack Notification,Line Counter Visualization,Camera Calibration,Single-Label Classification Model,Clip Comparison,PLC Reader,VLM As Detector,CogVLM,Camera Focus,Corner Visualization,Ellipse Visualization,PP-OCR,Morphological Transformation,Detections List Roll-Up,Anthropic Claude,Roboflow Visual Search,Color Visualization,Instance Segmentation Model,OpenAI,Triangle Visualization,Detection Event Log,Object Detection Model,Image Contours,Image Threshold,Current Time,Roboflow Visual Search Classifier,QR Code Generator,OpenAI-Compatible LLM,Florence-2 Model,Semantic Segmentation Model,Polygon Zone Visualization,Stitch OCR Detections,Roboflow Asset Library Attributes,GeoTag Detection,Microsoft SQL Server Sink,VLM As Classifier,Camera Focus,Image Convert Grayscale,Label Visualization,Llama 3.2 Vision,Stability AI Image Generation,VLM As Detector,Instance Segmentation Model,Line Counter,MQTT Writer,Roboflow Dataset Upload,Local File Sink,Identify Outliers,VLM As Classifier,Semantic Segmentation Model,Google Gemini,Event Writer,Depth Estimation,Google Gemini,OpenAI,Trace Visualization,Twilio SMS Notification,Pixel Color Count,PLC EthernetIP,LMM For Classification,Object Detection Model,Webhook Sink,Halo Visualization,Buffer,Mask Visualization,Template Matching,Pixelate Visualization,Twilio SMS/MMS Notification,MoonshotAI Kimi,Dot Visualization,Multi-Label Classification Model,Image Stack,OPC UA Writer Sink,Google Gemini,Keypoint Visualization,Dimension Collapse,LMM,Image Slicer,PTZ Tracking (ONVIF),OCR Model,Circle Visualization,Contrast Enhancement,Relative Static Crop,Morphological Transformation,Email Notification,Halo Visualization,Clip Comparison,Cosmos 3,Polygon Visualization,Qwen-VL,PLC Writer,Google Gemma,Crop Visualization,Qwen 3.5 API,Model Monitoring Inference Aggregator,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Motion Detection,Multi-Label Classification Model,Google Gemma API,Instance Segmentation Model,Detections Consensus,CSV Formatter,Image Blur,Background Color Visualization,Grid Visualization,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Perspective Correction,Background Subtraction,Polygon Visualization,Contrast Equalization,SIFT,Qwen 3.6 API,Text Display,JSON Parser,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,Identify Changes,Keypoint Detection Model,OpenAI,SIFT Comparison,PLC ModbusTCP,Multi-Label Classification Model,S3 Sink,Line Counter,Roboflow Dataset Upload - outputs:
Roboflow Dataset Upload,SAM 3,Trace Visualization,Single-Label Classification Model,SAM 3 Interactive,Time in Zone,Dynamic Crop,Detections Filter,Mask Area Measurement,BoT-SORT Tracker,Object Detection Model,Bounding Box Visualization,Mask Edge Snap,Velocity,Object Detection Model,Webhook Sink,Halo Visualization,Mask Visualization,Path Deviation,Pixelate Visualization,Dot Visualization,Multi-Label Classification Model,Instance Segmentation Model,Stability AI Inpainting,Frame Delay,Distance Measurement,OC-SORT Tracker,Track Class Lock,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Detections Combine,Detections Transformation,PTZ Tracking (ONVIF),Time in Zone,Byte Tracker,Keypoint Detection Model,Circle Visualization,Model Comparison Visualization,Detections Classes Replacement,Byte Tracker,SAM2 Video Tracker,Single-Label Classification Model,ByteTrack Tracker,Per-Class Confidence Filter,Halo Visualization,Cosmos 3,Polygon Visualization,SORT Tracker,Ellipse Visualization,Corner Visualization,Camera Focus,Qwen-VL,Crop Visualization,Model Monitoring Inference Aggregator,Qwen3-VL,Detections List Roll-Up,Qwen3.5,Keypoint Detection Model,Size Measurement,Heatmap Visualization,Icon Visualization,Single-Label Classification Model,Color Visualization,Instance Segmentation Model,Multi-Label Classification Model,Detections Consensus,Instance Segmentation Model,Overlap Analysis,Triangle Visualization,Time in Zone,Background Color Visualization,Detection Event Log,Detections Stitch,Segment Anything 2 Model,Detections Stabilizer,SAM3 Video Tracker,Object Detection Model,SAM 3,Blur Visualization,SAM 3,GLM-OCR,Detections Merge,Qwen2.5-VL,Florence-2 Model,Semantic Segmentation Model,SmolVLM2,Path Deviation,GeoTag Detection,Moondream2,Perspective Correction,Polygon Visualization,Label Visualization,Detection Offset,Instance Segmentation Model,Line Counter,Roboflow Dataset Upload,Qwen3.5-VL,Roboflow Vision Events,Keypoint Detection Model,Bounding Rectangle,Multi-Label Classification Model,Overlap Filter,Event Writer,Semantic Segmentation Model,Line Counter,Byte Tracker
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:
Image Preprocessing,Single-Label Classification Model,Anthropic Claude,Image Slicer,Dynamic Crop,Bounding Box Visualization,Object Detection Model,Absolute Static Crop,SIFT Comparison,Stitch Images,Stitch OCR Detections,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Distance Measurement,Frame Delay,EasyOCR,Llama 3.2 Vision,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Auto Rotate on Edges,Keypoint Detection Model,Stability AI Outpainting,Model Comparison Visualization,Slack Notification,Line Counter Visualization,Camera Calibration,Single-Label Classification Model,Clip Comparison,PLC Reader,VLM As Detector,CogVLM,Camera Focus,Corner Visualization,Ellipse Visualization,PP-OCR,Morphological Transformation,Detections List Roll-Up,Anthropic Claude,Roboflow Visual Search,Color Visualization,Instance Segmentation Model,OpenAI,Triangle Visualization,Detection Event Log,Object Detection Model,Image Contours,Image Threshold,Current Time,Roboflow Visual Search Classifier,QR Code Generator,OpenAI-Compatible LLM,Florence-2 Model,Semantic Segmentation Model,Polygon Zone Visualization,Stitch OCR Detections,Roboflow Asset Library Attributes,GeoTag Detection,Microsoft SQL Server Sink,VLM As Classifier,Camera Focus,Image Convert Grayscale,Label Visualization,Llama 3.2 Vision,Stability AI Image Generation,VLM As Detector,Instance Segmentation Model,Line Counter,MQTT Writer,Roboflow Dataset Upload,Local File Sink,Identify Outliers,VLM As Classifier,Semantic Segmentation Model,Google Gemini,Event Writer,Depth Estimation,Google Gemini,OpenAI,Trace Visualization,Twilio SMS Notification,Pixel Color Count,PLC EthernetIP,LMM For Classification,Object Detection Model,Webhook Sink,Halo Visualization,Buffer,Mask Visualization,Template Matching,Pixelate Visualization,Twilio SMS/MMS Notification,MoonshotAI Kimi,Dot Visualization,Multi-Label Classification Model,Image Stack,OPC UA Writer Sink,Google Gemini,Keypoint Visualization,Dimension Collapse,LMM,Image Slicer,PTZ Tracking (ONVIF),OCR Model,Circle Visualization,Contrast Enhancement,Relative Static Crop,Morphological Transformation,Email Notification,Halo Visualization,Clip Comparison,Cosmos 3,Polygon Visualization,Qwen-VL,PLC Writer,Google Gemma,Crop Visualization,Qwen 3.5 API,Model Monitoring Inference Aggregator,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Motion Detection,Multi-Label Classification Model,Google Gemma API,Instance Segmentation Model,Detections Consensus,CSV Formatter,Image Blur,Background Color Visualization,Grid Visualization,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Perspective Correction,Background Subtraction,Polygon Visualization,Contrast Equalization,SIFT,Qwen 3.6 API,Text Display,JSON Parser,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,Identify Changes,Keypoint Detection Model,OpenAI,SIFT Comparison,PLC ModbusTCP,Multi-Label Classification Model,S3 Sink,Line Counter,Roboflow Dataset Upload - outputs:
Roboflow Dataset Upload,SAM 3,Trace Visualization,Single-Label Classification Model,SAM 3 Interactive,Time in Zone,Dynamic Crop,Detections Filter,Mask Area Measurement,BoT-SORT Tracker,Object Detection Model,Bounding Box Visualization,Mask Edge Snap,Velocity,Object Detection Model,Webhook Sink,Halo Visualization,Mask Visualization,Path Deviation,Pixelate Visualization,Dot Visualization,Multi-Label Classification Model,Instance Segmentation Model,Stability AI Inpainting,Frame Delay,Distance Measurement,OC-SORT Tracker,Track Class Lock,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Detections Combine,Detections Transformation,PTZ Tracking (ONVIF),Time in Zone,Byte Tracker,Keypoint Detection Model,Circle Visualization,Model Comparison Visualization,Detections Classes Replacement,Byte Tracker,SAM2 Video Tracker,Single-Label Classification Model,ByteTrack Tracker,Per-Class Confidence Filter,Halo Visualization,Cosmos 3,Polygon Visualization,SORT Tracker,Ellipse Visualization,Corner Visualization,Camera Focus,Qwen-VL,Crop Visualization,Model Monitoring Inference Aggregator,Qwen3-VL,Detections List Roll-Up,Qwen3.5,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Color Visualization,Instance Segmentation Model,Multi-Label Classification Model,Detections Consensus,Instance Segmentation Model,Overlap Analysis,Time in Zone,Detection Event Log,Detections Stitch,Background Color Visualization,Segment Anything 2 Model,Triangle Visualization,Detections Stabilizer,SAM3 Video Tracker,Object Detection Model,SAM 3,Blur Visualization,SAM 3,GLM-OCR,Detections Merge,Qwen2.5-VL,Florence-2 Model,Semantic Segmentation Model,SmolVLM2,Path Deviation,GeoTag Detection,Moondream2,Perspective Correction,Polygon Visualization,Label Visualization,Detection Offset,Instance Segmentation Model,Line Counter,Roboflow Dataset Upload,Qwen3.5-VL,Roboflow Vision Events,Keypoint Detection Model,Bounding Rectangle,Multi-Label Classification Model,Overlap Filter,Event Writer,Semantic Segmentation Model,Line Counter,Byte Tracker
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:
Image Preprocessing,Single-Label Classification Model,Anthropic Claude,Image Slicer,Dynamic Crop,Bounding Box Visualization,Object Detection Model,Absolute Static Crop,SIFT Comparison,Stitch Images,Stitch OCR Detections,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Distance Measurement,Frame Delay,EasyOCR,Llama 3.2 Vision,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Auto Rotate on Edges,Keypoint Detection Model,Stability AI Outpainting,Model Comparison Visualization,Slack Notification,Line Counter Visualization,Camera Calibration,PLC Reader,Clip Comparison,Single-Label Classification Model,VLM As Detector,CogVLM,Camera Focus,Corner Visualization,Ellipse Visualization,PP-OCR,Morphological Transformation,Detections List Roll-Up,Anthropic Claude,Roboflow Visual Search,Color Visualization,Instance Segmentation Model,OpenAI,Triangle Visualization,Detection Event Log,Object Detection Model,Image Contours,Image Threshold,Current Time,Roboflow Visual Search Classifier,QR Code Generator,OpenAI-Compatible LLM,Florence-2 Model,Semantic Segmentation Model,Polygon Zone Visualization,Stitch OCR Detections,Roboflow Asset Library Attributes,GeoTag Detection,Microsoft SQL Server Sink,VLM As Classifier,Camera Focus,Image Convert Grayscale,Label Visualization,Llama 3.2 Vision,Stability AI Image Generation,VLM As Detector,Instance Segmentation Model,Line Counter,MQTT Writer,Roboflow Dataset Upload,Local File Sink,Identify Outliers,VLM As Classifier,Semantic Segmentation Model,Google Gemini,Event Writer,Depth Estimation,Google Gemini,OpenAI,Trace Visualization,Twilio SMS Notification,Pixel Color Count,PLC EthernetIP,LMM For Classification,Object Detection Model,Webhook Sink,Halo Visualization,Buffer,Mask Visualization,Template Matching,Pixelate Visualization,Twilio SMS/MMS Notification,MoonshotAI Kimi,Dot Visualization,Image Stack,Multi-Label Classification Model,OPC UA Writer Sink,Google Gemini,Keypoint Visualization,Dimension Collapse,LMM,Image Slicer,PTZ Tracking (ONVIF),OCR Model,Circle Visualization,Contrast Enhancement,Relative Static Crop,Morphological Transformation,Email Notification,Halo Visualization,Clip Comparison,Cosmos 3,Polygon Visualization,Qwen-VL,PLC Writer,Google Gemma,Crop Visualization,Qwen 3.5 API,Model Monitoring Inference Aggregator,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Motion Detection,Multi-Label Classification Model,Google Gemma API,Instance Segmentation Model,Detections Consensus,CSV Formatter,Image Blur,Background Color Visualization,Grid Visualization,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Perspective Correction,Background Subtraction,Polygon Visualization,Contrast Equalization,SIFT,Qwen 3.6 API,Text Display,JSON Parser,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,Identify Changes,Keypoint Detection Model,SIFT Comparison,OpenAI,PLC ModbusTCP,Multi-Label Classification Model,S3 Sink,Line Counter,Roboflow Dataset Upload - outputs:
Roboflow Dataset Upload,SAM 3,Trace Visualization,Single-Label Classification Model,SAM 3 Interactive,Time in Zone,Dynamic Crop,Detections Filter,Mask Area Measurement,BoT-SORT Tracker,Object Detection Model,Bounding Box Visualization,Mask Edge Snap,Velocity,Object Detection Model,Webhook Sink,Halo Visualization,Mask Visualization,Path Deviation,Pixelate Visualization,Dot Visualization,Multi-Label Classification Model,Instance Segmentation Model,Stability AI Inpainting,Frame Delay,Distance Measurement,OC-SORT Tracker,Track Class Lock,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Detections Combine,Detections Transformation,PTZ Tracking (ONVIF),Time in Zone,Byte Tracker,Keypoint Detection Model,Circle Visualization,Model Comparison Visualization,Detections Classes Replacement,Byte Tracker,SAM2 Video Tracker,Single-Label Classification Model,ByteTrack Tracker,Per-Class Confidence Filter,Halo Visualization,Cosmos 3,Polygon Visualization,SORT Tracker,Ellipse Visualization,Corner Visualization,Camera Focus,Qwen-VL,Crop Visualization,Model Monitoring Inference Aggregator,Qwen3-VL,Detections List Roll-Up,Qwen3.5,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Color Visualization,Instance Segmentation Model,Multi-Label Classification Model,Detections Consensus,Instance Segmentation Model,Overlap Analysis,Time in Zone,Detection Event Log,Detections Stitch,Background Color Visualization,Segment Anything 2 Model,Triangle Visualization,Detections Stabilizer,SAM3 Video Tracker,Object Detection Model,SAM 3,Blur Visualization,SAM 3,GLM-OCR,Detections Merge,Qwen2.5-VL,Florence-2 Model,Semantic Segmentation Model,SmolVLM2,Path Deviation,GeoTag Detection,Moondream2,Perspective Correction,Polygon Visualization,Label Visualization,Detection Offset,Instance Segmentation Model,Line Counter,Roboflow Dataset Upload,Qwen3.5-VL,Roboflow Vision Events,Keypoint Detection Model,Bounding Rectangle,Multi-Label Classification Model,Overlap Filter,Event Writer,Semantic Segmentation Model,Line Counter,Byte Tracker
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Instance Segmentation Model in version v2 has.
Bindings
-
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..confidence(float_zero_to_one): Confidence threshold for predictions..class_filter(list_of_values): List of accepted classes. Classes must exist in the model's training set..iou_threshold(float_zero_to_one): Minimum overlap threshold between boxes to combine them into a single detection, used in NMS. Learn more..max_detections(integer): Maximum number of detections to return..class_agnostic_nms(boolean): Boolean flag to specify if NMS is to be used in class-agnostic mode..max_candidates(integer): Maximum number of candidates as NMS input to be taken into account..mask_decode_mode(string): Parameter of mask decoding in prediction post-processing..tradeoff_factor(float_zero_to_one): Post-processing parameter to dictate tradeoff between fast and accurate..disable_active_learning(boolean): Boolean flag to disable project-level active learning for this block..active_learning_target_dataset(roboflow_project): Target dataset for active learning, if enabled..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:
Image Preprocessing,Single-Label Classification Model,Anthropic Claude,Image Slicer,Dynamic Crop,Bounding Box Visualization,Object Detection Model,Absolute Static Crop,SIFT Comparison,Stitch Images,Stitch OCR Detections,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Distance Measurement,Frame Delay,EasyOCR,Llama 3.2 Vision,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,Auto Rotate on Edges,Keypoint Detection Model,Stability AI Outpainting,Model Comparison Visualization,Slack Notification,Line Counter Visualization,Camera Calibration,PLC Reader,Clip Comparison,Single-Label Classification Model,VLM As Detector,CogVLM,Camera Focus,Corner Visualization,Ellipse Visualization,PP-OCR,Morphological Transformation,Detections List Roll-Up,Anthropic Claude,Roboflow Visual Search,Color Visualization,Instance Segmentation Model,OpenAI,Triangle Visualization,Detection Event Log,Object Detection Model,Image Contours,Image Threshold,Current Time,Roboflow Visual Search Classifier,QR Code Generator,OpenAI-Compatible LLM,Florence-2 Model,Semantic Segmentation Model,Polygon Zone Visualization,Stitch OCR Detections,Roboflow Asset Library Attributes,GeoTag Detection,Microsoft SQL Server Sink,VLM As Classifier,Camera Focus,Image Convert Grayscale,Label Visualization,Llama 3.2 Vision,Stability AI Image Generation,VLM As Detector,Instance Segmentation Model,Line Counter,MQTT Writer,Roboflow Dataset Upload,Local File Sink,Identify Outliers,VLM As Classifier,Semantic Segmentation Model,Google Gemini,Event Writer,Depth Estimation,Google Gemini,OpenAI,Trace Visualization,Twilio SMS Notification,Pixel Color Count,PLC EthernetIP,LMM For Classification,Object Detection Model,Webhook Sink,Halo Visualization,Buffer,Mask Visualization,Template Matching,Pixelate Visualization,Twilio SMS/MMS Notification,MoonshotAI Kimi,Dot Visualization,Image Stack,Multi-Label Classification Model,OPC UA Writer Sink,Google Gemini,Keypoint Visualization,Dimension Collapse,LMM,Image Slicer,PTZ Tracking (ONVIF),OCR Model,Circle Visualization,Contrast Enhancement,Relative Static Crop,Morphological Transformation,Email Notification,Halo Visualization,Clip Comparison,Cosmos 3,Polygon Visualization,Qwen-VL,PLC Writer,Google Gemma,Crop Visualization,Qwen 3.5 API,Model Monitoring Inference Aggregator,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Motion Detection,Multi-Label Classification Model,Google Gemma API,Instance Segmentation Model,Detections Consensus,CSV Formatter,Image Blur,Background Color Visualization,Grid Visualization,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Perspective Correction,Background Subtraction,Polygon Visualization,Contrast Equalization,SIFT,Qwen 3.6 API,Text Display,JSON Parser,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,Identify Changes,Keypoint Detection Model,SIFT Comparison,OpenAI,PLC ModbusTCP,Multi-Label Classification Model,S3 Sink,Line Counter,Roboflow Dataset Upload - outputs:
SAM 3,Image Preprocessing,Anthropic Claude,Time in Zone,CLIP Embedding Model,Dynamic Crop,Mask Area Measurement,BoT-SORT Tracker,Bounding Box Visualization,Cache Get,Mask Edge Snap,Path Deviation,SIFT Comparison,Stitch OCR Detections,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Distance Measurement,Frame Delay,Llama 3.2 Vision,Track Class Lock,Florence-2 Model,Roboflow Custom Metadata,Dynamic Zone,YOLO-World Model,Auto Rotate on Edges,Detections Transformation,Byte Tracker,Stability AI Outpainting,Model Comparison Visualization,Slack Notification,Detections Classes Replacement,Line Counter Visualization,Byte Tracker,Clip Comparison,CogVLM,SORT Tracker,Corner Visualization,Ellipse Visualization,Camera Focus,Morphological Transformation,Detections List Roll-Up,Anthropic Claude,Roboflow Visual Search,Color Visualization,Instance Segmentation Model,OpenAI,Triangle Visualization,Time in Zone,Detection Event Log,Detections Stabilizer,Object Detection Model,SAM 3,Image Threshold,SAM 3,Detections Merge,Current Time,Roboflow Visual Search Classifier,QR Code Generator,OpenAI-Compatible LLM,Florence-2 Model,Semantic Segmentation Model,Polygon Zone Visualization,Stitch OCR Detections,Path Deviation,Roboflow Asset Library Attributes,GeoTag Detection,Microsoft SQL Server Sink,Moondream2,Label Visualization,Llama 3.2 Vision,Stability AI Image Generation,Detection Offset,Instance Segmentation Model,Perception Encoder Embedding Model,Line Counter,MQTT Writer,Roboflow Dataset Upload,Local File Sink,Cache Set,Bounding Rectangle,Event Writer,Google Gemini,Depth Estimation,Seg Preview,Byte Tracker,Google Gemini,OpenAI,Trace Visualization,Twilio SMS Notification,Pixel Color Count,SAM 3 Interactive,Detections Filter,LMM For Classification,Velocity,Webhook Sink,Halo Visualization,Mask Visualization,Twilio SMS/MMS Notification,Pixelate Visualization,MoonshotAI Kimi,Dot Visualization,OPC UA Writer Sink,Google Gemini,OC-SORT Tracker,Keypoint Visualization,LMM,Detections Combine,PTZ Tracking (ONVIF),Time in Zone,Circle Visualization,SAM2 Video Tracker,ByteTrack Tracker,Morphological Transformation,Per-Class Confidence Filter,Email Notification,Halo Visualization,Cosmos 3,Polygon Visualization,Qwen-VL,Google Gemma,Crop Visualization,Qwen 3.5 API,Model Monitoring Inference Aggregator,Size Measurement,Icon Visualization,Heatmap Visualization,Single-Label Classification Model,Google Gemma API,Instance Segmentation Model,Detections Consensus,Overlap Analysis,Segment Anything 2 Model,Image Blur,Background Color Visualization,SAM3 Video Tracker,Detections Stitch,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Perspective Correction,Polygon Visualization,Contrast Equalization,Qwen 3.6 API,Text Display,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,Keypoint Detection Model,OpenAI,Multi-Label Classification Model,Overlap Filter,S3 Sink,Line Counter,Roboflow Dataset Upload
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
}