Semantic Segmentation Model¶
v2¶
Class: RoboflowSemanticSegmentationModelBlockV2 (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 a semantic segmentation model hosted on or uploaded to Roboflow.
Semantic segmentation assigns a class label to every pixel in the image, producing a dense segmentation mask rather than per-object bounding boxes or instance masks.
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_semantic_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_mode |
str |
How confidence thresholds are determined.. | ✅ |
custom_confidence |
float |
Custom confidence threshold for predictions.. | ✅ |
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 Semantic Segmentation Model in version v2.
- inputs:
Stability AI Outpainting,OpenAI-Compatible LLM,Morphological Transformation,Object Detection Model,Multi-Label Classification Model,Contrast Enhancement,Identify Outliers,Crop Visualization,Camera Focus,Blur Visualization,Image Preprocessing,Corner Visualization,Ellipse Visualization,Mask Visualization,Stability AI Image Generation,Qwen-VL,Object Detection Model,Heatmap Visualization,Stitch OCR Detections,Google Gemma API,Roboflow Vision Events,Image Slicer,Qwen 3.5 API,Trace Visualization,Background Color Visualization,Slack Notification,Anthropic Claude,Qwen 3.6 API,OpenAI,Webhook Sink,Email Notification,Color Visualization,Bounding Box Visualization,Keypoint Visualization,Detections Consensus,Model Comparison Visualization,Google Gemma,Relative Static Crop,CogVLM,Llama 3.2 Vision,Polygon Zone Visualization,Instance Segmentation Model,Qwen3.5-VL,Single-Label Classification Model,Dynamic Crop,Camera Focus,Polygon Visualization,QR Code Generator,Instance Segmentation Model,Google Vision OCR,Stitch Images,GLM-OCR,Google Gemini,Llama 3.2 Vision,OpenRouter,Single-Label Classification Model,Twilio SMS Notification,Semantic Segmentation Model,Image Blur,Model Monitoring Inference Aggregator,Clip Comparison,Anthropic Claude,Image Slicer,Depth Estimation,OpenAI,Multi-Label Classification Model,Instance Segmentation Model,Google Gemini,Classification Label Visualization,Pixelate Visualization,EasyOCR,SIFT,Florence-2 Model,MoonshotAI Kimi,Contrast Equalization,Image Threshold,Instance Segmentation Model,MoonshotAI Kimi,Dot Visualization,Polygon Visualization,Background Subtraction,Keypoint Detection Model,Roboflow Dataset Upload,Anthropic Claude,Halo Visualization,Stability AI Inpainting,Roboflow Custom Metadata,Semantic Segmentation Model,Keypoint Detection Model,Florence-2 Model,Label Visualization,Local File Sink,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,Absolute Static Crop,Google Gemini,Grid Visualization,VLM As Classifier,Camera Calibration,Halo Visualization,Email Notification,OpenAI,Multi-Label Classification Model,Object Detection Model,LMM,LMM For Classification,Text Display,Image Convert Grayscale,Reference Path Visualization,Circle Visualization,Line Counter Visualization,Stitch OCR Detections,OCR Model,Keypoint Detection Model,SIFT Comparison,VLM As Detector,Identify Changes,Morphological Transformation,Twilio SMS/MMS Notification,Roboflow Dataset Upload,CSV Formatter,S3 Sink,Triangle Visualization,Perspective Correction - outputs:
Multi-Label Classification Model,Qwen3-VL,Instance Segmentation Model,Object Detection Model,Multi-Label Classification Model,SAM 3,Moondream2,Mask Visualization,Qwen-VL,Instance Segmentation Model,Object Detection Model,Keypoint Detection Model,Qwen3.5,Qwen2.5-VL,Keypoint Detection Model,Semantic Segmentation Model,Webhook Sink,Single-Label Classification Model,SAM2 Video Tracker,SAM 3,Instance Segmentation Model,Multi-Label Classification Model,Qwen3.5-VL,Object Detection Model,Single-Label Classification Model,Instance Segmentation Model,GLM-OCR,Keypoint Detection Model,SAM 3,Single-Label Classification Model,Semantic Segmentation Model,Model Monitoring Inference Aggregator,SmolVLM2
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Semantic Segmentation Model in version v2 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..
-
output
inference_id(inference_id): Inference identifier.predictions(semantic_segmentation_prediction): Prediction with per-pixel class label and confidence for semantic segmentation.model_id(roboflow_model_id): Roboflow model id.
Example JSON definition of step Semantic Segmentation Model in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_semantic_segmentation_model@v2",
"images": "$inputs.image",
"model_id": "my_project/3",
"confidence_mode": "<block_does_not_provide_example>",
"custom_confidence": 0.3
}
v1¶
Class: RoboflowSemanticSegmentationModelBlockV1 (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 a semantic segmentation model hosted on or uploaded to Roboflow.
Semantic segmentation assigns a class label to every pixel in the image, producing a dense segmentation mask rather than per-object bounding boxes or instance masks.
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_semantic_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.. | ✅ |
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 Semantic Segmentation Model in version v1.
- inputs:
Stability AI Outpainting,Instance Segmentation Model,Classification Label Visualization,Morphological Transformation,Object Detection Model,Pixelate Visualization,Multi-Label Classification Model,SIFT,Contrast Enhancement,Contrast Equalization,Image Threshold,Crop Visualization,Camera Focus,Blur Visualization,Image Preprocessing,Corner Visualization,Ellipse Visualization,Mask Visualization,Stability AI Image Generation,Dot Visualization,Heatmap Visualization,Background Subtraction,Polygon Visualization,Keypoint Detection Model,Image Slicer,Halo Visualization,Trace Visualization,Background Color Visualization,Stability AI Inpainting,Triangle Visualization,Semantic Segmentation Model,Keypoint Detection Model,Label Visualization,Icon Visualization,Image Contours,Color Visualization,Absolute Static Crop,Bounding Box Visualization,Keypoint Visualization,Image Slicer,Grid Visualization,Model Comparison Visualization,Relative Static Crop,Camera Calibration,Halo Visualization,Polygon Zone Visualization,Instance Segmentation Model,Multi-Label Classification Model,Object Detection Model,Single-Label Classification Model,Text Display,Image Convert Grayscale,Reference Path Visualization,Dynamic Crop,Camera Focus,Circle Visualization,Line Counter Visualization,Polygon Visualization,QR Code Generator,Stitch Images,Instance Segmentation Model,SIFT Comparison,Single-Label Classification Model,Morphological Transformation,Semantic Segmentation Model,Image Blur,Depth Estimation,Perspective Correction - outputs:
Multi-Label Classification Model,Qwen3-VL,Instance Segmentation Model,Object Detection Model,Multi-Label Classification Model,SAM 3,Moondream2,Mask Visualization,Qwen-VL,Instance Segmentation Model,Object Detection Model,Keypoint Detection Model,Qwen3.5,Qwen2.5-VL,Keypoint Detection Model,Semantic Segmentation Model,Webhook Sink,Single-Label Classification Model,SAM2 Video Tracker,SAM 3,Instance Segmentation Model,Multi-Label Classification Model,Qwen3.5-VL,Object Detection Model,Single-Label Classification Model,Instance Segmentation Model,GLM-OCR,Keypoint Detection Model,SAM 3,Single-Label Classification Model,Semantic Segmentation Model,Model Monitoring Inference Aggregator,SmolVLM2
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Semantic Segmentation Model in version v1 has.
Bindings
-
input
images(image): The image to infer on..model_id(roboflow_model_id): Roboflow model identifier..
-
output
inference_id(inference_id): Inference identifier.predictions(semantic_segmentation_prediction): Prediction with per-pixel class label and confidence for semantic segmentation.model_id(roboflow_model_id): Roboflow model id.
Example JSON definition of step Semantic Segmentation Model in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/roboflow_semantic_segmentation_model@v1",
"images": "$inputs.image",
"model_id": "my_project/3"
}