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:
Object Detection Model,Perspective Correction,S3 Sink,Stability AI Inpainting,Image Convert Grayscale,Keypoint Detection Model,Morphological Transformation,Email Notification,Object Detection Model,Clip Comparison,VLM As Detector,QR Code Generator,Qwen-VL,Twilio SMS/MMS Notification,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,MoonshotAI Kimi,Polygon Zone Visualization,Image Threshold,Stitch OCR Detections,Anthropic Claude,OpenAI-Compatible LLM,OpenAI,Dynamic Crop,Detections Consensus,Heatmap Visualization,Keypoint Visualization,Email Notification,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Google Vision OCR,Camera Focus,Label Visualization,Instance Segmentation Model,Identify Outliers,Contrast Enhancement,Bounding Box Visualization,Local File Sink,Depth Estimation,Multi-Label Classification Model,Google Gemini,Keypoint Detection Model,Image Contours,EasyOCR,Relative Static Crop,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Background Color Visualization,Qwen 3.6 API,Single-Label Classification Model,Instance Segmentation Model,Qwen 3.5 API,Image Blur,Polygon Visualization,Google Gemini,SIFT Comparison,Grid Visualization,Anthropic Claude,Florence-2 Model,Triangle Visualization,Object Detection Model,Single-Label Classification Model,OCR Model,Roboflow Custom Metadata,OpenAI,Slack Notification,VLM As Classifier,Pixelate Visualization,Stitch Images,Single-Label Classification Model,Instance Segmentation Model,OpenAI,Image Slicer,LMM For Classification,Keypoint Detection Model,Image Preprocessing,SIFT,Line Counter Visualization,Roboflow Dataset Upload,Image Slicer,Semantic Segmentation Model,Corner Visualization,Stability AI Outpainting,Halo Visualization,Multi-Label Classification Model,LMM,Roboflow Dataset Upload,Qwen3.5-VL,Color Visualization,Semantic Segmentation Model,Blur Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Morphological Transformation,Trace Visualization,Stitch OCR Detections,Reference Path Visualization,Halo Visualization,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,Identify Changes,Mask Visualization,GLM-OCR,Crop Visualization,Background Subtraction,Circle Visualization,CogVLM,Text Display,Absolute Static Crop,CSV Formatter,Florence-2 Model,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Icon Visualization,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
Object Detection Model,Single-Label Classification Model,SAM 3,Keypoint Detection Model,Qwen2.5-VL,SAM 3,Object Detection Model,Qwen-VL,Single-Label Classification Model,Instance Segmentation Model,Model Monitoring Inference Aggregator,Keypoint Detection Model,Moondream2,Semantic Segmentation Model,Multi-Label Classification Model,Qwen3-VL,Qwen3.5-VL,Semantic Segmentation Model,SAM 3,Instance Segmentation Model,Qwen3.5,Multi-Label Classification Model,SmolVLM2,Keypoint Detection Model,Mask Visualization,GLM-OCR,Multi-Label Classification Model,Single-Label Classification Model,Instance Segmentation Model,SAM2 Video Tracker,Webhook Sink,Object Detection Model
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:
Single-Label Classification Model,Perspective Correction,Object Detection Model,Stability AI Inpainting,Image Convert Grayscale,Keypoint Detection Model,Morphological Transformation,Object Detection Model,Pixelate Visualization,Stitch Images,QR Code Generator,Instance Segmentation Model,Image Slicer,Image Preprocessing,SIFT,Line Counter Visualization,Polygon Zone Visualization,Image Threshold,Image Slicer,Semantic Segmentation Model,Corner Visualization,Dynamic Crop,Stability AI Outpainting,Halo Visualization,Heatmap Visualization,Keypoint Visualization,Color Visualization,Semantic Segmentation Model,Blur Visualization,Stability AI Image Generation,Camera Focus,Label Visualization,Classification Label Visualization,Camera Focus,Camera Calibration,Morphological Transformation,Trace Visualization,Contrast Enhancement,Bounding Box Visualization,Reference Path Visualization,Depth Estimation,Halo Visualization,Multi-Label Classification Model,Ellipse Visualization,Model Comparison Visualization,Dot Visualization,SIFT Comparison,Image Contours,Mask Visualization,Keypoint Detection Model,Relative Static Crop,Crop Visualization,Background Subtraction,Circle Visualization,Multi-Label Classification Model,Text Display,Polygon Visualization,Background Color Visualization,Absolute Static Crop,Single-Label Classification Model,Instance Segmentation Model,Image Blur,Polygon Visualization,Contrast Equalization,Grid Visualization,Icon Visualization,Triangle Visualization - outputs:
Object Detection Model,Single-Label Classification Model,SAM 3,Keypoint Detection Model,Qwen2.5-VL,SAM 3,Object Detection Model,Qwen-VL,Single-Label Classification Model,Instance Segmentation Model,Model Monitoring Inference Aggregator,Keypoint Detection Model,Moondream2,Semantic Segmentation Model,Multi-Label Classification Model,Qwen3-VL,Qwen3.5-VL,Semantic Segmentation Model,SAM 3,Instance Segmentation Model,Qwen3.5,Multi-Label Classification Model,SmolVLM2,Keypoint Detection Model,Mask Visualization,GLM-OCR,Multi-Label Classification Model,Single-Label Classification Model,Instance Segmentation Model,SAM2 Video Tracker,Webhook Sink,Object Detection Model
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"
}