Semantic Segmentation Model¶
Class: RoboflowSemanticSegmentationModelBlockV1
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:
Dynamic Crop,Circle Visualization,Image Blur,Background Subtraction,Polygon Zone Visualization,SIFT Comparison,Image Contours,Image Preprocessing,Image Convert Grayscale,Grid Visualization,Object Detection Model,Bounding Box Visualization,Multi-Label Classification Model,Camera Focus,Dot Visualization,Camera Focus,SIFT,Depth Estimation,Polygon Visualization,Perspective Correction,Halo Visualization,Camera Calibration,Corner Visualization,Icon Visualization,Image Slicer,Semantic Segmentation Model,Line Counter Visualization,Heatmap Visualization,Morphological Transformation,Stability AI Image Generation,Model Comparison Visualization,Blur Visualization,Keypoint Visualization,QR Code Generator,Halo Visualization,Keypoint Detection Model,Background Color Visualization,Absolute Static Crop,Label Visualization,Image Slicer,Polygon Visualization,Pixelate Visualization,Single-Label Classification Model,Contrast Equalization,Triangle Visualization,Stability AI Outpainting,Stability AI Inpainting,Ellipse Visualization,Mask Visualization,Crop Visualization,Trace Visualization,Color Visualization,Instance Segmentation Model,Stitch Images,Text Display,Relative Static Crop,Reference Path Visualization,Image Threshold,Classification Label Visualization - outputs:
Webhook Sink,Qwen2.5-VL,Keypoint Detection Model,Instance Segmentation Model,Object Detection Model,Single-Label Classification Model,Multi-Label Classification Model,Multi-Label Classification Model,Model Monitoring Inference Aggregator,Single-Label Classification Model,Keypoint Detection Model,Qwen3-VL,SmolVLM2,SAM 3,SAM 3,Mask Visualization,Instance Segmentation Model,SAM 3,Moondream2,Qwen3.5-VL,Object Detection Model,Semantic Segmentation 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"
}