Semantic Segmentation Model

Assign a class label to every pixel in the image.

v2

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@v2 to add the block as a 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.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Semantic Segmentation Model in version v2 has.

Input and output 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

{
	    "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

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@v1 to add the block as a 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.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Semantic Segmentation Model in version v1 has.

Input and output 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

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/roboflow_semantic_segmentation_model@v1",
	    "images": "$inputs.image",
	    "model_id": "my_project/3"
	}