Multi-Label Classification Model

Apply multiple tags to an image.

v3

Run inference on a multi-label classification 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_multi_label_classification_model@v3 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 to determine the confidence threshold..	✅
custom_confidence	float	Custom confidence threshold for predictions..	✅
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.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Multi-Label Classification Model in version v3 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 to determine the confidence threshold..

  • custom_confidence (float_zero_to_one): Custom confidence threshold for predictions..

  • 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

  • predictions (classification_prediction): Predictions from classifier.

  • inference_id (inference_id): Inference identifier.

  • model_id (roboflow_model_id): Roboflow model id.

Example JSON definition

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/roboflow_multi_label_classification_model@v3",
	    "images": "$inputs.image",
	    "model_id": "my_project/3",
	    "confidence_mode": "<block_does_not_provide_example>",
	    "custom_confidence": 0.3,
	    "disable_active_learning": true,
	    "active_learning_target_dataset": "my_project"
	}

v2

Run inference on a multi-label classification 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_multi_label_classification_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	float	Confidence threshold for predictions..	✅
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.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Multi-Label Classification 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 (float_zero_to_one): Confidence threshold for predictions..

  • 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

  • predictions (classification_prediction): Predictions from classifier.

  • inference_id (inference_id): Inference identifier.

  • model_id (roboflow_model_id): Roboflow model id.

Example JSON definition

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/roboflow_multi_label_classification_model@v2",
	    "images": "$inputs.image",
	    "model_id": "my_project/3",
	    "confidence": 0.3,
	    "disable_active_learning": true,
	    "active_learning_target_dataset": "my_project"
	}

v1

Run inference on a multi-label classification 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_multi_label_classification_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..	✅
confidence	float	Confidence threshold for predictions..	✅
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.

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Multi-Label Classification 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..

  • confidence (float_zero_to_one): Confidence threshold for predictions..

  • 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

  • predictions (classification_prediction): Predictions from classifier.

  • inference_id (string): String value.

Example JSON definition

{
	    "name": "<your_step_name_here>",
	    "type": "roboflow_core/roboflow_multi_label_classification_model@v1",
	    "images": "$inputs.image",
	    "model_id": "my_project/3",
	    "confidence": 0.3,
	    "disable_active_learning": true,
	    "active_learning_target_dataset": "my_project"
	}