Multi-Label Classification Model

v2

Class: RoboflowMultiLabelClassificationModelBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.multi_label_classification.v2.RoboflowMultiLabelClassificationModelBlockV2

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 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@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	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.

Available Connections

Compatible Blocks

Check what blocks you can connect to Multi-Label Classification Model in version v2.

• inputs: Reference Path Visualization, Identify Outliers, Blur Visualization, Pixelate Visualization, Email Notification, Classification Label Visualization, Instance Segmentation Model, Background Color Visualization, VLM as Detector, Dynamic Crop, Keypoint Visualization, Camera Focus, Mask Visualization, Single-Label Classification Model, Clip Comparison, Image Slicer, Webhook Sink, Twilio SMS Notification, Absolute Static Crop, Model Monitoring Inference Aggregator, Stability AI Image Generation, Image Blur, Roboflow Dataset Upload, Identify Changes, Circle Visualization, Grid Visualization, Crop Visualization, Image Convert Grayscale, Image Threshold, Trace Visualization, VLM as Detector, JSON Parser, SIFT Comparison, Multi-Label Classification Model, Object Detection Model, Polygon Visualization, Triangle Visualization, Stability AI Inpainting, Keypoint Detection Model, Detections Consensus, Halo Visualization, Dot Visualization, Polygon Zone Visualization, Local File Sink, Roboflow Custom Metadata, VLM as Classifier, Camera Calibration, VLM as Classifier, Slack Notification, SIFT, Corner Visualization, Image Contours, Model Comparison Visualization, Stitch Images, Bounding Box Visualization, Line Counter Visualization, Image Slicer, Roboflow Dataset Upload, Perspective Correction, Image Preprocessing, SIFT Comparison, Label Visualization, Relative Static Crop, Color Visualization, Ellipse Visualization
• outputs: Multi-Label Classification Model, Keypoint Detection Model, Single-Label Classification Model, Classification Label Visualization, Instance Segmentation Model, Webhook Sink, Instance Segmentation Model, Single-Label Classification Model, Roboflow Custom Metadata, Detections Classes Replacement, Model Monitoring Inference Aggregator, Qwen2.5-VL, Object Detection Model, Roboflow Dataset Upload, Roboflow Dataset Upload, Keypoint Detection Model, SmolVLM2, Moondream2, Object Detection Model, Multi-Label Classification Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Multi-Label Classification Model in version v2 has.

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 of step Multi-Label Classification Model in version v2

{
    "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

Class: RoboflowMultiLabelClassificationModelBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.multi_label_classification.v1.RoboflowMultiLabelClassificationModelBlockV1

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 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@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..	✅
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.

Available Connections

Compatible Blocks

Check what blocks you can connect to Multi-Label Classification Model in version v1.

• inputs: Reference Path Visualization, Identify Outliers, Blur Visualization, Pixelate Visualization, Email Notification, Classification Label Visualization, Instance Segmentation Model, Background Color Visualization, VLM as Detector, Dynamic Crop, Keypoint Visualization, Camera Focus, Mask Visualization, Single-Label Classification Model, Clip Comparison, Image Slicer, Webhook Sink, Twilio SMS Notification, Absolute Static Crop, Model Monitoring Inference Aggregator, Stability AI Image Generation, Image Blur, Roboflow Dataset Upload, Identify Changes, Circle Visualization, Grid Visualization, Crop Visualization, Image Convert Grayscale, Image Threshold, Trace Visualization, VLM as Detector, JSON Parser, SIFT Comparison, Multi-Label Classification Model, Object Detection Model, Polygon Visualization, Triangle Visualization, Stability AI Inpainting, Keypoint Detection Model, Detections Consensus, Halo Visualization, Dot Visualization, Polygon Zone Visualization, Local File Sink, Roboflow Custom Metadata, VLM as Classifier, Camera Calibration, VLM as Classifier, Slack Notification, SIFT, Corner Visualization, Image Contours, Model Comparison Visualization, Stitch Images, Bounding Box Visualization, Line Counter Visualization, Image Slicer, Roboflow Dataset Upload, Perspective Correction, Image Preprocessing, SIFT Comparison, Label Visualization, Relative Static Crop, Color Visualization, Ellipse Visualization
• outputs: Classification Label Visualization, Webhook Sink, Background Color Visualization, Dynamic Crop, Cache Get, Mask Visualization, Clip Comparison, Twilio SMS Notification, Google Vision OCR, Segment Anything 2 Model, Model Monitoring Inference Aggregator, Stability AI Image Generation, LMM For Classification, Florence-2 Model, Image Blur, Cache Set, Roboflow Dataset Upload, CogVLM, Line Counter, Circle Visualization, Crop Visualization, Path Deviation, OpenAI, Detections Stitch, OpenAI, Pixel Color Count, Path Deviation, Line Counter, Time in Zone, Model Comparison Visualization, Bounding Box Visualization, Perspective Correction, SIFT Comparison, Slack Notification, Color Visualization, Ellipse Visualization, Reference Path Visualization, Anthropic Claude, Email Notification, LMM, Llama 3.2 Vision, Instance Segmentation Model, Keypoint Visualization, Time in Zone, Florence-2 Model, YOLO-World Model, Trace Visualization, Image Threshold, Triangle Visualization, Polygon Visualization, Stability AI Inpainting, Halo Visualization, Dot Visualization, Google Gemini, Polygon Zone Visualization, CLIP Embedding Model, Local File Sink, Size Measurement, Instance Segmentation Model, Roboflow Custom Metadata, Detections Classes Replacement, Corner Visualization, Roboflow Dataset Upload, Line Counter Visualization, Image Preprocessing, Label Visualization, Distance Measurement

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Multi-Label Classification Model in version v1 has.

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 of step Multi-Label Classification Model in version v1

{
    "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"
}