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: Instance Segmentation Model, VLM As Detector, Ellipse Visualization, Twilio SMS Notification, VLM As Classifier, Depth Estimation, Camera Focus, Email Notification, PTZ Tracking (ONVIF).md), Morphological Transformation, Motion Detection, Classification Label Visualization, Polygon Visualization, SIFT, Triangle Visualization, Roboflow Dataset Upload, Corner Visualization, Dot Visualization, Model Comparison Visualization, Object Detection Model, JSON Parser, Slack Notification, Contrast Equalization, Reference Path Visualization, Mask Visualization, Local File Sink, Stability AI Outpainting, Circle Visualization, Halo Visualization, Single-Label Classification Model, Dynamic Crop, Email Notification, Perspective Correction, Grid Visualization, Polygon Visualization, Roboflow Dataset Upload, Identify Outliers, Text Display, VLM As Detector, Heatmap Visualization, Background Subtraction, Line Counter Visualization, Polygon Zone Visualization, SIFT Comparison, SIFT Comparison, QR Code Generator, Image Contours, Roboflow Custom Metadata, Absolute Static Crop, Label Visualization, Stability AI Image Generation, Dynamic Zone, Twilio SMS/MMS Notification, Multi-Label Classification Model, Image Threshold, VLM As Classifier, Identify Changes, Bounding Box Visualization, Blur Visualization, Image Preprocessing, Camera Calibration, Model Monitoring Inference Aggregator, Relative Static Crop, Image Slicer, Icon Visualization, Clip Comparison, Keypoint Detection Model, Pixelate Visualization, Halo Visualization, Background Color Visualization, Image Blur, Webhook Sink, Keypoint Visualization, Image Convert Grayscale, Stitch Images, Camera Focus, Stability AI Inpainting, Color Visualization, Trace Visualization, Crop Visualization, Detections Consensus, Image Slicer
• outputs: Object Detection Model, Roboflow Dataset Upload, SAM 3, Model Monitoring Inference Aggregator, Instance Segmentation Model, Multi-Label Classification Model, Object Detection Model, Qwen3-VL, Keypoint Detection Model, Instance Segmentation Model, SmolVLM2, Single-Label Classification Model, Qwen2.5-VL, Multi-Label Classification Model, SAM 3, Roboflow Custom Metadata, SAM 3, Webhook Sink, Single-Label Classification Model, Detections Classes Replacement, Keypoint Detection Model, Classification Label Visualization, Moondream2, Roboflow Dataset Upload

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: Instance Segmentation Model, VLM As Detector, Ellipse Visualization, Twilio SMS Notification, VLM As Classifier, Depth Estimation, Camera Focus, Email Notification, PTZ Tracking (ONVIF).md), Morphological Transformation, Motion Detection, Classification Label Visualization, Polygon Visualization, SIFT, Triangle Visualization, Roboflow Dataset Upload, Corner Visualization, Dot Visualization, Model Comparison Visualization, Object Detection Model, JSON Parser, Slack Notification, Contrast Equalization, Reference Path Visualization, Mask Visualization, Local File Sink, Stability AI Outpainting, Circle Visualization, Halo Visualization, Single-Label Classification Model, Dynamic Crop, Email Notification, Perspective Correction, Grid Visualization, Polygon Visualization, Roboflow Dataset Upload, Identify Outliers, Text Display, VLM As Detector, Heatmap Visualization, Background Subtraction, Line Counter Visualization, Polygon Zone Visualization, SIFT Comparison, SIFT Comparison, QR Code Generator, Image Contours, Roboflow Custom Metadata, Absolute Static Crop, Label Visualization, Stability AI Image Generation, Dynamic Zone, Twilio SMS/MMS Notification, Multi-Label Classification Model, Image Threshold, VLM As Classifier, Identify Changes, Bounding Box Visualization, Blur Visualization, Image Preprocessing, Camera Calibration, Model Monitoring Inference Aggregator, Relative Static Crop, Image Slicer, Icon Visualization, Clip Comparison, Keypoint Detection Model, Pixelate Visualization, Halo Visualization, Background Color Visualization, Image Blur, Webhook Sink, Keypoint Visualization, Image Convert Grayscale, Stitch Images, Camera Focus, Stability AI Inpainting, Color Visualization, Trace Visualization, Crop Visualization, Detections Consensus, Image Slicer
• outputs: Stitch OCR Detections, OpenAI, Google Gemini, SAM 3, Instance Segmentation Model, Ellipse Visualization, Distance Measurement, Twilio SMS Notification, Depth Estimation, Time in Zone, Email Notification, SAM 3, PTZ Tracking (ONVIF).md), Detections Classes Replacement, Morphological Transformation, Classification Label Visualization, Moondream2, Polygon Visualization, Size Measurement, Triangle Visualization, Roboflow Dataset Upload, Corner Visualization, Dot Visualization, Time in Zone, Model Comparison Visualization, Slack Notification, Contrast Equalization, LMM For Classification, Reference Path Visualization, Local File Sink, Mask Visualization, Google Vision OCR, Stability AI Outpainting, Halo Visualization, SAM 3, Circle Visualization, Dynamic Crop, Email Notification, Perspective Correction, Line Counter, Polygon Visualization, OpenAI, YOLO-World Model, Florence-2 Model, Roboflow Dataset Upload, Text Display, CogVLM, Anthropic Claude, Heatmap Visualization, OpenAI, Line Counter Visualization, Polygon Zone Visualization, SIFT Comparison, Segment Anything 2 Model, QR Code Generator, Perception Encoder Embedding Model, Roboflow Custom Metadata, Cache Get, Google Gemini, Label Visualization, Detections Stitch, Stitch OCR Detections, OpenAI, Stability AI Image Generation, Cache Set, Twilio SMS/MMS Notification, Image Threshold, Google Gemini, Pixel Color Count, Anthropic Claude, Crop Visualization, Bounding Box Visualization, Image Preprocessing, Llama 3.2 Vision, Model Monitoring Inference Aggregator, Anthropic Claude, Icon Visualization, Clip Comparison, LMM, Instance Segmentation Model, CLIP Embedding Model, Halo Visualization, Background Color Visualization, Image Blur, Webhook Sink, Florence-2 Model, Path Deviation, Line Counter, Keypoint Visualization, Time in Zone, Seg Preview, Stability AI Inpainting, Color Visualization, Trace Visualization, Path Deviation

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"
}