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: Corner Visualization, Image Convert Grayscale, Label Visualization, Image Slicer, Image Blur, SIFT Comparison, Ellipse Visualization, Halo Visualization, Webhook Sink, Contrast Equalization, Stability AI Outpainting, Camera Focus, Model Comparison Visualization, Stitch Images, Polygon Visualization, Stability AI Inpainting, Reference Path Visualization, Slack Notification, Circle Visualization, Background Subtraction, Stability AI Image Generation, Roboflow Dataset Upload, Icon Visualization, VLM as Classifier, Twilio SMS/MMS Notification, Model Monitoring Inference Aggregator, Object Detection Model, Multi-Label Classification Model, Color Visualization, Clip Comparison, Mask Visualization, Roboflow Dataset Upload, Image Slicer, Pixelate Visualization, Instance Segmentation Model, Keypoint Detection Model, Email Notification, Image Contours, Dynamic Zone, JSON Parser, Text Display, Blur Visualization, Roboflow Custom Metadata, Triangle Visualization, Identify Outliers, VLM as Detector, Relative Static Crop, Detections Consensus, Camera Focus, Classification Label Visualization, Image Threshold, PTZ Tracking (ONVIF).md), Camera Calibration, Dot Visualization, Background Color Visualization, Polygon Zone Visualization, Keypoint Visualization, Grid Visualization, Dynamic Crop, Trace Visualization, Crop Visualization, Absolute Static Crop, Line Counter Visualization, Twilio SMS Notification, Image Preprocessing, Identify Changes, SIFT, Perspective Correction, Email Notification, Motion Detection, Halo Visualization, VLM as Classifier, SIFT Comparison, Local File Sink, Depth Estimation, Morphological Transformation, Polygon Visualization, Single-Label Classification Model, QR Code Generator, VLM as Detector, Bounding Box Visualization
• outputs: Roboflow Custom Metadata, SmolVLM2, Moondream2, Model Monitoring Inference Aggregator, Object Detection Model, Multi-Label Classification Model, Instance Segmentation Model, SAM 3, Classification Label Visualization, SAM 3, Webhook Sink, Roboflow Dataset Upload, Single-Label Classification Model, Multi-Label Classification Model, Detections Classes Replacement, Qwen2.5-VL, Qwen3-VL, Single-Label Classification Model, Object Detection Model, Instance Segmentation Model, Keypoint Detection Model, Keypoint Detection Model, SAM 3, 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: Corner Visualization, Image Convert Grayscale, Label Visualization, Image Slicer, Image Blur, SIFT Comparison, Ellipse Visualization, Halo Visualization, Webhook Sink, Contrast Equalization, Stability AI Outpainting, Camera Focus, Model Comparison Visualization, Stitch Images, Polygon Visualization, Stability AI Inpainting, Reference Path Visualization, Slack Notification, Circle Visualization, Background Subtraction, Stability AI Image Generation, Roboflow Dataset Upload, Icon Visualization, VLM as Classifier, Twilio SMS/MMS Notification, Model Monitoring Inference Aggregator, Object Detection Model, Multi-Label Classification Model, Color Visualization, Clip Comparison, Mask Visualization, Roboflow Dataset Upload, Image Slicer, Pixelate Visualization, Instance Segmentation Model, Keypoint Detection Model, Email Notification, Image Contours, Dynamic Zone, JSON Parser, Text Display, Blur Visualization, Roboflow Custom Metadata, Triangle Visualization, Identify Outliers, VLM as Detector, Relative Static Crop, Detections Consensus, Camera Focus, Classification Label Visualization, Image Threshold, PTZ Tracking (ONVIF).md), Camera Calibration, Dot Visualization, Background Color Visualization, Polygon Zone Visualization, Keypoint Visualization, Grid Visualization, Dynamic Crop, Trace Visualization, Crop Visualization, Absolute Static Crop, Line Counter Visualization, Twilio SMS Notification, Image Preprocessing, Identify Changes, SIFT, Perspective Correction, Email Notification, Motion Detection, Halo Visualization, VLM as Classifier, SIFT Comparison, Local File Sink, Depth Estimation, Morphological Transformation, Polygon Visualization, Single-Label Classification Model, QR Code Generator, VLM as Detector, Bounding Box Visualization
• outputs: Corner Visualization, Label Visualization, Image Blur, Florence-2 Model, SIFT Comparison, Google Gemini, Ellipse Visualization, Halo Visualization, Webhook Sink, Contrast Equalization, Stability AI Outpainting, Perception Encoder Embedding Model, Model Comparison Visualization, Line Counter, Distance Measurement, Polygon Visualization, Stability AI Inpainting, Reference Path Visualization, OpenAI, OpenAI, Slack Notification, Circle Visualization, Stability AI Image Generation, Roboflow Dataset Upload, Path Deviation, Icon Visualization, LMM For Classification, YOLO-World Model, Pixel Color Count, Cache Get, Twilio SMS/MMS Notification, Model Monitoring Inference Aggregator, Color Visualization, Clip Comparison, SAM 3, Mask Visualization, Roboflow Dataset Upload, Anthropic Claude, Time in Zone, Detections Classes Replacement, Line Counter, OpenAI, CLIP Embedding Model, Instance Segmentation Model, Email Notification, Google Gemini, Stitch OCR Detections, Text Display, Roboflow Custom Metadata, Triangle Visualization, Google Vision OCR, SAM 3, Classification Label Visualization, Image Threshold, LMM, PTZ Tracking (ONVIF).md), Dot Visualization, Anthropic Claude, Time in Zone, Stitch OCR Detections, Background Color Visualization, Seg Preview, Polygon Zone Visualization, Keypoint Visualization, Anthropic Claude, Dynamic Crop, Trace Visualization, SAM 3, Crop Visualization, Line Counter Visualization, Florence-2 Model, Time in Zone, Google Gemini, Detections Stitch, Segment Anything 2 Model, Moondream2, Twilio SMS Notification, Image Preprocessing, Instance Segmentation Model, Perspective Correction, Email Notification, Halo Visualization, Path Deviation, Local File Sink, Depth Estimation, CogVLM, Morphological Transformation, Polygon Visualization, OpenAI, QR Code Generator, Cache Set, Llama 3.2 Vision, Bounding Box Visualization, Size 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"
}