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

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"
}