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

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

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"
}