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