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