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

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

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"
}