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

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

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"
}