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

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"
}