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: Instance Segmentation Model, Clip Comparison, Morphological Transformation, Polygon Zone Visualization, Motion Detection, Keypoint Visualization, Email Notification, Roboflow Custom Metadata, Camera Focus, Image Threshold, Email Notification, Reference Path Visualization, Camera Focus, Image Slicer, Stability AI Image Generation, Stability AI Outpainting, Stitch Images, Blur Visualization, Roboflow Dataset Upload, Depth Estimation, Image Preprocessing, Identify Outliers, JSON Parser, Local File Sink, Image Convert Grayscale, VLM as Detector, Dynamic Crop, Dot Visualization, Triangle Visualization, Crop Visualization, PTZ Tracking (ONVIF).md), Twilio SMS Notification, Perspective Correction, Twilio SMS/MMS Notification, Grid Visualization, Object Detection Model, Trace Visualization, QR Code Generator, Pixelate Visualization, Detections Consensus, Camera Calibration, Roboflow Dataset Upload, Webhook Sink, VLM as Detector, Background Subtraction, SIFT Comparison, Bounding Box Visualization, Contrast Equalization, Halo Visualization, Model Comparison Visualization, Label Visualization, Slack Notification, Dynamic Zone, Circle Visualization, Image Contours, Background Color Visualization, Image Blur, Mask Visualization, VLM as Classifier, Color Visualization, Corner Visualization, Classification Label Visualization, Single-Label Classification Model, Line Counter Visualization, Ellipse Visualization, Icon Visualization, Model Monitoring Inference Aggregator, Image Slicer, Absolute Static Crop, VLM as Classifier, Polygon Visualization, SIFT Comparison, Stability AI Inpainting, Keypoint Detection Model, Identify Changes, Relative Static Crop, SIFT, Text Display, Multi-Label Classification Model
• outputs: Instance Segmentation Model, Single-Label Classification Model, Detections Classes Replacement, Roboflow Dataset Upload, Webhook Sink, Instance Segmentation Model, Single-Label Classification Model, Object Detection Model, Roboflow Custom Metadata, SAM 3, Model Monitoring Inference Aggregator, Multi-Label Classification Model, Moondream2, Keypoint Detection Model, Qwen3-VL, SAM 3, Keypoint Detection Model, Qwen2.5-VL, Roboflow Dataset Upload, SmolVLM2, SAM 3, Classification Label Visualization, 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: Instance Segmentation Model, Clip Comparison, Morphological Transformation, Polygon Zone Visualization, Motion Detection, Keypoint Visualization, Email Notification, Roboflow Custom Metadata, Camera Focus, Image Threshold, Email Notification, Reference Path Visualization, Camera Focus, Image Slicer, Stability AI Image Generation, Stability AI Outpainting, Stitch Images, Blur Visualization, Roboflow Dataset Upload, Depth Estimation, Image Preprocessing, Identify Outliers, JSON Parser, Local File Sink, Image Convert Grayscale, VLM as Detector, Dynamic Crop, Dot Visualization, Triangle Visualization, Crop Visualization, PTZ Tracking (ONVIF).md), Twilio SMS Notification, Perspective Correction, Twilio SMS/MMS Notification, Grid Visualization, Object Detection Model, Trace Visualization, QR Code Generator, Pixelate Visualization, Detections Consensus, Camera Calibration, Roboflow Dataset Upload, Webhook Sink, VLM as Detector, Background Subtraction, SIFT Comparison, Bounding Box Visualization, Contrast Equalization, Halo Visualization, Model Comparison Visualization, Label Visualization, Slack Notification, Dynamic Zone, Circle Visualization, Image Contours, Background Color Visualization, Image Blur, Mask Visualization, VLM as Classifier, Color Visualization, Corner Visualization, Classification Label Visualization, Single-Label Classification Model, Line Counter Visualization, Ellipse Visualization, Icon Visualization, Model Monitoring Inference Aggregator, Image Slicer, Absolute Static Crop, VLM as Classifier, Polygon Visualization, SIFT Comparison, Stability AI Inpainting, Keypoint Detection Model, Identify Changes, Relative Static Crop, SIFT, Text Display, Multi-Label Classification Model
• outputs: Instance Segmentation Model, Clip Comparison, Florence-2 Model, Morphological Transformation, Google Gemini, LMM, Instance Segmentation Model, Email Notification, Polygon Zone Visualization, Detections Stitch, Roboflow Custom Metadata, Keypoint Visualization, Anthropic Claude, Pixel Color Count, Image Threshold, LMM For Classification, Anthropic Claude, Email Notification, Reference Path Visualization, Stitch OCR Detections, Stability AI Image Generation, Stability AI Outpainting, Time in Zone, OpenAI, Roboflow Dataset Upload, Depth Estimation, YOLO-World Model, Google Gemini, CogVLM, Image Preprocessing, Local File Sink, Florence-2 Model, SAM 3, Time in Zone, Dynamic Crop, Time in Zone, Perception Encoder Embedding Model, Moondream2, Dot Visualization, Triangle Visualization, Seg Preview, Crop Visualization, PTZ Tracking (ONVIF).md), Twilio SMS Notification, Path Deviation, Twilio SMS/MMS Notification, Perspective Correction, SAM 3, Google Gemini, Line Counter, Trace Visualization, QR Code Generator, OpenAI, CLIP Embedding Model, Roboflow Dataset Upload, Webhook Sink, Bounding Box Visualization, Cache Set, Contrast Equalization, Halo Visualization, Model Comparison Visualization, Slack Notification, Label Visualization, OpenAI, Circle Visualization, Cache Get, Image Blur, Background Color Visualization, Mask Visualization, Size Measurement, Google Vision OCR, Llama 3.2 Vision, Path Deviation, Corner Visualization, Classification Label Visualization, Color Visualization, Segment Anything 2 Model, OpenAI, Detections Classes Replacement, Line Counter Visualization, Icon Visualization, Ellipse Visualization, Model Monitoring Inference Aggregator, Line Counter, Polygon Visualization, SIFT Comparison, Stability AI Inpainting, SAM 3, Distance Measurement, Text Display

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"
}