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

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"
}