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

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

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"
}