Single-Label Classification Model

v2

Class: RoboflowClassificationModelBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.multi_class_classification.v2.RoboflowClassificationModelBlockV2

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-class 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_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 Single-Label Classification Model in version v2.

• inputs: Mask Visualization, Classification Label Visualization, Detections Consensus, Webhook Sink, Dynamic Zone, Email Notification, QR Code Generator, Dynamic Crop, VLM As Detector, VLM As Detector, Multi-Label Classification Model, Image Blur, Corner Visualization, Image Convert Grayscale, Stability AI Outpainting, Halo Visualization, Stability AI Inpainting, JSON Parser, Image Contours, Single-Label Classification Model, Trace Visualization, Morphological Transformation, Triangle Visualization, Instance Segmentation Model, Relative Static Crop, Text Display, Stitch Images, Camera Calibration, Grid Visualization, Local File Sink, Slack Notification, VLM As Classifier, Roboflow Dataset Upload, Camera Focus, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Image Slicer, Polygon Visualization, Object Detection Model, Line Counter Visualization, Contrast Equalization, Identify Changes, SIFT Comparison, Camera Focus, Background Subtraction, Image Slicer, Circle Visualization, Identify Outliers, SIFT Comparison, Halo Visualization, Blur Visualization, Label Visualization, Twilio SMS/MMS Notification, Clip Comparison, Email Notification, Ellipse Visualization, SIFT, Image Preprocessing, Model Monitoring Inference Aggregator, Image Threshold, Background Color Visualization, Model Comparison Visualization, Depth Estimation, Keypoint Detection Model, Absolute Static Crop, Roboflow Custom Metadata, Perspective Correction, Pixelate Visualization, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, Polygon Visualization, Twilio SMS Notification, VLM As Classifier, Bounding Box Visualization, Polygon Zone Visualization, Icon Visualization, Crop Visualization, Motion Detection, Roboflow Dataset Upload
• outputs: Classification Label Visualization, Moondream2, Instance Segmentation Model, Roboflow Custom Metadata, Webhook Sink, Multi-Label Classification Model, Qwen3-VL, Roboflow Dataset Upload, Multi-Label Classification Model, SAM 3, Qwen2.5-VL, Object Detection Model, SAM 3, Keypoint Detection Model, Model Monitoring Inference Aggregator, SmolVLM2, SAM 3, Single-Label Classification Model, Detections Classes Replacement, Object Detection Model, Single-Label Classification Model, Keypoint Detection Model, Roboflow Dataset Upload, Instance Segmentation Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Single-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 Single-Label Classification Model in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_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: RoboflowClassificationModelBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.models.roboflow.multi_class_classification.v1.RoboflowClassificationModelBlockV1

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-class 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_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 Single-Label Classification Model in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Single-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 Single-Label Classification Model in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/roboflow_classification_model@v1",
    "images": "$inputs.image",
    "model_id": "my_project/3",
    "confidence": 0.3,
    "disable_active_learning": true,
    "active_learning_target_dataset": "my_project"
}