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

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

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"
}