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

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"
}