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

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

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"
}