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