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

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"
}