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

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"
}