Classification Label Visualization

Class: ClassificationLabelVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.classification_label.v1.ClassificationLabelVisualizationBlockV1

Visualizes classification predictions with customizable labels and positioning options. Perfect for creating clear, informative displays of model predictions!

How It Works

This visualization processes classification predictions by:

1. 🎯 Analyzing predictions based on task type (single-label or multi-label)

2. 📊 Organizing results by confidence score

3. 🎨 Rendering labels with customizable positioning and styling

Parameters

• task_type: Specifies how to handle predictions. Available options:

  • "single-label": Shows only the highest confidence prediction

  • "multi-label": Displays all predictions above threshold

• text_position: Controls label placement with 9 options:

  • Top: LEFT, CENTER, RIGHT
  • Center: LEFT, CENTER, RIGHT
  • Bottom: LEFT, CENTER, RIGHT

• text: Determines what information to display:

  • "Class": Only show class names
  • "Confidence": Only show confidence scores
  • "Class and Confidence": Show both

• text_padding: Controls spacing between labels and from image edges

Why Use This Visualization?

This is especially useful for:

• 🏷️ Creating clear, professional-looking prediction displays

• 📱 Supporting different UI layouts with flexible positioning

• 🎨 Customizing appearance for different use cases

• 📊 Showing prediction confidence in an intuitive way

Example Usage

Use this visualization after any classification model to display predictions in a clean, organized format. Perfect for both single predictions and multiple class probabilities.

Type identifier

Use the following identifier in step "type" field: roboflow_core/classification_label_visualization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
copy_image	bool	Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
text	str	The data to display in the text labels..	✅
text_position	str	The anchor position for placing the label..	✅
text_color	str	Color of the text..	✅
text_scale	float	Scale of the text..	✅
text_thickness	int	Thickness of the text characters..	✅
text_padding	int	Padding around the text in pixels..	✅
border_radius	int	Radius of the label in pixels..	✅

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 Classification Label Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Classification Label Visualization in version v1 has.

Bindings

• input

  • image (image): The image to visualize on..
  • copy_image (boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..
  • predictions (classification_prediction): Classification predictions..
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • text (string): The data to display in the text labels..
  • text_position (string): The anchor position for placing the label..
  • text_color (string): Color of the text..
  • text_scale (float): Scale of the text..
  • text_thickness (integer): Thickness of the text characters..
  • text_padding (integer): Padding around the text in pixels..
  • border_radius (integer): Radius of the label in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Classification Label Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/classification_label_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.classification_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "text": "LABEL",
    "text_position": "CENTER",
    "text_color": "WHITE",
    "text_scale": 1.0,
    "text_thickness": 1,
    "text_padding": 10,
    "border_radius": 0
}