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: Line Counter, Detections Consensus, Llama 3.2 Vision, Blur Visualization, Dimension Collapse, Perspective Correction, Polygon Zone Visualization, Bounding Box Visualization, QR Code Generator, Pixelate Visualization, Distance Measurement, Trace Visualization, Roboflow Custom Metadata, Image Threshold, Polygon Visualization, Dynamic Crop, Icon Visualization, Image Slicer, Identify Outliers, Stability AI Outpainting, Model Comparison Visualization, Dynamic Zone, Single-Label Classification Model, Clip Comparison, LMM, OpenAI, Classification Label Visualization, Stitch Images, Florence-2 Model, Mask Visualization, Single-Label Classification Model, Size Measurement, Relative Static Crop, Absolute Static Crop, SIFT Comparison, Google Gemini, Circle Visualization, Florence-2 Model, LMM For Classification, Ellipse Visualization, Image Convert Grayscale, Object Detection Model, OCR Model, Image Preprocessing, Color Visualization, Image Blur, Stability AI Image Generation, Anthropic Claude, Google Vision OCR, Keypoint Visualization, Camera Calibration, Local File Sink, VLM as Detector, EasyOCR, Image Slicer, Email Notification, VLM as Detector, Line Counter, Roboflow Dataset Upload, Background Color Visualization, Triangle Visualization, Slack Notification, Keypoint Detection Model, Multi-Label Classification Model, Halo Visualization, Corner Visualization, Google Gemini, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Dot Visualization, Image Contours, Twilio SMS Notification, Multi-Label Classification Model, Instance Segmentation Model, VLM as Classifier, CSV Formatter, Reference Path Visualization, Morphological Transformation, Motion Detection, OpenAI, Webhook Sink, PTZ Tracking (ONVIF).md), Contrast Equalization, Camera Focus, Stitch OCR Detections, Stability AI Inpainting, JSON Parser, Clip Comparison, CogVLM, Line Counter Visualization, Identify Changes, Template Matching, Email Notification, Crop Visualization, Grid Visualization, VLM as Classifier, OpenAI, Buffer, SIFT, Depth Estimation, Background Subtraction, Label Visualization, Anthropic Claude, Pixel Color Count, SIFT Comparison, OpenAI
• outputs: Llama 3.2 Vision, Blur Visualization, Perception Encoder Embedding Model, SAM 3, Perspective Correction, Polygon Zone Visualization, Bounding Box Visualization, Pixelate Visualization, Trace Visualization, QR Code Detection, Segment Anything 2 Model, Image Threshold, Image Slicer, Icon Visualization, Polygon Visualization, Dynamic Crop, Stability AI Outpainting, Model Comparison Visualization, Clip Comparison, Single-Label Classification Model, LMM, OpenAI, Stitch Images, Classification Label Visualization, Florence-2 Model, Mask Visualization, Single-Label Classification Model, Relative Static Crop, SIFT Comparison, Absolute Static Crop, SAM 3, Moondream2, Google Gemini, Circle Visualization, Florence-2 Model, LMM For Classification, Time in Zone, Ellipse Visualization, Image Convert Grayscale, Object Detection Model, OCR Model, SmolVLM2, Anthropic Claude, Image Preprocessing, Image Blur, Stability AI Image Generation, Google Vision OCR, Color Visualization, Keypoint Visualization, Camera Calibration, VLM as Detector, Keypoint Detection Model, EasyOCR, Image Slicer, VLM as Detector, Byte Tracker, Roboflow Dataset Upload, Gaze Detection, Background Color Visualization, Triangle Visualization, Keypoint Detection Model, Multi-Label Classification Model, Object Detection Model, Detections Stabilizer, Halo Visualization, Google Gemini, Corner Visualization, Roboflow Dataset Upload, Dot Visualization, Image Contours, Multi-Label Classification Model, Instance Segmentation Model, VLM as Classifier, Seg Preview, Reference Path Visualization, Morphological Transformation, Motion Detection, OpenAI, Instance Segmentation Model, Barcode Detection, Detections Stitch, Contrast Equalization, YOLO-World Model, Camera Focus, Stability AI Inpainting, Clip Comparison, CogVLM, Line Counter Visualization, Template Matching, CLIP Embedding Model, Email Notification, Crop Visualization, VLM as Classifier, Buffer, OpenAI, SIFT, Background Subtraction, Depth Estimation, SAM 3, Anthropic Claude, Pixel Color Count, Label Visualization, Qwen2.5-VL, OpenAI, Dominant Color

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
}