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

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
}