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

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
}