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

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
}