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

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
}