Classification Label Visualization¶
Class: 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:
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🎯 Analyzing predictions based on task type (single-label or multi-label)
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📊 Organizing results by confidence score
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🎨 Rendering labels with customizable positioning and styling
Parameters¶
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task_type: Specifies how to handle predictions. Available options:-
"single-label": Shows only the highest confidence prediction
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"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
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text_padding: Controls spacing between labels and from image edges
Why Use This Visualization?¶
This is especially useful for:
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🏷️ Creating clear, professional-looking prediction displays
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📱 Supporting different UI layouts with flexible positioning
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🎨 Customizing appearance for different use cases
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📊 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:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,VLM as Detector,VLM as Classifier,LMM,Polygon Zone Visualization,Camera Focus,Image Slicer,Line Counter,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Stability AI Inpainting,Pixelate Visualization,Line Counter,OpenAI,Detections Consensus,Distance Measurement,Image Convert Grayscale,Absolute Static Crop,Stability AI Image Generation,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,VLM as Classifier,Dynamic Zone,CogVLM,Camera Calibration,Email Notification,Classification Label Visualization,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Ellipse Visualization,Pixel Color Count,Size Measurement,Bounding Box Visualization,JSON Parser,Object Detection Model,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Clip Comparison,Local File Sink,Image Slicer,Anthropic Claude,Crop Visualization,Identify Outliers,OCR Model,Relative Static Crop,Model Comparison Visualization,Stitch OCR Detections,Perspective Correction,OpenAI,Mask Visualization,Clip Comparison,Dynamic Crop,Template Matching,CSV Formatter,Florence-2 Model,Instance Segmentation Model,Keypoint Detection Model,Image Contours,Buffer,SIFT,SIFT Comparison,Reference Path Visualization,Multi-Label Classification Model,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,VLM as Detector,Keypoint Visualization,Identify Changes,Multi-Label Classification Model,LMM For Classification,Roboflow Custom Metadata,Grid Visualization,Stitch Images,Dimension Collapse,Blur Visualization - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,VLM as Detector,VLM as Classifier,LMM,Polygon Zone Visualization,Camera Focus,Image Slicer,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Single-Label Classification Model,Stability AI Inpainting,Pixelate Visualization,OpenAI,Gaze Detection,Dominant Color,Stability AI Image Generation,Image Convert Grayscale,Absolute Static Crop,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,VLM as Classifier,CogVLM,Instance Segmentation Model,Camera Calibration,Object Detection Model,Classification Label Visualization,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Byte Tracker,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,Object Detection Model,Line Counter Visualization,Image Preprocessing,Keypoint Detection Model,Trace Visualization,Label Visualization,Clip Comparison,Image Slicer,Anthropic Claude,Crop Visualization,Detections Stitch,OCR Model,YOLO-World Model,Model Comparison Visualization,Relative Static Crop,QR Code Detection,Perspective Correction,OpenAI,Qwen2.5-VL,Mask Visualization,Time in Zone,Clip Comparison,Dynamic Crop,Template Matching,Florence-2 Model,Barcode Detection,Instance Segmentation Model,Keypoint Detection Model,Image Contours,Buffer,SIFT,Multi-Label Classification Model,Florence-2 Model,Reference Path Visualization,CLIP Embedding Model,Triangle Visualization,SIFT Comparison,VLM as Detector,Keypoint Visualization,Multi-Label Classification Model,LMM For Classification,Segment Anything 2 Model,Detections Stabilizer,Stitch Images,Blur Visualization
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
}