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