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