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:
-
🎯 Analyzing predictions based on task type (single-label or multi-label)
-
📊 Organizing results by confidence score
-
🎨 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@v1
to 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:
LMM
,Depth Estimation
,Classification Label Visualization
,Camera Calibration
,Stitch OCR Detections
,LMM For Classification
,Clip Comparison
,CSV Formatter
,Instance Segmentation Model
,Stitch Images
,Line Counter
,CogVLM
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Twilio SMS Notification
,Multi-Label Classification Model
,Distance Measurement
,SIFT Comparison
,Florence-2 Model
,OpenAI
,Label Visualization
,Single-Label Classification Model
,Roboflow Dataset Upload
,Dimension Collapse
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Object Detection Model
,Pixel Color Count
,VLM as Classifier
,Detections Consensus
,Grid Visualization
,Dynamic Zone
,Image Convert Grayscale
,Halo Visualization
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Camera Focus
,Identify Changes
,VLM as Detector
,Florence-2 Model
,Local File Sink
,VLM as Detector
,OpenAI
,Google Vision OCR
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,SIFT
,Blur Visualization
,Circle Visualization
,Dot Visualization
,Image Blur
,Background Color Visualization
,Multi-Label Classification Model
,Single-Label Classification Model
,Color Visualization
,Pixelate Visualization
,Identify Outliers
,Clip Comparison
,Google Gemini
,Stability AI Inpainting
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,OCR Model
,Keypoint Visualization
,Template Matching
,Mask Visualization
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,Trace Visualization
,JSON Parser
,Corner Visualization
,Polygon Visualization
,Crop Visualization
,Email Notification
,Image Contours
,Image Slicer
,Line Counter
,Image Threshold
- outputs:
LMM
,Depth Estimation
,Classification Label Visualization
,Camera Calibration
,LMM For Classification
,Clip Comparison
,Instance Segmentation Model
,Stitch Images
,CogVLM
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Multi-Label Classification Model
,Object Detection Model
,Florence-2 Model
,QR Code Detection
,OpenAI
,Label Visualization
,Single-Label Classification Model
,Qwen2.5-VL
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Pixel Color Count
,Object Detection Model
,VLM as Classifier
,Moondream2
,Image Convert Grayscale
,Halo Visualization
,Buffer
,Triangle Visualization
,Time in Zone
,Keypoint Detection Model
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Camera Focus
,VLM as Detector
,Florence-2 Model
,OpenAI
,Google Vision OCR
,VLM as Detector
,Stability AI Image Generation
,Ellipse Visualization
,SIFT
,Blur Visualization
,Circle Visualization
,Image Blur
,Dot Visualization
,CLIP Embedding Model
,Background Color Visualization
,Gaze Detection
,Multi-Label Classification Model
,Single-Label Classification Model
,Byte Tracker
,Color Visualization
,Pixelate Visualization
,Dominant Color
,Clip Comparison
,Google Gemini
,Keypoint Detection Model
,Stability AI Inpainting
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,Detections Stabilizer
,OCR Model
,Keypoint Visualization
,Template Matching
,Mask Visualization
,SmolVLM2
,Image Preprocessing
,Line Counter Visualization
,Anthropic Claude
,SIFT Comparison
,YOLO-World Model
,Trace Visualization
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Barcode Detection
,Crop Visualization
,Image Contours
,Image Slicer
,Image Threshold
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
}