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