Model Comparison Visualization¶
Class: ModelComparisonVisualizationBlockV1
The ModelComparisonVisualization block draws all areas
predicted by neither model with a specified color,
lets overlapping areas of the predictions shine through,
and colors areas predicted by only one model with a distinct color.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/model_comparison_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_a |
str |
Color of the areas Model A predicted that Model B did not... | ✅ |
color_b |
str |
Color of the areas Model B predicted that Model A did not.. | ✅ |
background_color |
str |
Color of the areas neither model predicted.. | ✅ |
opacity |
float |
Transparency of the overlay.. | ✅ |
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 Model Comparison Visualization in version v1.
- inputs:
Segment Anything 2 Model,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Object Detection Model,Roboflow Custom Metadata,Object Detection Model,SIFT Comparison,Detection Offset,Grid Visualization,Ellipse Visualization,SIFT,VLM as Detector,CogVLM,Image Contours,OpenAI,Absolute Static Crop,Camera Focus,Trace Visualization,Multi-Label Classification Model,VLM as Detector,Dot Visualization,Google Vision OCR,Identify Outliers,Polygon Zone Visualization,Roboflow Dataset Upload,Identify Changes,VLM as Classifier,Gaze Detection,Classification Label Visualization,Corner Visualization,Byte Tracker,Llama 3.2 Vision,Dynamic Crop,Reference Path Visualization,Label Visualization,Detections Stabilizer,Mask Visualization,Triangle Visualization,Line Counter Visualization,Template Matching,Dynamic Zone,Detections Transformation,Model Monitoring Inference Aggregator,Time in Zone,Blur Visualization,Line Counter,Anthropic Claude,Webhook Sink,Instance Segmentation Model,SIFT Comparison,Time in Zone,Instance Segmentation Model,Slack Notification,Detections Filter,Stitch OCR Detections,Pixelate Visualization,Path Deviation,OpenAI,Relative Static Crop,Detections Consensus,Twilio SMS Notification,VLM as Classifier,Roboflow Dataset Upload,Keypoint Detection Model,Google Gemini,Model Comparison Visualization,Halo Visualization,JSON Parser,Crop Visualization,Byte Tracker,Image Blur,Circle Visualization,Velocity,Keypoint Detection Model,Image Preprocessing,Background Color Visualization,Bounding Rectangle,Florence-2 Model,Bounding Box Visualization,Byte Tracker,Local File Sink,Image Slicer,Florence-2 Model,LMM For Classification,Stitch Images,Stability AI Image Generation,Image Threshold,Detections Stitch,OCR Model,LMM,Keypoint Visualization,Email Notification,Color Visualization,Path Deviation,Single-Label Classification Model,YOLO-World Model,CSV Formatter,Image Convert Grayscale,Detections Classes Replacement,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
Model Comparison 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_a(Union[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction]): Predictions.color_a(string): Color of the areas Model A predicted that Model B did not...predictions_b(Union[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction]): Predictions.color_b(string): Color of the areas Model B predicted that Model A did not..background_color(string): Color of the areas neither model predicted..opacity(float_zero_to_one): Transparency of the overlay..
-
output
image(image): Image in workflows.
Example JSON definition of step Model Comparison Visualization in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/model_comparison_visualization@v1",
"image": "$inputs.image",
"copy_image": true,
"predictions_a": "$steps.object_detection_model.predictions",
"color_a": "GREEN",
"predictions_b": "$steps.object_detection_model.predictions",
"color_b": "RED",
"background_color": "BLACK",
"opacity": 0.7
}