Principle:Roboflow Rf detr Detection Visualization
| Knowledge Sources | |
|---|---|
| Domains | Computer_Vision, Visualization |
| Last Updated | 2026-02-08 15:00 GMT |
Overview
The process of rendering detection results as visual overlays on images for human interpretation and debugging.
Description
Detection visualization transforms structured detection outputs (bounding boxes, class labels, confidence scores) into annotated images. This involves:
- Box annotation: Drawing colored rectangles around detected objects
- Label annotation: Displaying class names and confidence scores near each box
- Class name mapping: Converting integer class IDs to human-readable names using a lookup dictionary
Visualization is essential for debugging model performance, presenting results to stakeholders, and building interactive applications.
Usage
Use this principle after running object detection to visually inspect or present results. The Supervision library provides composable annotators that can be combined for custom visualizations.
Theoretical Basis
Detection visualization maps the abstract detection output (numerical bounding boxes and class probabilities) to a visual representation. The COCO_CLASSES dictionary provides the standard 80-class name mapping used by COCO-pretrained models, while fine-tuned models use custom class names stored in the model.