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📊 Evaluation Metrics

AI Performance Metrics

Understand Precision, Recall, F1-Score, mAP and IoU to evaluate and improve your models

AI Evaluation Metrics
10+
Metrics
99%+
Target
mAP
Standard
Key Features

What Makes This Technology Special

🎯

Precision

Ratio of correct detections out of all detections made

📈

Recall

Ratio of objects detected out of all objects present

⚖️

F1-Score

Harmonic mean of Precision and Recall

📊

mAP (Mean Average Precision)

Global standard for measuring Object Detection performance

📐

IoU (Intersection over Union)

Measure overlap between predicted and ground truth boxes

📉

Confusion Matrix

Table showing True/False Positive/Negative breakdown

Benefits

Why You Need This Technology

Choose Models Correctly

Compare models systematically with numerical metrics

Targeted Improvement

Identify model weaknesses for focused optimization

Client Communication

Report results using standardized metrics

Global Standards

Use the same metrics as global research and industry

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