mAP Calculator
Calculate mean Average Precision for evaluating object detection model performance.
Inputs
Comma-separated average precision for each class
mAP
0.7900
Number of Classes
5
Worst Class AP
0.6800
Best Class AP
0.9100
Step by step
Sum of AP scores
0.85 + 0.72 + 0.91 + 0.68 + 0.79
= 3.9500
mAP = sum ÷ number of classes
3.9500 ÷ 5
= 0.7900
How it works
Mean Average Precision (mAP) is the primary metric for object detection, computed as the average of per-class Average Precision (AP) scores. Each class's AP summarizes the precision-recall curve into a single number, and mAP averages these across all object categories.
Formula
mAP
mAP = (1/C) * sum(AP_c) for c=1..C
- AP_c
- Average precision for class c
- C
- Total number of object classes
Frequently Asked Questions
What is the difference between mAP@0.5 and mAP@[0.5:0.95]?
mAP@0.5 uses a single IoU threshold of 0.5 to determine correct detections. mAP@[0.5:0.95] averages mAP across IoU thresholds from 0.5 to 0.95 in steps of 0.05, rewarding more precise localization.
How is AP computed for a single class?
AP is the area under the precision-recall curve for that class. Detections are sorted by confidence, and precision/recall are computed at each threshold, then integrated (often using 101-point interpolation in COCO).