mAP Calculator
Calculate mean Average Precision for evaluating object detection model performance.
About mAP Calculator
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
Worked example
With the default values below, mAP Calculator returns mAP of 0.7900. Change any input above and every figure here is recalculated in your browser.
Inputs used
- AP Scores per Class
- 0.85, 0.72, 0.91, 0.68, 0.79
Results
- mAP
- 0.7900
- Number of Classes
- 5
- Worst Class AP
- 0.6800
- Best Class AP
- 0.9100
Step by step
- 1Sum of AP scores= 3.9500
- 2mAP = sum ÷ number of classes= 0.7900
Assumptions and limits
- The result is derived only from the single input listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
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).