IoU (Object Detection) Calculator

Work out iou (object detection) instantly with clear inputs, formula shown and shareable results.

About IoU (Object Detection) Calculator

Intersection over union divides the overlapping area of two boxes by the area they jointly cover, so it rewards both correct position and correct size. Detection benchmarks turn IoU into a binary match using a threshold: PASCAL VOC used 0.5, while COCO averages over thresholds from 0.5 to 0.95 in steps of 0.05 to reward tighter localisation.

Formula

IoU

IoU = intersection / (area A + area B - intersection)

Worked example

With the default values below, IoU (Object Detection) Calculator returns Intersection over union of 0.3636. Change any input above and every figure here is recalculated in your browser.

Inputs used

Intersection areaminimum 0 px^2: 1,200 px^2
Predicted box areaminimum 1 px^2: 2,500 px^2
Ground-truth box areaminimum 1 px^2: 2,000 px^2
Match thresholdaccepts 0.05–0.99: 0.5

Results

Intersection over union: 0.3636
Union area: 3,300.00 px^2
Ground truth covered by the prediction: 60.00%
Detection verdict: Counts as a false positive at IoU >= 0.5

Step by step

1Values used
2IoU
3Intersection over union= 0.3636
4Union area= 3,300.00 px^2
5Ground truth covered by the prediction= 60.00
6Detection verdict= Counts as a false positive at IoU >= 0.5

Assumptions and limits

The result is derived only from the 4 inputs 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

Why is my IoU low when the boxes look close?

IoU falls quickly with size mismatch. A prediction twice the area of the ground truth caps at 0.5 even when perfectly centred.

What does mAP@[.5:.95] mean?

It is the mean average precision averaged over ten IoU thresholds from 0.50 to 0.95, so a model must localise tightly, not just find the object.