Anchor Box Size Calculator for Detection Models

Calculator Inputs

Use dataset statistics, stride, scale multipliers, and aspect ratios to estimate anchor widths and heights for object detection training.

Image width (px)

Image height (px)

Feature map stride

Average object width (px)

Average object height (px)

Average-to-median blend (%)

Median object width (px)

Median object height (px)

Coverage factor

Minimum object width (px)

Minimum object height (px)

Rounding increment (px)

Maximum object width (px)

Maximum object height (px)

Sort results by

Scale list

Comma separated. Example: 0.5, 0.75, 1, 1.5, 2

Aspect ratio list

Width ÷ height ratios. Example: 0.5, 1, 2, 3

Output mode

Validation object width (px)

Validation object height (px)

Reset

Example Data Table

Class	Avg Width	Avg Height	Median Width	Median Height	Typical Ratio	Suggested Scale
Pedestrian	34	78	30	74	0.44	0.75
Vehicle	96	62	92	58	1.55	1.25
Traffic Sign	42	41	40	40	1.02	0.50
Helmet	28	25	26	24	1.12	0.50

Formula Used

1) Blended reference size
Reference width = Average width × (1 − blend weight) + Median width × blend weight
Reference height = Average height × (1 − blend weight) + Median height × blend weight

2) Base anchor size
Base size = max(feature stride, √(reference width × reference height) × coverage factor)

3) Anchor width and height
Anchor width = Base size × Scale × √(aspect ratio)
Anchor height = Base size × Scale ÷ √(aspect ratio)

4) Centered IoU check
IoU = Intersection area ÷ Union area, using same-center boxes. This helps compare candidate anchors with a validation object size.

How to Use This Calculator

Enter your training image width, height, and feature stride.
Add average and median object dimensions from your labeled dataset.
Optionally add minimum and maximum object sizes to keep the reference size realistic.
Set a blend weight to decide how much median values influence the reference object.
Enter scale multipliers and aspect ratios you want to test.
Add a validation object size to compare candidates with centered IoU.
Click the calculate button to generate ranked anchors, chart points, and exportable results.
Download the final table as CSV or PDF for training notes or model review.

FAQs

1) What does this calculator estimate?

It estimates candidate anchor widths and heights for detection models. It combines object statistics, stride, aspect ratios, and user-selected scales to create anchor options you can compare before training.

2) Why use average and median sizes together?

Averages reflect the overall dataset, while medians reduce the effect of outliers. Blending both gives a steadier reference size when your object distribution is skewed.

3) What does the coverage factor do?

The coverage factor scales the base anchor size up or down. Higher values create larger anchors, which can help when you want more context around typical objects.

4) Why is stride included?

Stride links the anchor design to the feature map resolution. A very small anchor on a coarse feature map often performs poorly, so the formula prevents the base size from dropping below stride.

5) What does the IoU score mean here?

It measures overlap between a candidate anchor and a validation object size, assuming both boxes share the same center. It is a quick screening metric, not a full dataset evaluation.

6) Should I use normalized or pixel output?

Pixel output is easier for manual review. Normalized output helps when comparing anchor sizes across different image resolutions or documenting model settings in a resolution-independent way.

7) Can this replace k-means anchor clustering?

No. It is a planning and sanity-check tool. K-means or auto-anchor methods still provide stronger data-driven optimization when you need production-grade anchor selection.

8) When is this most useful?

It is useful during early model setup, dataset audits, anchor debugging, or when you want a faster manual starting point before heavier experiments.