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Data Augmentation Multiplier Calculator

Work out data augmentation multiplier instantly with clear inputs, formula shown and shareable results.

About Data Augmentation Multiplier Calculator

Offline augmentation writes every transformed copy to disk, so the dataset multiplies in both example count and storage. Keeping the originals adds one more copy per image. On-the-fly augmentation in the data loader avoids the storage cost entirely and gives a different random transform each epoch, which usually regularises better — the offline route is mainly for reproducibility or slow pipelines.

Formula

Augmentation volume

total = base x copies (+ base if originals kept); bytes = total x average size

Worked example

With the default values below, Data Augmentation Multiplier Calculator returns Training images after augmentation of 25,000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Original imagesminimum 1
5,000
Augmented copies per imageminimum 1
4
Average image sizeminimum 1 KB
350 KB
Keep the originals in the training setone of Yes, No - replace with augmentations
Yes

Results

Training images after augmentation
25,000
Dataset multiplier
5.00 x
Dataset size on disk
8.34 GiB
Extra storage consumed
6.68 GiB

Step by step

  1. 1Values used
  2. 2Augmentation volume
  3. 3Training images after augmentation= 25,000
  4. 4Dataset multiplier= 5.00 x
  5. 5Dataset size on disk= 8,960,000,000
  6. 6Extra storage consumed= 7,168,000,000

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 2 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

Does augmentation add real information?

No. It encodes invariances you believe hold, such as a flipped cat still being a cat. It reduces overfitting but cannot substitute for genuinely new examples.

Should augmentation apply to the validation set?

No, apart from the deterministic resize and normalisation used at inference. Augmenting validation data makes the score measure the wrong distribution.

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