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Dataset Size Requirement Calculator

Work out dataset size requirement instantly with clear inputs, formula shown and shareable results.

About Dataset Size Requirement Calculator

Classical learning theory ties sample complexity to model capacity: with roughly ten samples per free parameter, a model has enough evidence to fit each weight without memorising. The rule is crude for deep networks, where regularisation, augmentation and pretraining shift the requirement by orders of magnitude, but it remains a useful floor for tabular models and a warning sign when a network is far larger than its data.

Formula

Rule of ten

samples required = parameters x samples per parameter; per class = samples / classes; add a holdout fraction on top

Worked example

With the default values below, Dataset Size Requirement Calculator returns Training samples required of 500,000. Change any input above and every figure here is recalculated in your browser.

Inputs used

Trainable parametersminimum 1
50,000
Samples wanted per parameterminimum 0.1
10
Number of classesminimum 1
5

Results

Training samples required
500,000
Samples per class
100,000
Absolute floor (one sample per parameter)
50,000
Total including a 25% holdout
625,000

Step by step

  1. 1Values used
  2. 2Rule of ten
  3. 3Training samples required= 500,000
  4. 4Samples per class= 100,000
  5. 5Absolute floor (one sample per parameter)= 50,000
  6. 6Total including a 25% holdout= 625,000

Assumptions and limits

  • The result is derived only from the 3 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 whole numbers, 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 this apply to fine-tuning a large model?

No. Pretrained weights already encode most of the structure, so instruction fine-tuning can succeed with a few thousand high-quality examples despite billions of parameters.

What if I cannot collect enough data?

Shrink the model, add regularisation, use augmentation or transfer learning, and prefer simpler feature-based models over deep architectures.

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