Data Augmentation Calculator

Augmented samples are derived from the same original images or records, so they carry correlated information rather than being fully independent new data points -- training on ten flipped/rotated versions of one photo doesn't teach a model as much as ten genuinely different photos would. Effective Dataset Size discounts the raw total by a diversity and quality factor to reflect that diminishing return, rather than treating every augmented copy as equally valuable to a brand-new sample.

Augmentations per Sample enters the formula as a direct multiplier -- each additional augmented copy per sample adds roughly Original Samples × Diversity Factor × Quality Factor to Effective Dataset Size, with no ceiling within its own declared range (1 to 50). Augmentation Types only adjusts the Diversity Factor itself, and that factor is capped between 0.5 and 1.0 -- so even a large change in Augmentation Types can at most double the multiplier it feeds into, while Augmentations per Sample has no such limit. At this calculator's default inputs, one additional Augmentations per Sample moves Effective Dataset Size by roughly 3,600 samples, versus roughly 2,250 for one additional Augmentation Type -- and that gap widens sharply across the full declared range of both inputs.

It's your estimate of how much label reliability or visual fidelity degrades from the augmentation process itself -- for example, an aggressive crop that cuts off the labeled object, or synthetic noise that makes a class harder to recognize even for a human reviewer. Raising Quality Drop % lowers Effective Dataset Size because it discounts how much genuinely useful training signal your augmented copies actually contribute.

Original Samples is both the starting point every other factor multiplies against and, in absolute terms, usually the largest number in the calculation by a wide margin (thousands to millions of samples versus a handful of augmentation types or a percentage quality drop). This calculator verifies that Original Samples moves Effective Dataset Size more than any other single input directly against the underlying formula.

It's a rough planning estimate that assumes roughly 100KB per sample on average, which is reasonable for typical compressed images but can be far off for other data types -- high-resolution photos, audio clips, or video frames can each run many times larger, while short text records can be far smaller. Treat Est. Storage as a starting point for budgeting disk space, not a precise prediction for your specific dataset format.