Test-time augmentation has a reputation problem: it's the trick Kaggle winners confess to in the last paragraph, the thing production engineers wave off as leaderboard cosmetics. The idea is almost embarrassingly simple — run inference on several transformed copies of the input, average the probabilities — and because it multiplies serving cost, most advice online rounds it down to "not worth it in the real world" without a number attached. After a month of measuring training tricks whose true effects turned out to be +0.08 , +0.14 , or zero , I wanted TTA on the same scale. Ten seeds of the usual tuned CIFAR ResNet18 recipe, each model evaluated four ways — plain, mirror-flip averaging, five 2-pixel shifts, and shifts-plus-flips — with every comparison paired within run, plus clean warmed-up GPU timings for the actual price tag.

Update (2026-10-22): the cost column below is the worst case — these timings run each view as a separate forward pass at a saturating batch size.…