Top-K Accuracy Calculator
Fraction of samples whose true label is among the model's top-K predictions — the ImageNet-style metric.
Top-1 is strict (the single best guess must be right); top-5 credits a hit if the answer is anywhere in the top five — historically reported on ImageNet's 1000 classes because many categories are genuinely ambiguous (dog breeds, similar objects).
Formula
About Top-K Accuracy Calculator
Top-K accuracy credits a prediction as correct if the true label appears anywhere in the model's K highest-confidence guesses. Top-1 (the single best guess must be exactly right) is the strict standard; top-5 became famous on ImageNet's 1000 classes, where many categories are inherently ambiguous — distinguishing a hundred dog breeds means even a strong model's correct answer is often its second or third guess. This calculator converts your hit count into top-K accuracy and error rate, the headline metrics of every image-classification leaderboard.
How to use Top-K Accuracy Calculator
- 1Enter your values into Top-K Accuracy Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Top-K Accuracy Calculator?
- ✓Computes Top-K Accuracy instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: top-K accuracy = (samples whose true label is among the K highest-scored predictions) / total samples.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why is top-5 accuracy used alongside top-1?+
Because many classification tasks have genuinely confusable classes. On ImageNet, telling apart visually near-identical dog breeds or mushroom species is hard even for humans; top-5 measures whether the model has the right answer in contention. Top-1 measures decisiveness; top-5 measures whether the knowledge is present at all.
What top-1 accuracy is state of the art on ImageNet?+
Modern vision models (large ViTs, ConvNeXt, with heavy pretraining) exceed 88–90% top-1 and 98%+ top-5 on ImageNet-1k. For context, AlexNet in 2012 achieved ~63% top-1 / 84% top-5 — the jump that launched the deep learning era. Always state which ImageNet variant and resolution.
Is top-K accuracy useful outside image classification?+
Yes — anywhere the system returns a ranked shortlist a human or downstream process reviews: retrieval (is the answer in the top K results?), recommendation, next-word prediction, entity linking. It's effectively recall@K for single-label problems. Match K to how many candidates actually get considered.
How does top-K accuracy relate to other ranking metrics?+
It's the simplest: a hit/miss at cutoff K ignoring rank within the top-K and assuming exactly one correct answer. MRR refines it by rewarding higher placement of the first hit; NDCG and MAP handle multiple relevant items and graded relevance. Use top-K for single-label classification, the others for ranking with multiple targets.
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