Mean Average Precision (MAP) Calculator
Average precision across recall levels for binary relevance — the IR and object-detection ranking standard.
AP integrates precision over recall by averaging precision at the rank of each relevant item, rewarding rankings that cluster relevant results high. MAP averages AP across queries. In object detection, AP is computed per class over IoU thresholds, then mean-averaged into mAP.
Formula
About Mean Average Precision (MAP) Calculator
Mean Average Precision is the workhorse ranking metric of information retrieval and object detection. For each query it computes Average Precision — the mean of the precision values measured at every rank where a relevant item appears — which rewards rankings that pack relevant results toward the top across all recall levels. Then it averages AP over queries. This calculator handles multiple queries (separated by semicolons) and computes per-query AP and the overall MAP, the same computation behind the mAP numbers reported in every detection benchmark from PASCAL VOC to COCO.
How to use Mean Average Precision (MAP) Calculator
- 1Enter your values into Mean Average Precision (MAP) 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 Mean Average Precision (MAP) Calculator?
- ✓Computes Mean Average Precision (MAP) instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: AP = (1/R) Σ_{k: relevant} Precision@k.
- ✓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
How is Average Precision different from Precision@K?+
Precision@K measures one cutoff; AP integrates precision across ALL cutoffs by sampling it at each relevant item's rank. So AP captures the whole precision-recall trade-off in one number and rewards getting relevant items high throughout the list, not just within an arbitrary top-K window.
Why is MAP used for object detection (mAP)?+
Detection ranks bounding boxes by confidence; AP measures how well high-confidence boxes are actually correct across the precision-recall curve, per class. mAP averages AP over classes (and, in COCO, over IoU thresholds 0.5–0.95). It rewards detectors that are both accurate and well-calibrated in their confidence ordering.
MAP vs NDCG?+
MAP assumes binary relevance (relevant or not); NDCG handles graded relevance (degrees of relevance) and applies a position discount. Use MAP for binary-relevance IR and detection; use NDCG when relevance comes in grades. Both reward ranking relevant items high, but NDCG is richer when your judgments aren't yes/no.
What's a good MAP score?+
Entirely task-dependent — there's no universal threshold. On hard detection benchmarks (COCO), state-of-the-art mAP is ~55–65%; on easier retrieval tasks, 0.8+ is achievable. Always interpret MAP relative to a baseline and the task difficulty, and compare models on the SAME data and relevance judgments.
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