What is average precision (AP)?

What is average precision?

Average precision reduces the precision-recall curve for a single class to one number, roughly the area under that curve. A higher AP means the model maintains high precision even as recall increases, so it both finds most of the real positives and keeps its positive predictions trustworthy across thresholds.

In detection tasks, a prediction is counted as correct only when it overlaps a ground-truth object above an intersection over union threshold, so AP is always reported with respect to a chosen IoU level.

Key takeaways

AP is the area under the precision-recall curve for one class.
Higher AP means better precision sustained across recall levels.
In detection, AP is tied to a specific IoU matching threshold.

How it works

Predictions for a class are ranked by confidence, precision and recall are computed at each threshold, and the resulting curve is integrated to a single value. Different conventions exist for the integration, such as interpolating precision or sampling at fixed recall points, which is why AP definitions can vary slightly between benchmarks.

Why it matters

AP condenses a model's threshold-independent quality on one class into a number you can track and compare. It is the per-class quantity that gets averaged into mean average precision, the headline metric for most object detection and instance segmentation benchmarks.

Frequently asked questions

What is the difference between AP and mAP?

AP is computed for a single class. Mean average precision averages AP across all classes, and often across several IoU thresholds.

Why is AP tied to an IoU threshold?

In detection and segmentation, a prediction only counts as correct if it overlaps the ground truth enough, and IoU sets that bar.

Related terms

Precision, Recall, Precision-recall curve, Mean average precision (mAP), Intersection over union (IoU).

Jesse Mostipak is the SEO and Content Manager at Voxel51, where the work is helping humans find and trust what the brand knows, and teaching the Google knowledge graph and the LLMs answering on their behalf to do the same. That question, how knowledge gets built inside a system, is one Jesse has been chasing for years. Earlier versions of it ran through a New York City high school science classroom, data science and machine learning, and developer relations at Kaggle, Posit (formerly RStudio), and Baseten. The answer doesn't change much depending on whether the learner is a teenager, a software engineer, or a knowledge graph. Jesse holds a Master's in Education from CUNY Hunter College. LinkedIn

Average precision (AP)

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