This paper describes ANN-Benchmarks, a tool for evaluating the performance of
in-memory approximate nearest neighbor algorithms. It provides a standard
interface for measuring the performance and quality achieved by nearest
neighbor algorithms on different standard data sets. It supports several
different ways of integrating $k$-NN algorithms, and its configuration system
automatically tests a range of parameter settings for each algorithm.
Algorithms are compared with respect to many different (approximate) quality
measures, and adding more is easy and fast; the included plotting front-ends
can visualise these as images, $\LaTeX$ plots, and websites with interactive
plots. ANN-Benchmarks aims to provide a constantly updated overview of the
current state of the art of $k$-NN algorithms. In the short term, this overview
allows users to choose the correct $k$-NN algorithm and parameters for their
similarity search task; in the longer term, algorithm designers will be able to
use this overview to test and refine automatic parameter tuning. The paper
gives an overview of the system, evaluates the results of the benchmark, and
points out directions for future work. Interestingly, very different approaches
to $k$-NN search yield comparable quality-performance trade-offs. The system is
available at this http URL .

本文介绍了 ANN-Benchmarks，这是一种用于评估内存中近似最近邻算法的性能的工具，它提供了一个标准接口来测量不同标准数据集上最近邻算法的性能和质量，并支持多种不同方式的 k-NN 算法集成，其配置系统自动测试每个算法的一系列参数设置。ANN-Benchmarks 旨在提供 k-NN 算法的最新发展概述，并对评估结果进行了评估，并指出了未来工作的方向。