ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms
Martin Aum\"uller, Erik Bernhardsson, Alexander Faithfull

TL;DR
ANN-Benchmarks is a comprehensive benchmarking tool that evaluates approximate nearest neighbor algorithms across multiple datasets and metrics, aiding users in selecting optimal algorithms and guiding future research.
Contribution
It introduces a standardized, flexible benchmarking system for $k$-NN algorithms, enabling easy comparison, visualization, and parameter tuning.
Findings
Different $k$-NN approaches achieve similar quality-performance trade-offs.
The system facilitates automatic parameter testing and visualization.
Benchmark results help in selecting suitable algorithms for specific tasks.
Abstract
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 -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, plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of -NN algorithms. In the short term, this overview allows users to choose the correct -NN algorithm and parameters…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Advanced Image and Video Retrieval Techniques · Machine Learning and Data Classification
