Info

ANN-Benchmarks is a benchmarking environment for approximate nearest neighbor algorithms search. This website contains the current benchmarking results. Please visit http://github.com/erikbern/ann-benchmarks/ to get an overview over evaluated data sets and algorithms. Make a pull request on Github to add your own code or improvements to the benchmarking system.

Benchmarking Results

Results are split by distance measure and dataset. In the bottom, you can find an overview of an algorithm's performance on all datasets. Each dataset is annoted by (k = ...), the number of nearest neighbors an algorithm was supposed to return. The plot shown depicts Recall (the fraction of true nearest neighbors found, on average over all queries) against Queries per second. Clicking on a plot reveils detailled interactive plots, including approximate recall, index size, and build time.

Benchmarks for Single Queries

Results by Dataset

Distance: Euclidean

sift-128-euclidean (k = 10)

Results by Algorithm

hnswlib

scann

bruteforce-blas

faiss-ivf

faiss-ivfpqfs

qdrant

milvus-flat

milvus-hnsw

milvus-ivfflat

milvus-ivfsq8

milvus-scann

milvus-ivfpq

patann

Benchmarks for Batched Queries

Results by Dataset

Distance: Euclidean-batch

sift-128-euclidean (k = 10)

Results by Algorithm

hnswlib-batch

scann-batch

bruteforce-blas-batch

faiss-ivf-batch

faiss-ivfpqfs-batch

qdrant-batch

milvus-flat-batch

milvus-hnsw-batch

milvus-ivfflat-batch

milvus-ivfsq8-batch

milvus-scann-batch

milvus-ivfpq-batch

patann-batch

Contact

ANN-Benchmarks has been developed by Martin Aumueller (maau@itu.dk), Erik Bernhardsson (mail@erikbern.com), and Alec Faitfull (alef@itu.dk). Please use Github to submit your implementation or improvements.