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Benchmarking nearest neighbors

This project contains some tools to benchmark various implementations of approximate nearest neighbor (ANN) search for different metrics.

See the results of this benchmark.


Euclidean space

Set similarity


Doing fast searching of nearest neighbors in high dimensional spaces is an increasingly important problem, but with little attempt at objectively comparing methods.

Data sets

We have a number of precomputed data sets for this. All data sets are pre-split into train/test and come with ground truth data in the form of the top 100 neighbors. We store them in a HDF5 format:

Dataset Dimensions Train size Test size Neighbors Distance Download
Fashion-MNIST 784 60,000 10,000 100 Euclidean HDF5 (217MB)
GIST 960 1,000,000 1,000 100 Euclidean N/A (coming shortly)
GloVe 25 1,133,628 59,886 100 Angular HDF5 (121MB)
Glove 50 1,133,628 59,886 100 Angular HDF5 (235MB)
Glove 100 1,133,628 59,886 100 Angular HDF5 (463MB)
Glove 200 1,133,628 59,886 100 Angular HDF5 (918MB)
MNIST 784 60,000 10,000 100 Euclidean HDF5 (217MB)
NYTimes 256 290,000 10,000 100 Angular HDF5 (301MB)
SIFT 128 1,000,000 10,000 100 Euclidean HDF5 (501MB)

Note that a few other datasets were used previously, in particular for Hamming and set similarity. We are going to add them back shortly in the more convenient HDF5 format.


Clone the repo and run bash This will install all libraries. It could take a while. It has been tested in Ubuntu 16.04. We advice to run it only in a VM.

Experiment Setup

Running a set of algorithms with specific parameters works:

  • Check that algos.yaml contains the parameter settings that you want to test
  • To run experiments on SIFT, invoke python --dataset glove-100-angular. See python --help for more information on possible settings. Note that experiments can take a long time.
  • To process the results, either use python --dataset glove-100-angular or python An example call: python --plottype recall/time --latex --scatter --outputdir website/.

Including Your Algorithm

You have two choices to include your own algorithm. If your algorithm has a Python wrapper (or is entirely written in Python), then all you need to do is to add your algorithm into ann_benchmarks/algorithms by providing a small wrapper.


  • Everyone is welcome to submit pull requests with tweaks and changes to how each library is being used.
  • In particular: if you are the author of any of these libraries, and you think the benchmark can be improved, consider making the improvement and submitting a pull request.
  • This is meant to be an ongoing project and represent the current state.
  • Make everything easy to replicate, including installing and preparing the datasets.
  • Try many different values of parameters for each library and ignore the points that are not on the precision-performance frontier.
  • High-dimensional datasets with approximately 100-1000 dimensions. This is challenging but also realistic. Not more than 1000 dimensions because those problems should probably be solved by doing dimensionality reduction separately.
  • No batching of queries, use single queries by default. ANN-Benchmarks saturates CPU cores by using a thread pool.
  • Avoid extremely costly index building (more than several hours).
  • Focus on datasets that fit in RAM. Out of core ANN could be the topic of a later comparison.
  • We currently support CPU-based ANN algorithms. GPU support is planned as future work.
  • Do proper train/test set of index data and query points.



Note that NMSLIB saves indices in the directory indices. If the tests are re-run using a different seed and/or a different number of queries, the content of this directory should be deleted.


The project is fully tested using Travis, with unit tests run for all different libraries and algorithms.