ANN-Benchmarks
ANN-Benchmarks is a benchmarking environment for evaluating the performance of approximate nearest neighbor (ANN) search algorithms. It provides a platform to compare algorithms across multiple datasets and distance measures.
Features
- Benchmarking Platform: Offers comprehensive benchmarking for a wide range of approximate nearest neighbor algorithms.
- Multiple Datasets: Includes datasets such as glove, nytimes, fashion-mnist, gist, sift, word2bits, kosarak, and more, covering various data types and dimensionalities.
- Variety of Distance Measures: Supports benchmarking on different distance metrics, including Angular, Euclidean, Hamming, and Jaccard.
- Algorithm Coverage: Benchmarks a large selection of algorithms, including faiss-ivf, scann, pgvector, annoy, glass, hnswlib, BallTree(nmslib), vald(NGT-anng), hnsw(faiss), qdrant, n2, Milvus(Knowhere), mrpt, redisearch, SW-graph(nmslib), pynndescent, vearch, flann, luceneknn, weaviate, puffinn, elastiknn-l2lsh, sptag, ckdtree, opensearchknn, datasketch, and more.
- Interactive Results: Provides interactive plots and detailed statistics for each benchmark, including recall, queries per second, index size, and build time.
- Open Source Collaboration: Users can contribute new algorithms or improvements via pull requests on GitHub.
- Results Visualization: Results are split by distance measure and dataset, with summary plots for quick comparison.
Pricing
No pricing information is provided; ANN-Benchmarks is an open-source benchmarking platform.
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