BigANN Benchmarks

Billion-scale approximate nearest neighbor search benchmark competition. Features datasets like SIFT1B, Deep1B with standardized evaluation metrics for comparing vector search algorithms at scale.

Vector Index Benchmark for Embeddings - an extensible benchmarking suite for approximate nearest neighbor search methods using modern embedding datasets. VIBE addresses limitations of traditional ANN benchmarks by focusing on contemporary embedding models and datasets.

A comprehensive benchmarking project that evaluates and compares implementations of approximate nearest neighbor algorithms. Provides standardized datasets and metrics for comparing ANN libraries including FAISS, HNSW, Annoy, and ScaNN.

Billion-scale benchmark dataset containing 128-dimensional SIFT descriptors of one billion images. Widely used standard for evaluating approximate nearest neighbor search algorithms at scale.

WEAVESS is an open-source benchmarking and evaluation framework for graph-based approximate nearest neighbor (ANN) search methods, providing code and experiments for large-scale vector similarity search. It is useful for researchers and practitioners comparing vector indexing algorithms for vector databases and AI search applications.

A 2024 paper introducing CANDY, a benchmark for continuous ANN search with a focus on dynamic data ingestion, crucial for next-generation vector databases.