

Main competition for large-scale vector database algorithms held at NeurIPS conferences. Framework for evaluating approximate nearest neighbor search algorithms on billion-scale datasets with standardized metrics and datasets.
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BigANN is the premier benchmark and competition for evaluating approximate nearest neighbor (ANN) search algorithms at billion-scale. Hosted at NeurIPS conferences, it provides standardized datasets and evaluation metrics for comparing vector search performance.
Includes billion-point datasets:
Major technology companies and research labs have participated, including Intel, Microsoft (SPTAG), and others, advancing the state of the art in vector search.
Datasets and evaluation code are publicly available for research purposes.