The Excellence
Directory Platform Template

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

Billion-scale benchmark dataset containing 96-dimensional deep learning image embeddings. Provides real-world proxy for testing distributed systems and GPU-accelerated vector search at scale.

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.

The open‑source repository containing the implementation, configuration, and scripts of VectorDBBench, enabling users to run standardized benchmarks across multiple vector database systems locally or in CI.

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.

An annual competition focused on similarity search and indexing algorithms, including approximate nearest neighbor methods and high-dimensional vector indexing, providing benchmarks and results relevant to vector database research.

ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.

BEIR (Benchmarking IR) is a benchmark suite for evaluating information retrieval and vector search systems across multiple tasks and datasets. Useful for comparing vector database performance.

A benchmarking resource for evaluating approximate nearest neighbor search (ANNS) methods on billion-scale datasets, highly relevant for assessing the scalability of vector databases.

A collection of datasets curated by Intel Labs specifically for evaluating and benchmarking vector search algorithms and databases.

Benchmark results and tools by MyScale aimed at measuring the performance of vector databases in various search and retrieval tasks.