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Machine learning research has long focused on models rather than datasets,and prominent datasets are used for common ML tasks without regard to thebreadth, difficulty, and faithfulness of the underlying problems. Neglectingthe fundamental importance of data has given rise to inaccuracy, bias, andfragility in real-world applications, and research is hindered by saturationacross existing dataset benchmarks. In response, we present DataPerf, acommunity-led benchmark suite for evaluating ML datasets and data-centricalgorithms. We aim to foster innovation in data-centric AI through competition,comparability, and reproducibility. We enable the ML community to iterate ondatasets, instead of just architectures, and we provide an open, onlineplatform with multiple rounds of challenges to support this iterativedevelopment. The first iteration of DataPerf contains five benchmarks coveringa wide spectrum of data-centric techniques, tasks, and modalities in vision,speech, acquisition, debugging, and diffusion prompting, and we support hostingnew contributed benchmarks from the community. The benchmarks, onlineevaluation platform, and baseline implementations are open source, and theMLCommons Association will maintain DataPerf to ensure long-term benefits toacademia and industry.

DataPerf: Benchmarks for Data-Centric AI Development