Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring out what subset to use as features. So much time, in fact, that progress in the field of ML is hampered by a fundamental obstacle: the wide variety of data representations.
ML datasets cover a broad range of content types, from text and structured data to images, audio, and video. Even within datasets that cover the same types of content, every dataset has a unique ad hoc arrangement of files and data formats. This challenge reduces productivity throughout the entire ML development process, from finding the data to training the model. It also impedes development of badly needed tooling for working with datasets.
There are general purpose metadata formats for datasets such as schema.org and DCAT. However, these formats were designed for data discovery rather than for the specific needs of ML data, such as the ability to extract and combine data from structured and unstructured sources, to include metadata that would enable responsible use of the data, or to describe ML usage characteristics such as defining training, test and validation sets.
Today, we’re introducing Croissant, a new metadata format for ML-ready datasets. Croissant was developed collaboratively by a community from industry and academia, as part of the MLCommons effort. The Croissant format doesn’t change how the actual data is represented (e.g., image or text file formats) — it provides a standard way to describe and organize it. Croissant builds upon schema.org, the de facto standard for publishing structured data on the Web, which is already used by over 40M datasets. Croissant augments it with comprehensive layers for ML relevant metadata, data resources, data organization, and default ML semantics.
In addition, we are announcing support from major tools and repositories: Today, three widely used collections of ML datasets — Kaggle, Hugging Face, and OpenML — will begin supporting the Croissant format for the datasets they host; the Dataset Search tool lets users search for Croissant datasets across the Web; and popular ML frameworks, including TensorFlow, PyTorch, and JAX, can load Croissant datasets easily using the TensorFlow Datasets (TFDS) package.