HDMF-AI is a schema and Python API for storing the common results of AI algorithms in a standardized way within the Hierarchical Data Modeling Framework (HDMF).

HDMF-AI is designed to be flexible and extensible, allowing users to store a range of AI and machine learning results and metadata, such as from classification, regression, and clustering. These results are stored in the ResultsTable data type, which extends the DynamicTable data type within the base HDMF schema. The ResultsTable schema represents each data sample as a row and includes columns for storing model outputs and information about the AI/ML workflow, such as which data were used for training, validation, and testing.

By leveraging existing HDMF tools and standards, HDMF-AI provides a scalable and extensible framework for storing AI results in an accessible, standardized way that is compatible with other HDMF-based data formats, such as Neurodata Without Borders (NWB), a popular data standard for neurophysiology, and HDMF-Seq, a format for storing taxonomic and genomic sequence data. By enabling standardized co-storage of data and AI results, HDMF-AI may enhance the reproducibility and explainability of AI for science.

pip install hdmf-ai

For example usage, see example_usage.ipynb.