Macaroni: Crawling and Enriching Metadata from Public Model Zoos

Ziyu Li, Henk Kant, Rihan Hai, Asterios Katsifodimos, Alessandro Bozzon

Abstract: Machine learning (ML) researchers and practitioners are building repositories of pre-trained models, called model zoos. These model zoos contain metadata that detail various properties of the ML models and datasets, which are useful for reporting, auditing, reproducibility, and interpretability. Unfortunately, the existing metadata representations come with limited expressivity and lack of standardization. Meanwhile, an interoperable method to store and query model zoo metadata is missing. These two gaps hinder model search, reuse, comparison, and composition. In this demo paper, we advocate for standardized ML model metadata representation, proposing Macaroni, a metadata search engine with toolkits that support practitioners to obtain and enrich that metadata.