EUFCC-340K: A faceted hierarchical dataset for metadata annotation in GLAM collections

Abstract

In this paper, we address the challenges of automatic metadata annotation in the domain of Galleries, Libraries, Archives, and Museums (GLAMs) by introducing a novel dataset, EUFCC-340K, collected from the Europeana portal. Comprising over 340,000 images, the EUFCC-340K dataset is organized across multiple facets – Materials, Object Types, Disciplines, and Subjects – following a hierarchical structure based on the Art & Architecture Thesaurus (AAT). We developed several baseline models, incorporating multiple heads on a ConvNeXT backbone for multi-label image tagging on these facets, and fine-tuning a CLIP model with our image-text pairs. Our experiments to evaluate model robustness and generalization capabilities in two different test scenarios demonstrate the dataset’s utility in improving multi-label classification tools that have the potential to alleviate cataloging tasks in the cultural heritage sector. The EUFCC-340K dataset is publicly available at https://github.com/cesc47/EUFCC-340K.