Abstract: In this work we present a large-scale dataset of Ukiyo-e woodblock prints. Unlike previous works and datasets in the artistic domain that primarily focus on western art, this paper explores this pre-modern Japanese art form with the aim of broadening the scope for stylistic analysis and to provide a benchmark to evaluate a variety of art focused Computer Vision approaches. Our dataset consists of over 175.000 prints with corresponding metadata (eg. the artist, era, and creation date) ranging from the 17th century to present day. By approaching stylistic analysis as a Multi-Task learning problem we aim to more efficiently utilize the available metadata, and learn more general representations of style. We show results for a variety of well-known and reliable baselines to enable future comparison, and to encourage stylistic analysis on this artistic domain.