We introduce JONES-19, a high-quality image dataset documenting 1,901 ornament designs belonging to nineteen human cultures. The images and their annotations are based on an open access archive of The Grammar of Ornament (London, 1856), by Owen Jones. The dataset poses numerous challenges as a benchmark for computer vision classification tasks and for research at the intersection of machine learning and art and design: a small sample size, image samples of human-designed artifacts rather than common objects in-context or natural scenes, imbalanced class distribution, and image distinctions based on fine details involving line patterns, reliefs, and colors. As a design-inspired dataset, JONES-19 can serve as a benchmark for various research fields, such as visual recognition, data-efficient learning, art-historical research, architectural style analysis, and cultural heritage. This paper describes the curation of the JONES-19 dataset and reports a baseline classification benchmark that evaluates the suitability of the dataset for training classifiers and exposes insights into inter-class relationships–particularly cultural similarities–by examining patterns in misclassification errors. The dataset and its accompanying documentation are available at: https://huggingface.co/datasets/harvardseas-cultural-ornaments/JONES-19.
