A Multimodal Dataset of 18th-Century Prints for Segmentation and Analysis of Rocaille Ornaments

This is the dataset introduced and described in the paper "A Multimodal Dataset of 18th-Century Prints for Segmentation and Analysis of Rocaille Ornaments" (Hudcovic et al.): We present a multimodal dataset for the computational study of 18th-century Rococo ornamental prints. Rocaille, a characteristic ornament of the period, exhibits a continuously transforming morphology that has historically resisted systematic formal description. The dataset comprises 1,611 high-resolution digitizations from major German collections, accompanied by expert-curated bilingual metadata and commentary describing iconography and ornamental morphology. A subset of 229 images includes pixel-accurate segmentation masks distinguishing the structural volutes and comb-like extensions of the Rocaille. The images, structured metadata, textual descriptions, and segmentation annotations together support computational analyses ranging from visual multimodal retrieval and semantic segmentation to morphological analysis. As technical validation, we train two standard vision backbones for segmentation, showing that the annotations provide a learnable segmentation signal and that pre-training on both external and on our dataset can support downstream learning. Code, data splits, trained model weights, and evaluation outputs are provided to facilitate reproducible use of the resource. The dataset provides a foundation for cross-disciplinary research at the intersection of art history, digital humanities, and computer vision. Due to file limit reasons, the top-level folders were zipped. Each ZIP-file in the dataset corresponds to a top level folder, as detailed in the image folder_structure.jpg.Our paper describing and showing the viability of our dataset can be found here: [TODO] The associated code and neural networks can be found at our codebase: https://github.com/hudo259/Rocaille-Dataset-Technical-Validation-CodeThe associated experiment artifacts (model weights, logs, visualizations) can be found at: https://doi.org/10.5281/zenodo.22978360Citation:[TODO] Additionally, if you use this dataset, please also credit the following institutions from whose collections the data has been digitized: Image courtesy of the following collections: Staatsgalerie Stuttgart, Graphische Sammlung; Staats- und Stadtbibliothek Augsburg, Graphische Sammlung; Staatliche Graphische Sammlung München.