WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges

Björn Barz, Joachim Denzler

Keywords: Architectural Style Recognition, Computer Vision, Image Dataset, Fine-Grained Visual Categorization, Fine-Grained Visual Recognition, Hierarchical Classification, Class Hierarchy, Data-efficient Deep Learning, Imprecise Labels, Imbalanced Classes

TL;DR: We introduce a dataset comprising images of church buildings, labeled with their architectural style and bounding boxes denoting distinctive architectural elements.

Abstract:

We introduce a novel dataset for architectural style classification, consisting of 9,485 images of church buildings. Both images and style labels were sourced from Wikipedia. The dataset can serve as a benchmark for various research fields, as it combines numerous real-world challenges: fine-grained distinctions between classes based on subtle visual features, a comparatively small sample size, a highly imbalanced class distribution, a high variance of viewpoints, and a hierarchical organization of labels, where only some images are labeled at the most precise level.
In addition, we provide 631 bounding box annotations of characteristic visual features for 139 churches from four major categories. These annotations can, for example, be useful for research on fine-grained classification, where additional expert knowledge about distinctive object parts is often available.
Images and annotations are available at: https://doi.org/10.5281/zenodo.5166986

URL: https://doi.org/10.5281/zenodo.5166986

Contribution Process Agreement: Yes

Dataset Url: https://doi.org/10.5281/zenodo.5166986

License: Creative Commons Attribution Share Alike 4.0 International

Author Statement: Yes