WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges
This dataset provides a resource for researchers in computer vision and fine-grained classification, though it is incremental as it builds on existing dataset creation efforts.
The authors introduced WikiChurches, a dataset of 9,485 church images with architectural style labels from Wikipedia, designed to benchmark fine-grained classification by addressing challenges like small sample size, class imbalance, and viewpoint variance, and included 631 bounding box annotations for 139 churches to aid in feature-based research.
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.5166987