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

arXiv: https://arxiv.org/abs/2108.06959 · HTML: https://mlsanity.com/paper/2108.06959/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Deep Learning on Small Datasets without Pre-Training using Cosine Loss](https://mlsanity.com/paper/1901.09054/) (2019)
- [Integrating domain knowledge: using hierarchies to improve deep classifiers](https://mlsanity.com/paper/1811.07125/) (2018)
- [The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop](https://mlsanity.com/paper/2106.01364/) (2021)
- [Classification-Specific Parts for Improving Fine-Grained Visual Categorization](https://mlsanity.com/paper/1909.07075/) (2019)
- [Region-Based Image Retrieval Revisited](https://mlsanity.com/paper/1709.09106/) (2017)
- [Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge](https://mlsanity.com/paper/2010.06469/) (2020)
- [The Herbarium 2021 Half-Earth Challenge Dataset](https://mlsanity.com/paper/2105.13808/) (2021)

## Built on by (most cited first, 3 of 3)

- [Data Representativity for Machine Learning and AI Systems](https://mlsanity.com/paper/2203.04706/) (2022)
- [WinSyn: A High Resolution Testbed for Synthetic Data](https://mlsanity.com/paper/2310.08471/) (2023)
- [Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models](https://mlsanity.com/paper/2512.21337/) (2025)

## Abstract (arXiv)

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