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Dataset . 2021
License: CC BY SA
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY SA
Data sources: ZENODO
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ZENODO
Dataset . 2021
License: CC BY SA
Data sources: Datacite
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WikiChurches – A Fine-Grained Dataset of Architectural Styles with Real-World Challenges

Authors: Barz, Björn; Denzler, Joachim;

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

Abstract

WikiChurches is a 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. Please refer to the README.md file for information about the different files contained in this dataset.

When using this dataset, please cite the following article: Björn Barz and Joachim Denzler. "WikiChurches: A Fine-Grained Dataset of Architectural Styles with Real-World Challenges." arXiv preprint arXiv:2108.06959, 2021.

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Keywords

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

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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