Have Fun Storming the Castle(s)!

Connor Anderson, Adam Teuscher, Elizabeth Anderson, Alysia Larsen, Josh Shirley, Ryan Farrell; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 3703-3712

In recent years, large-scale datasets, each typically tailored to a particular problem, have become a critical factor towards fueling rapid progress in the field of computer vision. This paper describes a valuable new dataset that should accelerate research efforts on problems such as fine-grained classification, instance recognition and retrieval, and geolocalization. The dataset is comprised of more than 2400 individual castles, palaces and fortresses from more than 90 countries. The dataset contains more than 770K images in total. This paper details the dataset's construction process, the characteristics including annotations such as location (geotagged latlong and country label), Google Maps link and estimated per-class and per-image difficulty. An experimental section provides baseline experiments for important vision tasks including classification, instance retrieval and geolocalization (estimating the global location from an image's visual appearance).