Semantics-aware dataset for the mono-label supervised classification of animals

This image dataset has been derived from Wikimedia Commons (https://commons.wikimedia.org), a large-scale free and collaborative media repository currently including over 92 million items including images, videos and recordings. The dataset includes images of several types of animals. The images are assigned to categories according to the Wikimedia Commons Category Graph and its direct links with the considered medias. As an output of the development of this image dataset, we created three ZIP Files that can be used for the evaluation of the effect of semantic features of considered labels on the efficiency of mono-label image classification algorithms : Class1 (The most general one): The labels are "Birds" and "mammals". Each category precisely includes 450 images. Class2: The labels are "Cat", "Cattle", "Columbidae", "Dog", "Phoenicopteridae", and "Psittacidae". Each category precisely includes 150 images. Class3 (The most granular one): The labels here are "Highland cattle", "‎Holstein Friesian cattle", "Charolais cattle‎", "American Pit Bull Terrier", "‎Belgian Shepherd Malinois", "‎Miniature Schnauzer‎", "Persian cats", "‎Chinchilla (cat)", "‎Bengal cats", "‎Phoeniconaias minor‎", "Phoenicopterus roseus‎", "Phoenicoparrus andinus", "‎Anodorhynchus hyacinthinus‎", "Ara ararauna‎", "Ara macao‎", "Ocyphaps lophotes", "Columbina talpacoti", and "Columba palumbus"‎. Each category precisely includes 50 images.This dataset has been developed to support a forthcoming proceedings paper in KDD 2021. Image Credits: Wikimedia Commons, CC-BY-SA 4.0