Danbooru2021 is a large-scale anime image database with 4.9m+ images annotated with 162m+ tags; it can be useful for machine learning purposes such as image recognition and generation.
Deep learning for computer revision relies on large annotated datasets. Classification/categorization has benefited from the creation of ImageNet, which classifies 1m photos into 10001,024ya categories. But classification/categorization is a coarse description of an image which limits application of classifiers, and there is no comparably large dataset of images with many tags or labels which would allow learning and detecting much richer information about images. Such a dataset would ideally be >1m images with at least 10 descriptive tags each which can be publicly distributed to all interested researchers, hobbyists, and organizations. There are currently no such public datasets, as ImageNet, Birds, Flowers, and MS COCO fall short either on image or tag count or restricted distribution. I suggest that the “image -boorus” be used. The image boorus are long-standing web databases which host large numbers of images which can be ‘tagged’ or labeled with an arbitrary number of textual descriptions; they were developed for and are most popular among fans of anime, who provide detailed annotations. The best known booru, with a focus on quality, is Danbooru.
We create Danbooru2021 covering Danbooru uploads 2005-05-24–16y2021-12-31 (final ID: #5,020,995),
Danbooru20xx datasets have been extensively used in projects & machine learning research.
Our hope is that the Danbooru2021 dataset can be used for rich large-scale classification/tagging & learned embeddings, test out the transferability of existing computer vision techniques (primarily developed using photographs) to illustration/anime-style images, provide an archival backup for the Danbooru community, feed back metadata improvements & corrections, and serve as a testbed for advanced techniques such as conditional image generation or style transfer.