  In this work we tackle the challenging problem of anime character
recognition. Anime, referring to animation produced within Japan and work
derived or inspired from it. For this purpose we present DAF:re
(DanbooruAnimeFaces:revamped), a large-scale, crowd-sourced, long-tailed
dataset with almost 500 K images spread across more than 3000 classes.
Additionally, we conduct experiments on DAF:re and similar datasets using a
variety of classification models, including CNN based ResNets and
self-attention based Vision Transformer (ViT). Our results give new insights
into the generalization and transfer learning properties of ViT models on
substantially different domain datasets from those used for the upstream
pre-training, including the influence of batch and image size in their
training. Additionally, we share our dataset, source-code, pre-trained
checkpoints and results, as Animesion, the first end-to-end framework for
large-scale anime character recognition: https://github.com/arkel23/animesion
