AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images
Abstract
In order to classify Japanese animation-style character faces, this paper attempts to delve further into the many models currently available, including InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNet, employing transfer learning. This paper demonstrates that EfficientNet-B7, which achieves a top-1 accuracy of 85.08%, has the highest accuracy rate. MobileNetV2, which achieves a less accurate result with a top-1 accuracy of 81.92%, benefits from a significantly faster inference time and fewer required parameters. However, from the experiment, MobileNet-V2 is prone to overfitting; EfficienNet-B0 fixed the overfitting issue but with a cost of a little slower in inference time than MobileNet-V2 but a little more accurate result, top-1 accuracy of 83.46%. This paper also uses a few-shot learning architecture called Prototypical Networks, which offers an adequate substitute for conventional transfer learning techniques.
Keywords
Cite
@article{arxiv.2208.11012,
title = {AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images},
author = {Martinus Grady Naftali and Jason Sebastian Sulistyawan and Kelvin Julian},
journal= {arXiv preprint arXiv:2208.11012},
year = {2023}
}
Comments
11 pages, 26 figures, 8 tables