We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| arkel23/Danbooru2018AnimeCharacterRecognitionDataset_Revamped | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| read_single | Ran | arkel23/Danbooru2018AnimeCharacterRecognitionDataset_Revamped/support/tags_prepare/combine_metadata.py pointer only (licence: NONE) · get_code("036caaafef7f7f4e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2101.08674")
get_code_for_paper("2101.08674")
have("2101.08674")
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