Datasets › Stylized ImageNet

Stylized ImageNet

Introduced by Robert Geirhos et al. in ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness1 Jan 2019 archive 2025-07-28

The Stylized-ImageNet dataset is created by removing local texture cues in ImageNet while retaining global shape information on natural images via AdaIN style transfer. This nudges CNNs towards learning more about shapes and less about local textures.

Source: Adversarial Examples Improve Image Recognition Image Source: https://github.com/rgeirhos/Stylized-ImageNet

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Adversarial Robustness Stylized ImageNet DeiT-S (AdamW, Cosine) Accuracy 13.0 Are Transformers More Robust Than CNNs? ytongbai/ViTs-vs-CNNs 4 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 106. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Are Transformers More Robust Than CNNs? 1 4 10 Nov 2021 not harvested

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Unknown

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • Stylized ImageNet

1 variant name, as the archive lists them.

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