Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
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Update README.md
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README.md
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@@ -88,6 +88,8 @@ To create ImageNet-D, a large pool of synthetic images is generated by combining
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Experiments show that ImageNet-D reveals significant robustness gaps in current vision models[1]. The synthetic images transfer well to unseen models, uncovering common failure modes[1]. ImageNet-D provides a more diverse and challenging test set than prior synthetic benchmarks like ImageNet-C, ImageNet-9, and Stylized ImageNet[1].
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Citations:
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[1] https://arxiv.org/html/2403.18775v1
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Experiments show that ImageNet-D reveals significant robustness gaps in current vision models[1]. The synthetic images transfer well to unseen models, uncovering common failure modes[1]. ImageNet-D provides a more diverse and challenging test set than prior synthetic benchmarks like ImageNet-C, ImageNet-9, and Stylized ImageNet[1].
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The recipe notebook for creating this dataset can be found [here](https://colab.research.google.com/drive/1iiiXN8B36YhjtOH2PDbHevHTXH736It_?usp=sharing)
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Citations:
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[1] https://arxiv.org/html/2403.18775v1
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