Datasets:

Voxel51
/

ImageNet-A

@@ -60,10 +60,7 @@ dataset_summary: '
 # Dataset Card for ImageNet-A
-<!-- Provide a quick summary of the dataset. -->
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7450 samples.
@@ -95,130 +92,53 @@ session = fo.launch_app(dataset)
 ### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-## Uses
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-### Direct Use
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-### Out-of-Scope Use
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-## Dataset Structure
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-## Dataset Creation
-### Curation Rationale
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-### Source Data
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-#### Data Collection and Processing
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-#### Who are the source data producers?
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-### Annotations [optional]
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-#### Annotation process
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-#### Personal and Sensitive Information
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-## Bias, Risks, and Limitations
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-### Recommendations
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-## Citation [optional]
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 # Dataset Card for ImageNet-A
+![image](ImageNet-A.gif)
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7450 samples.
 ### Dataset Description
+ImageNet-A is a dataset of adversarially filtered images that reliably fool current ImageNet classifiers.
+It contains natural, unmodified real-world examples that transfer to various unseen ImageNet models, demonstrating that these models share weaknesses with adversarially selected images. These images cause consistent classification mistakes across various models.
+To create ImageNet-A, the authors first downloaded numerous images related to an ImageNet class. They then deleted the images that fixed ResNet-50 classifiers correctly predicted.
+With the remaining incorrectly classified images, the authors manually selected visually clear images.
+The resulting ImageNet-A dataset has ~7,500 adversarially filtered images.
+The ImageNet-A dataset enables testing image classification performance when the input data distribution shifts[1]. ImageNet-A can be used to measure model robustness to distribution shift using challenging natural images.
+- The images belong to 200 ImageNet classes selected to avoid overly fine-grained classes and classes with substantial overlap[1]. The classes span the most broad categories in ImageNet-1K.
+- To create ImageNet-A, the authors downloaded images related to the 200 classes from sources like iNaturalist, Flickr, and DuckDuckGo[1].
+- They then filtered out images that fixed ResNet-50 classifiers could correctly predict[1]. Images that fooled the classifiers were kept.
+- The authors manually selected visually clear, single-class images from the remaining incorrectly classified images to include in the final dataset[1].
+- The resulting dataset contains 7,500 natural, unmodified images that reliably transfer to and fool unseen models[1].
+Citations:
+[1] https://ar5iv.labs.arxiv.org/html/1907.07174
+- **Curated by:** Jacob Steinhardt, Dawn Song
+- **Funded by:** UC Berkeley
+- **Shared by:** [Harpreet Sahota](https://twitter.com/DataScienceHarp), Hacker-in-Residence at Voxel51
+- **Language(s) (NLP):** en
+- **License:** MIT
+### Dataset Sources [optional]
+- **Repository:** https://github.com/hendrycks/natural-adv-examples
+- **Paper:** https://ar5iv.labs.arxiv.org/html/1907.07174
+## Citation
 **BibTeX:**
+```bibtex
+@article{hendrycks2021nae,
+  title={Natural Adversarial Examples},
+  author={Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song},
+  journal={CVPR},
+  year={2021}
+}
+```