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--- |
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annotations_creators: [] |
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language: en |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- image-classification |
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task_ids: [] |
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pretty_name: ImageNet-A |
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tags: |
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- fiftyone |
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- image |
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- image-classification |
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dataset_summary: ' |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7450 samples. |
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## Installation |
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If you haven''t already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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import fiftyone.utils.huggingface as fouh |
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# Load the dataset |
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# Note: other available arguments include ''max_samples'', etc |
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dataset = fouh.load_from_hub("Voxel51/ImageNet-A") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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' |
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--- |
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# Dataset Card for ImageNet-A |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7450 samples. |
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The recipe notebook for creating this FiftyOne Dataset can be found [here](https://colab.research.google.com/drive/1GpM0AEr8YolZxVF5tp0L94YIuT1WP-I7?usp=sharing). |
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## Installation |
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If you haven't already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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import fiftyone.utils.huggingface as fouh |
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# Load the dataset |
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# Note: other available arguments include 'max_samples', etc |
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dataset = fouh.load_from_hub("Voxel51/ImageNet-A") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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## Dataset Details |
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### Dataset Description |
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ImageNet-A is a dataset of adversarially filtered images that reliably fool current ImageNet classifiers. |
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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. |
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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. |
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With the remaining incorrectly classified images, the authors manually selected visually clear images. |
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The resulting ImageNet-A dataset has ~7,500 adversarially filtered images. |
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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. |
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- 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. |
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- To create ImageNet-A, the authors downloaded images related to the 200 classes from sources like iNaturalist, Flickr, and DuckDuckGo[1]. |
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- They then filtered out images that fixed ResNet-50 classifiers could correctly predict[1]. Images that fooled the classifiers were kept. |
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- The authors manually selected visually clear, single-class images from the remaining incorrectly classified images to include in the final dataset[1]. |
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- The resulting dataset contains 7,500 natural, unmodified images that reliably transfer to and fool unseen models[1]. |
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Citations: |
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[1] https://ar5iv.labs.arxiv.org/html/1907.07174 |
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- **Curated by:** Jacob Steinhardt, Dawn Song |
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- **Funded by:** UC Berkeley |
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- **Shared by:** [Harpreet Sahota](https://twitter.com/DataScienceHarp), Hacker-in-Residence at Voxel51 |
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- **Language(s) (NLP):** en |
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- **License:** MIT |
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### Dataset Sources [optional] |
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- **Repository:** https://github.com/hendrycks/natural-adv-examples |
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- **Paper:** https://ar5iv.labs.arxiv.org/html/1907.07174 |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@article{hendrycks2021nae, |
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title={Natural Adversarial Examples}, |
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author={Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song}, |
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journal={CVPR}, |
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year={2021} |
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} |
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``` |
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