#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/ImageNet-O" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for ImageNet-O

![image](https://huggingface.co/datasets/Voxel51/ImageNet-O/resolve/main/ImageNet-O.png)

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2000 samples.

The recipe notebook for creating this dataset can be found [here](https://colab.research.google.com/drive/1ScN-30Q-1ssAwuQYIbZ453h0vo0SAhz8).

## Installation

If you haven’t already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/ImageNet-O")

# Launch the App
session = fo.launch_app(dataset)
```

## Dataset Details

### Dataset Description

The ImageNet-O dataset consists of images from classes not found in the standard ImageNet-1k dataset. It tests the robustness and out-of-distribution detection capabilities of computer vision models trained on ImageNet-1k.

Key points about ImageNet-O:

- Contains images from classes distinct from the 1,000 classes in ImageNet-1k
- Enables testing model performance on out-of-distribution samples, i.e. images that are semantically different from the training data
- Commonly used to evaluate out-of-distribution detection methods for models trained on ImageNet
- Reported using the Area Under the Precision-Recall curve (AUPR) metric
- Manually annotated, naturally diverse class distribution, and large scale
- **Curated by:** Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song
- **Shared by:** [Harpreet Sahota](), Hacker-in-Residence at Voxel51
- **Language(s) (NLP):** en
- **License:** [MIT License](https://github.com/hendrycks/natural-adv-examples/blob/master/LICENSE)

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/hendrycks/natural-adv-examples
- **Paper:** https://arxiv.org/abs/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}
}
```