README.md

---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- image-classification
task_ids: []
pretty_name: ImageNet-O
tags:
- fiftyone
- image
- image-classification
dataset_summary: '




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


  ## 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 Card for ImageNet-O

![image](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](twitter.com/datascienceharp), 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}
}
```