Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
< 1K
ArXiv:
File size: 2,894 Bytes
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---
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
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}
}
```
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