metadata

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 dataset with 2000 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

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

Dataset Sources [optional]

Repository: https://github.com/hendrycks/natural-adv-examples
Paper: https://arxiv.org/abs/1907.07174

Citation

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}
}