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Dataset Card for ImageNet-O
This is a FiftyOne dataset with 2000 samples.
The recipe notebook for creating this dataset can be found here.
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}
}
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