Datasets:
metadata
license: unknown
size_categories: 10K<n<100K
task_categories:
- image-classification
pretty_name: Rademacher noise
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 333318820
num_examples: 10000
download_size: 333386324
dataset_size: 333318820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Dataset Card for Rademacher noise for OOD Detection
Dataset Details
Dataset Description
- Original Dataset Authors: [More Information Needed]
- OOD Split Authors: Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
- Shared by: Eduardo Dadalto
- License: unknown
Dataset Sources
- Original Dataset Paper: [More Information Needed]
- First OOD Application Paper: http://arxiv.org/abs/1812.04606v3
Direct Use
This dataset is intended to be used as an ouf-of-distribution dataset for image classification benchmarks.
Out-of-Scope Use
This dataset is not annotated.
Curation Rationale
The goal in curating and sharing this dataset to the HuggingFace Hub is to accelerate research and promote reproducibility in generalized Out-of-Distribution (OOD) detection.
Check the python library detectors if you are interested in OOD detection.
Personal and Sensitive Information
Please check original paper for details on the dataset.
Bias, Risks, and Limitations
Please check original paper for details on the dataset.
Citation
BibTeX:
@software{detectors2023,
author = {Eduardo Dadalto},
title = {Detectors: a Python Library for Generalized Out-Of-Distribution Detection},
url = {https://github.com/edadaltocg/detectors},
doi = {https://doi.org/10.5281/zenodo.7883596},
month = {5},
year = {2023}
}
@article{1812.04606v3,
author = {Dan Hendrycks and Mantas Mazeika and Thomas Dietterich},
title = {Deep Anomaly Detection with Outlier Exposure},
year = {2018},
month = {12},
note = {ICLR 2019; PyTorch code available at
https://github.com/hendrycks/outlier-exposure},
archiveprefix = {arXiv},
url = {http://arxiv.org/abs/1812.04606v3}
}
Dataset Card Authors
Eduardo Dadalto