detectors/rademacher-ood

---
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: 333318820.0
num_examples: 10000
download_size: 333386324
dataset_size: 333318820.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---

# Dataset Card for Rademacher noise for OOD Detection

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Original Dataset Authors**: [More Information Needed]
- **OOD Split Authors:** Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
- **Shared by:** Eduardo Dadalto
- **License:** unknown

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Original Dataset Paper:** [More Information Needed]
- **First OOD Application Paper:** http://arxiv.org/abs/1812.04606v3

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

This dataset is intended to be used as an ouf-of-distribution dataset for image classification benchmarks.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

This dataset is not annotated.

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

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](https://github.com/edadaltocg/detectors) if you are interested in OOD detection.

### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

Please check original paper for details on the dataset.

### Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Please check original paper for details on the dataset.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```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

## Dataset Card Contact

https://huggingface.co/edadaltocg

原始信息汇总

数据集卡片概述

数据集详情

数据集描述

• 数据集名称: Rademacher noise
• 数据集用途: 用于图像分类基准的分布外数据集。
• 数据集作者:
  • 原始数据集作者: [需要更多信息]
  • 分布外数据集作者: Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
  • 共享者: Eduardo Dadalto
• 许可证: 未知

数据集来源

• 原始数据集论文: [需要更多信息]
• 首次分布外应用论文: http://arxiv.org/abs/1812.04606v3

数据集结构

• 特征:
  • 名称: image
  • 数据类型: image
• 分割:
  • 名称: train
  • 字节数: 333318820.0
  • 样本数: 10000
• 下载大小: 333386324
• 数据集大小: 333318820.0
• 配置:
  • 配置名称: default
  • 数据文件:
    • 分割: train
    • 路径: data/train-*

数据集使用

• 直接使用: 该数据集旨在用作图像分类基准的分布外数据集。
• 超出范围使用: 该数据集未标注。

数据集创建动机

• 创建目的: 旨在加速研究并促进广义分布外检测的可重复性。

个人和敏感信息

• 信息描述: 请参考原始论文了解数据集的详细信息。

偏差、风险和限制

• 限制描述: 请参考原始论文了解数据集的详细信息。

引用

• BibTeX:

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} }

数据集卡片作者

• Eduardo Dadalto

数据集卡片联系

• https://huggingface.co/edadaltocg