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license: mit
tags:
- image-classification
- computer-vision
- imagenet-c
---
# Nano ImageNet-C (Severity 5)
This is a randomly sampled subset of the ImageNet-C dataset, containing 5,000 images exclusively from corruption **severity level 5**. It is designed for efficient testing and validation of model robustness.
这是一个从 ImageNet-C 数据集中随机抽样的子集，包含 5000 张仅来自损坏等级为 **5** 的图像。它旨在用于高效地测试和验证模型的鲁棒性。
## How to Generate / 如何生成
This dataset was generated using the `create_nano_dataset.py` script included in this repository. To ensure reproducibility, the following parameters were used:
本数据集使用此仓库中包含的 `create_nano_dataset.py` 脚本生成。为确保可复现性，生成时使用了以下参数：
- **Source Dataset / 源数据集**: The full ImageNet-C dataset is required. / 需要完整的 ImageNet-C 数据集。
- **Random Seed / 随机种子**: `7600`
- **Python Version / Python 版本**: `3.10.14`
## Dataset Structure / 数据集结构
The dataset is provided as a single `.tar` file named `nano-imagenet-c.tar` in the `webdataset` format. The internal structure preserves the original ImageNet-C hierarchy: `corruption_type/class_name/image.jpg`.
数据集以 `webdataset` 格式打包在名为 `nano-imagenet-c.tar` 的单个 `.tar` 文件中。其内部结构保留了原始 ImageNet-C 的层次结构：`corruption_type/class_name/image.jpg`。
## Citation / 引用
If you use this dataset, please cite the original ImageNet-C paper:
如果您使用此数据集，请引用原始 ImageNet-C 的论文：
```bibtex
@inproceedings{danhendrycks2019robustness,
title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
author={Dan Hendrycks and Thomas Dietterich},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=HJz6tiCqYm},
}
```
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