gtsrb

# Dataset Card for German Traffic Sign Recognition Benchmark

This dataset contains images of 43 classes of traffic signs. It is intended for developing and benchmarking traffic sign recognition systems.

## Dataset Details

### Dataset Description

The German Traffic Sign Recognition Benchmark (GTSRB) is a multi-class classification dataset featuring 43 classes of traffic signs.
The images were cropped from a larger set of images to focus on the traffic sign and eliminate background.
Multiple data augmentations such as Gaussian noise, motion blur, contrast changes, etc. are provided as additional test sets to benchmark model robustness.

### Dataset Sources

- [Paper with code](https://paperswithcode.com/dataset/gtsrb)

## Uses

### Direct Use

```python
from datasets import load_dataset

dataset = load_dataset('tanganke/gtsrb')
```

## Dataset Structure

The dataset is provided in 9 splits, including training data and clean test data:

- train: 26,640 images
- test: 12,630 images

and 7 kinds of corrupted test datasets to evaluate the robustness:

- contrast: 12,630 contrast-adjusted test images
- gaussian_noise: 12,630 Gaussian noise augmented test images
- impulse_noise: 12,630 impulse noise augmented test images
- jpeg_compression: 12,630 JPEG-compressed test images
- motion_blur: 12,630 motion-blurred test images
- pixelate: 12,630 pixelated test images
- spatter: 12,630 spatter augmented test images

Each split contains 43 classes of traffic signs, with the class labels and names specified in the dataset metadata.

## Citation [optional]

You can use any of the provided BibTeX entries for your reference list:

```bibtex
@article{stallkampManVsComputer2012,
title = {Man vs. Computer: {{Benchmarking}} Machine Learning Algorithms for Traffic Sign Recognition},
shorttitle = {Man vs. Computer},
author = {Stallkamp, J. and Schlipsing, M. and Salmen, J. and Igel, C.},
year = {2012},
month = aug,
journal = {Neural Networks},
series = {Selected {{Papers}} from {{IJCNN}} 2011},
volume = {32},
pages = {323--332},
issn = {0893-6080},
doi = {10.1016/j.neunet.2012.02.016},
url = {https://www.sciencedirect.com/science/article/pii/S0893608012000457},
keywords = {Benchmarking,Convolutional neural networks,Machine learning,Traffic sign recognition}
}

@misc{yangAdaMergingAdaptiveModel2023,
title = {{{AdaMerging}}: {{Adaptive Model Merging}} for {{Multi-Task Learning}}},
shorttitle = {{{AdaMerging}}},
author = {Yang, Enneng and Wang, Zhenyi and Shen, Li and Liu, Shiwei and Guo, Guibing and Wang, Xingwei and Tao, Dacheng},
year = {2023},
month = oct,
number = {arXiv:2310.02575},
eprint = {2310.02575},
primaryclass = {cs},
publisher = {arXiv},
doi = {10.48550/arXiv.2310.02575},
url = {http://arxiv.org/abs/2310.02575},
archiveprefix = {arxiv},
keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}

@misc{tangConcreteSubspaceLearning2023,
title = {Concrete {{Subspace Learning}} Based {{Interference Elimination}} for {{Multi-task Model Fusion}}},
author = {Tang, Anke and Shen, Li and Luo, Yong and Ding, Liang and Hu, Han and Du, Bo and Tao, Dacheng},
year = {2023},
month = dec,
number = {arXiv:2312.06173},
eprint = {2312.06173},
publisher = {arXiv},
url = {http://arxiv.org/abs/2312.06173},
archiveprefix = {arxiv},
copyright = {All rights reserved},
keywords = {Computer Science - Machine Learning}
}

@misc{tangMergingMultiTaskModels2024,
title = {Merging {{Multi-Task Models}} via {{Weight-Ensembling Mixture}} of {{Experts}}},
author = {Tang, Anke and Shen, Li and Luo, Yong and Yin, Nan and Zhang, Lefei and Tao, Dacheng},
year = {2024},
month = feb,
number = {arXiv:2402.00433},
eprint = {2402.00433},
primaryclass = {cs},
publisher = {arXiv},
doi = {10.48550/arXiv.2402.00433},
url = {http://arxiv.org/abs/2402.00433},
archiveprefix = {arxiv},
copyright = {All rights reserved},
keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}
```

## Dataset Card Authors

Anke Tang

## Dataset Card Contact

[[e-mail]](mailto:[e-mail])

# 德国交通标志识别基准数据集卡片（German Traffic Sign Recognition Benchmark，GTSRB）
本数据集收录了43类交通标志的图像，旨在用于开发与评测交通标志识别系统。

## 数据集详情
### 数据集概述
德国交通标志识别基准（German Traffic Sign Recognition Benchmark，GTSRB）是一个多分类数据集，涵盖43类交通标志。所有图像均从更大尺寸的图像集中裁剪得到，仅保留交通标志主体并移除背景。此外，数据集还提供了高斯噪声、运动模糊、对比度调整等多种数据增强手段的额外测试集，用于评测模型的鲁棒性。

### 数据集来源
- [论文与代码](https://paperswithcode.com/dataset/gtsrb)

## 用途
### 直接使用
python
from datasets import load_dataset

dataset = load_dataset('tanganke/gtsrb')

## 数据集结构
本数据集共分为9个拆分集，包含训练数据与干净测试数据：
- train：26640张图像
- test：12630张图像
以及7种用于评估模型鲁棒性的损坏测试数据集：
- contrast：12630张经过对比度调整的测试图像
- gaussian_noise：12630张添加了高斯噪声增强的测试图像
- impulse_noise：12630张添加了脉冲噪声增强的测试图像
- jpeg_compression：12630张经过JPEG压缩的测试图像
- motion_blur：12630张带有运动模糊的测试图像
- pixelate：12630张经过像素化处理的测试图像
- spatter：12630张添加了溅污增强效果的测试图像

每个拆分集均包含43类交通标志，类别标签与名称已在数据集元数据中完成指定。

## 引用（可选）
您可在参考文献列表中使用以下任意BibTeX条目：
bibtex
@article{stallkampManVsComputer2012,
title = {Man vs. Computer: {{Benchmarking}} Machine Learning Algorithms for Traffic Sign Recognition},
shorttitle = {Man vs. Computer},
author = {Stallkamp, J. and Schlipsing, M. and Salmen, J. and Igel, C.},
year = {2012},
month = aug,
journal = {Neural Networks},
series = {Selected {{Papers}} from {{IJCNN}} 2011},
volume = {32},
pages = {323--332},
issn = {0893-6080},
doi = {10.1016/j.neunet.2012.02.016},
url = {https://www.sciencedirect.com/science/article/pii/S0893608012000457},
keywords = {Benchmarking,Convolutional neural networks,Machine learning,Traffic sign recognition}
}

@misc{yangAdaMergingAdaptiveModel2023,
title = {{{AdaMerging}}: {{Adaptive Model Merging}} for {{Multi-Task Learning}}},
shorttitle = {{{AdaMerging}}},
author = {Yang, Enneng and Wang, Zhenyi and Shen, Li and Liu, Shiwei and Guo, Guibing and Wang, Xingwei and Tao, Dacheng},
year = {2023},
month = oct,
number = {arXiv:2310.02575},
eprint = {2310.02575},
primaryclass = {cs},
publisher = {arXiv},
doi = {10.48550/arXiv.2310.02575},
url = {http://arxiv.org/abs/2310.02575},
archiveprefix = {arxiv},
keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}

@misc{tangConcreteSubspaceLearning2023,
title = {Concrete {{Subspace Learning}} Based {{Interference Elimination}} for {{Multi-task Model Fusion}}},
author = {Tang, Anke and Shen, Li and Luo, Yong and Ding, Liang and Hu, Han and Du, Bo and Tao, Dacheng},
year = {2023},
month = dec,
number = {arXiv:2312.06173},
eprint = {2312.06173},
publisher = {arXiv},
url = {http://arxiv.org/abs/2312.06173},
archiveprefix = {arxiv},
copyright = {All rights reserved},
keywords = {Computer Science - Machine Learning}
}

@misc{tangMergingMultiTaskModels2024,
title = {Merging {{Multi-Task Models}} via {{Weight-Ensembling Mixture}} of {{Experts}}},
author = {Tang, Anke and Shen, Li and Luo, Yong and Yin, Nan and Zhang, Lefei and Tao, Dacheng},
year = {2024},
month = feb,
number = {arXiv:2402.00433},
eprint = {2402.00433},
primaryclass = {cs},
publisher = {arXiv},
doi = {10.48550/arXiv.2402.00433},
url = {http://arxiv.org/abs/2402.00433},
archiveprefix = {arxiv},
copyright = {All rights reserved},
keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}

## 数据集卡片作者
Anke Tang

## 数据集卡片联系人
[[e-mail]](mailto:[e-mail])

搜集汇总

背景与挑战

背景概述

GTSRB数据集是一个包含43类交通标志图像的多分类数据集，旨在开发和评估交通标志识别系统。数据集提供训练和清洁测试数据，以及7种损坏测试数据以评估模型鲁棒性。