metadata
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': red and white circle 20 kph speed limit
'1': red and white circle 30 kph speed limit
'2': red and white circle 50 kph speed limit
'3': red and white circle 60 kph speed limit
'4': red and white circle 70 kph speed limit
'5': red and white circle 80 kph speed limit
'6': end / de-restriction of 80 kph speed limit
'7': red and white circle 100 kph speed limit
'8': red and white circle 120 kph speed limit
'9': red and white circle red car and black car no passing
'10': red and white circle red truck and black car no passing
'11': red and white triangle road intersection warning
'12': white and yellow diamond priority road
'13': red and white upside down triangle yield right-of-way
'14': stop
'15': empty red and white circle
'16': red and white circle no truck entry
'17': red circle with white horizonal stripe no entry
'18': red and white triangle with exclamation mark warning
'19': red and white triangle with black left curve approaching warning
'20': red and white triangle with black right curve approaching warning
'21': red and white triangle with black double curve approaching warning
'22': red and white triangle rough / bumpy road warning
'23': red and white triangle car skidding / slipping warning
'24': red and white triangle with merging / narrow lanes warning
'25': >-
red and white triangle with person digging / construction / road
work warning
'26': red and white triangle with traffic light approaching warning
'27': red and white triangle with person walking warning
'28': red and white triangle with child and person walking warning
'29': red and white triangle with bicyle warning
'30': red and white triangle with snowflake / ice warning
'31': red and white triangle with deer warning
'32': white circle with gray strike bar no speed limit
'33': blue circle with white right turn arrow mandatory
'34': blue circle with white left turn arrow mandatory
'35': blue circle with white forward arrow mandatory
'36': blue circle with white forward or right turn arrow mandatory
'37': blue circle with white forward or left turn arrow mandatory
'38': blue circle with white keep right arrow mandatory
'39': blue circle with white keep left arrow mandatory
'40': blue circle with white arrows indicating a traffic circle
'41': >-
white circle with gray strike bar indicating no passing for cars
has ended
'42': >-
white circle with gray strike bar indicating no passing for trucks
has ended
splits:
- name: train
num_bytes: 3190230
num_examples: 26640
- name: test
num_bytes: 1411339
num_examples: 12630
- name: contrast
num_bytes: 1512379
num_examples: 12630
- name: gaussian_noise
num_bytes: 1663939
num_examples: 12630
- name: impulse_noise
num_bytes: 1638679
num_examples: 12630
- name: jpeg_compression
num_bytes: 1714459
num_examples: 12630
- name: motion_blur
num_bytes: 1588159
num_examples: 12630
- name: pixelate
num_bytes: 1512379
num_examples: 12630
- name: spatter
num_bytes: 1487119
num_examples: 12630
download_size: 841108239
dataset_size: 15718682
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
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
Uses
Direct Use
from datasets import load_dataset
dataset = load_dataset('tanganke/gtsrb')
Dataset Structure
The dataset is provided in 10 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:
@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