Update README.md

README.md

@@ -32,8 +32,9 @@ dataset_info:
           '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
+          '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
@@ -49,9 +50,11 @@ dataset_info:
           '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
+          '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
+          '42': >-
+            white circle with gray strike bar indicating no passing for trucks
             has ended
   splits:
   - name: train
@@ -83,4 +86,135 @@ dataset_info:
     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
+
+- [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 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:
+
+```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
+
+[tang.anke@foxmail.com](mailto:tang.anke@foxmail.com)