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--- |
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license: cc-by-2.0 |
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task_categories: |
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- object-detection |
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language: |
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- en |
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tags: |
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- traffic |
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- trafficsigns |
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- streetview |
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- yolo |
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--- |
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# TT100K Dataset |
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The [Tsinghua-Tencent 100K (TT100K)](https://cg.cs.tsinghua.edu.cn/traffic-sign/) is a large-scale traffic sign benchmark dataset created from 100,000 Tencent Street View panoramas. This dataset is specifically designed for traffic sign detection and classification in real-world conditions, providing researchers and developers with a comprehensive resource for building robust traffic sign recognition systems. |
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The dataset contains **100,000 images** with over **30,000 traffic sign instances** across **221 different categories**. These images capture large variations in illuminance, weather conditions, viewing angles, and distances, making it ideal for training models that need to perform reliably in diverse real-world scenarios. |
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This dataset is particularly valuable for: |
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- Autonomous driving systems |
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- Advanced driver assistance systems (ADAS) |
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- Traffic monitoring applications |
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- Urban planning and traffic analysis |
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- Computer vision research in real-world conditions |
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## Key Features |
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The TT100K dataset provides several key advantages: |
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- **Scale**: 100,000 high-resolution images (2048×2048 pixels) |
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- **Diversity**: 221 traffic sign categories covering Chinese traffic signs |
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- **Real-world conditions**: Large variations in weather, illumination, and viewing angles |
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- **Rich annotations**: Each sign includes class label, bounding box, and pixel mask |
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- **Comprehensive coverage**: Includes prohibitory, warning, mandatory, and informative signs |
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- **Train/Test split**: Pre-defined splits for consistent evaluation |
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## Dataset Structure |
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The TT100K dataset is split into three subsets: |
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1. **Training Set**: |
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The primary collection of traffic-scene images used to train models for detecting and classifying different types of traffic signs. |
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2. **Validation Set**: |
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A subset used during model development to monitor performance and tune hyperparameters. |
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3. **Test Set**: |
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A held-out collection of images used to evaluate the final model's ability to detect and classify traffic signs in real-world scenarios. |
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The TT100K dataset includes 221 traffic sign categories organized into several major groups: |
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**Speed Limit Signs (pl*, pm*)** |
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1. **pl\_**: Prohibitory speed limits (pl5, pl10, pl20, pl30, pl40, pl50, pl60, pl70, pl80, pl100, pl120) |
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2. **pm\_**: Minimum speed limits (pm5, pm10, pm20, pm30, pm40, pm50, pm55) |
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**Prohibitory Signs (p*, pn*, pr\_)** |
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1. **p1-p28**: General prohibitory signs (no entry, no parking, no stopping, etc.) |
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2. **pn/pne**: No entry and no parking signs |
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3. **pr**: Various restriction signs (pr10, pr20, pr30, pr40, pr50, etc.) |
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**Warning Signs (w\_)** |
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1. **w1-w66**: Warning signs for various road hazards, conditions, and situations |
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2. Includes pedestrian crossings, sharp turns, slippery roads, animals, construction, etc. |
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**Height/Width Limit Signs (ph*, pb*)** |
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1. **ph\_**: Height limit signs (ph2, ph2.5, ph3, ph3.5, ph4, ph4.5, ph5, etc.) |
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2. **pb\_**: Width limit signs |
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**Informative Signs (i*, il*, io, ip)** |
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1. **i1-i15**: General informative signs |
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2. **il\_**: Speed limit information (il60, il80, il100, il110) |
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3. **io**: Other informative signs |
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4. **ip**: Information plates |
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### Usage |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("PrashantDixit0/TT-100K") |
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# Access splits |
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train_data = dataset['train'] |
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val_data = dataset['val'] |
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test_data = dataset['test'] |
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# Example: Load first image |
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from PIL import Image |
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import io |
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sample = train_data[0] |
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image = Image.open(BytesIO(base64.b64decode(sample["image"]["bytes"])) |
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image.show() |
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``` |
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### Citation |
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If you use this dataset, please cite the original TT-100K paper: |
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```bibtex |
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@inproceedings{zhu2016traffic, |
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title={Traffic-sign detection and classification in the wild}, |
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author={Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin}, |
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, |
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pages={2110--2118}, |
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year={2016} |
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} |
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``` |
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