---
license: cc-by-2.0
task_categories:
- object-detection
language:
- en
tags:
- traffic
- trafficsigns
- streetview
- yolo
---


# TT100K Dataset

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.

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.

This dataset is particularly valuable for:

- Autonomous driving systems
- Advanced driver assistance systems (ADAS)
- Traffic monitoring applications
- Urban planning and traffic analysis
- Computer vision research in real-world conditions


![sample_with_bboxes](https://cdn-uploads.huggingface.co/production/uploads/60f6ff297666eeb11bc2b8d7/TLLyjmgoY7BTt5_WjIUSO.png)

## Key Features

The TT100K dataset provides several key advantages:

- **Scale**: 100,000 high-resolution images (2048×2048 pixels)
- **Diversity**: 221 traffic sign categories covering Chinese traffic signs
- **Real-world conditions**: Large variations in weather, illumination, and viewing angles
- **Rich annotations**: Each sign includes class label, bounding box, and pixel mask
- **Comprehensive coverage**: Includes prohibitory, warning, mandatory, and informative signs
- **Train/Test split**: Pre-defined splits for consistent evaluation

## Dataset Structure

The TT100K dataset is split into three subsets:

1. **Training Set**:
   The primary collection of traffic-scene images used to train models for detecting and classifying different types of traffic signs.
2. **Validation Set**:
   A subset used during model development to monitor performance and tune hyperparameters.
3. **Test Set**:
   A held-out collection of images used to evaluate the final model's ability to detect and classify traffic signs in real-world scenarios.

The TT100K dataset includes 221 traffic sign categories organized into several major groups:

**Speed Limit Signs (pl*, pm*)**

1. **pl\_**: Prohibitory speed limits (pl5, pl10, pl20, pl30, pl40, pl50, pl60, pl70, pl80, pl100, pl120)
2. **pm\_**: Minimum speed limits (pm5, pm10, pm20, pm30, pm40, pm50, pm55)

**Prohibitory Signs (p*, pn*, pr\_)**

1. **p1-p28**: General prohibitory signs (no entry, no parking, no stopping, etc.)
2. **pn/pne**: No entry and no parking signs
3. **pr**: Various restriction signs (pr10, pr20, pr30, pr40, pr50, etc.)

**Warning Signs (w\_)**

1. **w1-w66**: Warning signs for various road hazards, conditions, and situations
2. Includes pedestrian crossings, sharp turns, slippery roads, animals, construction, etc.

**Height/Width Limit Signs (ph*, pb*)**

1. **ph\_**: Height limit signs (ph2, ph2.5, ph3, ph3.5, ph4, ph4.5, ph5, etc.)
2. **pb\_**: Width limit signs

**Informative Signs (i*, il*, io, ip)**

1. **i1-i15**: General informative signs
2. **il\_**: Speed limit information (il60, il80, il100, il110)
3. **io**: Other informative signs
4. **ip**: Information plates

### Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("PrashantDixit0/TT-100K")

# Access splits
train_data = dataset['train']
val_data = dataset['val']
test_data = dataset['test']

# Example: Load first image
from PIL import Image
import io

sample = train_data[0]
image = Image.open(BytesIO(base64.b64decode(sample["image"]["bytes"]))
image.show()
```

### Citation

If you use this dataset, please cite the original TT-100K paper:

```bibtex
@inproceedings{zhu2016traffic,
  title={Traffic-sign detection and classification in the wild},
  author={Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2110--2118},
  year={2016}
}
```