PrashantDixit0/TT-100K

TT100K Dataset

The Tsinghua-Tencent 100K (TT100K) 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

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

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:

@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}
}