docs/en/datasets/detect/tt100k.md
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 annotation categories. The original paper applies a 100-instance threshold per class for supervised training, yielding a commonly used 45-class subset; however, the provided Ultralytics dataset configuration retains all 221 annotated categories, many of which are very sparse. 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:
The TT100K dataset provides several key advantages:
The TT100K dataset is split into three subsets:
The TT100K dataset includes 221 traffic sign categories organized into several major groups:
Speed Limit Signs (pl, pm)**
Prohibitory Signs (p, pn, pr_)**
Warning Signs (w_)
Height/Width Limit Signs (ph, pb)**
Informative Signs (i, il, io, ip)**
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. For the TT100K dataset, the TT100K.yaml file includes automatic download and conversion functionality.
!!! example "ultralytics/cfg/datasets/TT100K.yaml"
```yaml
--8<-- "ultralytics/cfg/datasets/TT100K.yaml"
```
To train a YOLO26 model on the TT100K dataset for 100 epochs with an image size of 640, you can use the following code snippets. The dataset will be automatically downloaded and converted to YOLO format on first use.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model - dataset will auto-download on first run
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
# Dataset will auto-download and convert on first run
yolo detect train data=TT100K.yaml model=yolo26n.pt epochs=100 imgsz=640
```
Here are typical examples from the TT100K dataset:
The dataset includes:
If you use the TT100K dataset in your research or development work, please cite the following paper:
!!! quote ""
=== "BibTeX"
```bibtex
@InProceedings{Zhu_2016_CVPR,
author = {Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},
title = {Traffic-Sign Detection and Classification in the Wild},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}
```
We would like to acknowledge the Tsinghua University and Tencent collaboration for creating and maintaining this valuable resource for the computer vision and autonomous driving communities. For more information about the TT100K dataset, visit the official dataset website.
The Tsinghua-Tencent 100K (TT100K) dataset is specifically designed for traffic sign detection and classification in real-world conditions. It's primarily used for:
With 100,000 diverse street view images and 221 traffic sign categories, it provides a comprehensive testbed for real-world traffic sign detection.
The TT100K dataset contains 221 different traffic sign categories, including:
This comprehensive coverage includes most traffic signs found in Chinese road networks.
To train a YOLO26n model on the TT100K dataset for 100 epochs with an image size of 640, use the example below.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo26n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="TT100K.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Start training from a pretrained *.pt model
yolo detect train data=TT100K.yaml model=yolo26n.pt epochs=100 imgsz=640
```
For detailed training configurations, refer to the Training documentation.
TT100K presents several unique challenges:
These challenges make TT100K a valuable benchmark for developing robust detection algorithms.
The TT100K dataset uses 2048×2048 pixel images, which can be resource-intensive. Here are recommended strategies:
For Training:
# Option 1: Resize to standard YOLO size
model.train(data="TT100K.yaml", imgsz=640, batch=16)
# Option 2: Use larger size for better small object detection
model.train(data="TT100K.yaml", imgsz=1280, batch=4)
# Option 3: Multi-scale training
model.train(data="TT100K.yaml", imgsz=640, scale=0.5) # trains at varying scales
Recommendations:
imgsz=640 for initial experimentsimgsz=1280 if you have sufficient GPU memory (24GB+)