🚀 YOLOv11: Local Training on Traffic Sign Dataset with CUDA Acceleration

High-performance and accurate traffic sign detection using YOLOv11, supporting real-time image & video processing with CUDA acceleration. Train locally using Kaggle's Traffic Sign dataset and fine-tune with YOLOv11s.pt pre-trained model.

---

✨ Features

✔️ Train & Test on Traffic Sign Dataset (Kaggle)
✔️ Real-time Image & Video Detection
✔️ CUDA Accelerated Inference
✔️ AMP (Automatic Mixed Precision) Enabled
🚧 TensorRT Optimization (In Development)

---

📦 Installation

Ensure you have Python, PyTorch, and CUDA installed.
My testing platform : Python 3.10+ , torch 2.6.0+cu126, CUDA 12.8

# Clone the repository
git clone https://github.com/j89103138/YOLOv11-Traffic-Sign.git
cd YOLOv11-Traffic-Sign

# Install dependencies
pip install -r requirements.txt

You can find dataset to download here on Kaggle: https://www.kaggle.com/datasets/pkdarabi/cardetection/data

---

🔧 Usage

🏋️ Train a New Model

If you want to train the model from using pretrained yolo model, using the Traffic Sign Dataset, run:

python train.py
#you have to set some path manually

📊 Validate Model Performance

To evaluate the trained model on the validation dataset, run:

python val.py
#you have to set some path manually

🎥 Image & Video Detection

Run YOLOv11 on images or videos:

python detect.py
#you have to set some path manually

For live webcam detection, use:

python webcam.py
#you have to set some path manually

---

⚙️ Project Structure

📂 YOLOv11-Traffic-Sign
 ┣ 📂 archive              # special thanks Kaggle:pkdarabi 's dataset
 ┣ 📂 assets               #gif and images
 ┣ 📄 train.py             # Training model
 ┣ 📄 val.py               # Validation model
 ┣ 📄 detect.py            # Inference on images/videos
 ┣ 📄 webcam.py            # Live webcam detection
 ┣ 📄 checkmodeltype.py    # check model type ( would be useful if you're on different type of model)
 ┣ 📄 read_results.py      # use pandas for easy summarize model performance
 ┣ 📄 benchmark.py         # for benchmarking by epochs and times
 ┣ 📄 README.md            # Project documentation
 ┗ 📄 LICENSE.md           # License

---

🔍 Performance Metrics

---

🤝 Contributing

We welcome contributions! To contribute, follow these steps:

1. Fork the repo
2. Create a new branch (git checkout -b feature-xxx)
3. Commit changes (git commit -m "Add new feature")
4. Push to GitHub (git push origin feature-xxx)
5. Submit a Pull Request

---

📜 License

This project is licensed under the CC BY 4.0 LICENSE. See LICENSE for details.

Creative Commons Attribution 4.0 International (CC BY 4.0)

2025 Shawn Liu 

This work is licensed under the Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

For more details, see https://creativecommons.org/licenses/by/4.0/

---

📬 Contact

📧 Email: j89103138@gmail.com
🌐 GitHub: github.com/j89103138

---