NeuroDrive

NeuroDrive is a production-ready vehicle detection and classification system powered by YOLOv8. It identifies 8 distinct vehicle types with high accuracy, featuring a modern Streamlit interface for both real-time inference and batch processing.

🚀 Key Features

• Advanced Detection: Identifies Cars, Buses, Trucks, Motorcycles, Bicycles, Three-wheelers, Tractors, and Vans.
• Batch Processing: Automated analysis of image folders with dominant class categorization.
• Analytics Dashboard: Visual reports, class distribution charts, and downloadable CSV summaries.
• Professional UI: Glassmorphism-inspired design with dark mode and smooth animations.

🛠️ Tech Stack

• Model: YOLOv8 (Ultralytics) - Large/Medium backbones.
• Interface: Streamlit - Interactive web application.
• Processing: OpenCV & Pandas - Image manipulation and data analysis.
• Training: PyTorch - Custom training pipeline on 15k+ vehicle images.

🏁 Quick Start

1. Installation

Windows (PowerShell)

git clone https://github.com/yourusername/NeuroDrive.git
cd NeuroDrive
python -m venv venv
.\venv\Scripts\Activate
pip install -r requirements.txt

Linux / macOS

git clone https://github.com/yourusername/NeuroDrive.git
cd NeuroDrive
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Setup

The inference app lives in VehicleDetectionSystem. You need a trained model to run it.

1. Train a model (Optional, if you don't have one): Open training.ipynb and run the cells to train YOLOv8 on your dataset.

2. Deploy Model: Copy your best.pt to the app's model directory.

   Windows

   copy runs\detect\train\weights\best.pt VehicleDetectionSystem\models\best.pt

   Linux / macOS

   cp runs/detect/train/weights/best.pt VehicleDetectionSystem/models/best.pt

3. Run Application

Windows

cd VehicleDetectionSystem
pip install -r requirements.txt
streamlit run app.py

Linux / macOS

cd VehicleDetectionSystem
pip install -r requirements.txt
streamlit run app.py

Access the app at http://localhost:8501.

📁 Project Structure

NeuroDrive/
├── training.ipynb              # Reproducible training pipeline
├── requirements.txt            # Training dependencies
├── datasets/                   # (Ignored) Training data
├── runs/                       # (Ignored) Training artifacts
└── VehicleDetectionSystem/     # Main Application
    ├── app.py                  # Streamlit entry point
    ├── style.css               # Modern UI styling
    ├── models/                 # Inference weights
    └── utils/                  # Core detection logic

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a Pull Request.

📄 License

MIT License. See LICENSE for details.