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456efca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | # Highway Vehicle Detection - Team Instructions
## Quick Setup
1. **Clone the repository:**
```bash
git clone https://huggingface.co/bichuche0705/highway-vehicle-detection-code
cd highway-vehicle-detection-code
```
2. **Install dependencies:**
```bash
pip install ultralytics opencv-python numpy
```
3. **Run the application:**
```bash
python main.py
```
## External Resources
### Test Video
Watch the model in action: [YouTube Demo](https://www.youtube.com/watch?v=wqctLW0Hb_0&list=PLJKyZ_NuOhJQzif2-6-Kq9OiOj_UjJWvi)
### Main Dataset
Download the complete training dataset: [Kaggle Dataset](https://www.kaggle.com/datasets/sakshamjn/vehicle-detection-8-classes-object-detection/data)
## What's Included
- **Trained Models**: Ready-to-use YOLOv8m models
- **Source Code**: Complete detection application
- **Fine-tuning Dataset**: 92 images used for model improvement
- **Training Logs**: Performance metrics and visualizations
- **Documentation**: Complete project documentation
## Model Files
- `models/yolov8m_stage2_improved_best.pt` - Final model (recommended)
- `models/yolov8m_stage1_smart_best.pt` - Stage 1 model
## Usage Examples
### Basic Detection
```python
from ultralytics import YOLO
model = YOLO('models/yolov8m_stage2_improved_best.pt')
results = model('image.jpg')
results[0].show()
```
### Video Processing
```python
results = model('video.mp4', save=True)
```
## Support
For questions or issues, contact the project author.
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