YOLO Detection Study

This project documents my journey of learning and experimenting with YOLO object detection, starting from basic training to more advanced fine-tuning.

Project Structure

yolo-detection-study
 ├── scripts/         # Training and inference scripts
 ├── experiments/     # Markdown files documenting experiments
 ├── results/         # Output images, plots, and logs from experiments
 ├── configs/         # Dataset and model configuration files
 ├── requirements.txt # Project dependencies
 └── README.md

Experiments

Baseline: Vehicle Detection

I started with a baseline experiment to train a yolov8n model on a small vehicle dataset. This helped me understand the end-to-end training and evaluation process.

Key Results:

• Results Plot: Shows training metrics like loss and mAP.

• Confusion Matrix: Visualizes classification performance.

Note: The images above are placeholders. You should replace them with your actual experiment outputs.

How to Run

1. Setup Environment:

   pip install -r requirements.txt

2. Run Training: Modify the script scripts/train.py to point to your dataset configuration.

   python scripts/train.py