Project Directory Map

This document serves as the “Atlas” for the Drowsiness Detection project. It maps every file in the source code to its purpose and its corresponding documentation page.

🗺️ Source Code Architecture

The project is organized into a “Pipeline” structure: Data $\to$ Training $\to$ Inference.

graph TD
    Data[data.yaml] -->|Defines| Train[train_yolo.py]
    Train -->|Produces| Model[models/*.pt]
    Model -->|Powers| Infer[drowsiness_detection.py]
    Infer -->|Triggers| Alert[Alerts: Sound/Notification]
    Req[requirements.txt] -->|Supports| All[All Scripts]

📄 Detailed File Index

File / Folder	Technical Role	Beginner’s Explanation	Documentation Link
data.yaml	Configuration	Tells the AI where the images are.	dataset
train_yolo.py	Trainer	The script that teaches the AI.	model-training
drowsiness_detection.py	Engine	The real-time monitoring script.	detection-system
models/	Weight Storage	Stores the “learned” AI brain (.pt files).	architecture
requirements.txt	Manifest	List of all needed software libraries.	environment
runs/	Artifacts	Stores training logs and accuracy charts.	model-training
README.md	Overview	Quick-start guide.	index

📚 Documentation Hierarchy

The documentation is structured to take you from “Curious” to “Expert”:

1. The Big Picture $\to$ index.md
2. The Setup $\to$ configuration/environment.md
3. The AI Logic $\to$ models/architecture.md $\to$ training/dataset.md $\to$ training/model-training.md
4. The Real-time Engine $\to$ inference/detection-system.md $\to$ inference/alert-mechanism.md
5. Fine Tuning $\to$ configuration/hyperparameters.md

---