This repository contains a YOLO classification model fine-tuned to detect driver drowsiness from images. The model classifies input images into two categories: Drowsy and Non Drowsy (Awake).
This model was trained using the ultralytics framework and demonstrates high performance on an unseen test set, making it a reliable tool for safety applications.
Model Details
Base Model:yolo11x-cls (from the Ultralytics v8 ecosystem)
Fine-tuned on: A combined dataset for driver drowsiness detection.
Classes:Drowsy, Non Drowsy
Framework: PyTorch, Ultralytics
How to Get Started
You can easily use this model with the ultralytics library.
python
1# Install ultralytics2!pip install ultralytics
34from ultralytics import YOLO
56# Load the model from the Hugging Face Hub7model = YOLO('your-username/your-repo-name')89# Run inference on an image10image_path ='path/to/your/image.jpg'11results = model.predict(image_path)1213# Print the top prediction14probs = results[0].probs
15top1_class_index = probs.top1
16top1_confidence = probs.top1conf
17class_name = model.names[top1_class_index]1819print(f"Prediction: {class_name} with confidence {top1_confidence:.4f}")
Training Procedure
The model was fine-tuned on a large dataset of driver images. The training process involved:
Data Augmentation: Standard augmentations like random flips, color jitter (HSV), and scaling were applied.
Transfer Learning: The model was initialized with weights pretrained on a large-scale dataset, enabling rapid convergence.
Key Hyperparameters
Image Size: 224x224
Batch Size: 185 (auto-tuned)
Optimizer: SGD with momentum
Training Results
Evaluation
The model was evaluated on a completely unseen test set to ensure a fair assessment of its generalization capabilities.
Key Performance Metrics
Metric
Value
Description
Accuracy
99.80%
Overall correctness on the test set.
APCER
0.00%
Rate of 'Drowsy' drivers missed (False Negatives).
BPCER
0.41%
Rate of 'Non Drowsy' drivers flagged (False Positives).
ACER
0.21%
Average of APCER and BPCER.
APCER (Attack Presentation Classification Error Rate) is the most critical safety metric.
Confusion Matrix
Model Explainability (Grad-CAM)
To ensure the model is focusing on relevant facial features, Grad-CAM was used. The heatmaps confirm that the model's predictions are primarily based on the eye and mouth regions, as expected.
Grad-CAM
Intended Use and Limitations
This model is intended as a proof-of-concept for driver safety systems. It should not be used as the sole mechanism for preventing accidents. Real-world performance may vary based on lighting conditions, camera angles, occlusions (e.g., sunglasses), and individual differences.
