State Farm Distracted Driver Detection Model
Model Description
This is a YOLOv8 classification model trained on the State Farm Distracted Driver Detection dataset. The model is designed to classify driver behavior into 10 distinct categories to detect distracted driving activities.
Model Architecture
- Model: YOLOv8n-cls (YOLOv8 Nano for classification)
- Input Size: 224x224 pixels
- Number of Classes: 10
- Parameters: 1.45 million
- Training Framework: Ultralytics YOLO
Class Labels
The model predicts the following 10 classes of driver behavior:
- c0: safe driving
- c1: texting - right
- c2: talking on the phone - right
- c3: texting - left
- c4: talking on the phone - left
- c5: operating the radio
- c6: drinking
- c7: reaching behind
- c8: hair and makeup
- c9: talking to passenger
Training Details
- Training Epochs: 12
- Final Training Loss: 0.0603
- Validation Accuracy: 99.6%
- Top-5 Accuracy: 100%
- Training Time: 0.27 hours
- Dataset Split: 70% train, 15% validation, 15% test
- Total Training Images: 15,692
- Total Validation Images: 3,358
Performance Metrics
The model achieved excellent performance on the validation set:
- Top-1 Accuracy: 99.6%
- Top-5 Accuracy: 100%
- Consistent convergence across all epochs
- Low validation loss indicating good generalization
Installation and Usage
Requirements
pip install ultralytics
Basic Usage
from ultralytics import YOLO
# Load the model from Hugging Face
model = YOLO('Safe-Drive-TN/State-farm-detection')
# Perform prediction on an image
results = model.predict('path_to_your_image.jpg')
# Get prediction results
predicted_class = results[0].probs.top1
confidence_score = results[0].probs.top1conf
class_names = results[0].names
print(f"Predicted class: {class_names[predicted_class]}")
print(f"Confidence: {confidence_score:.2%}")
Dataset Information
The model was trained on the State Farm Distracted Driver Detection dataset, which contains images of drivers performing various activities in a vehicle. The dataset ensures driver separation between train and test splits to prevent data leakage.
Limitations
The model was trained in a controlled environment and may not generalize perfectly to real-world driving conditions
Performance may vary with different camera angles, lighting conditions, and vehicle types
The model classifies single frames and does not incorporate temporal information
Citation
If you use this model, please cite:
@misc{state-farm-detection-2025,
title = {State Farm Detection in Vehicle — Yolov8n-cls},
author = {Malek Messaoudi, Yassine Mhirsi},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Safe-Drive-TN/State-farm-detection}}
}
License
License : MIT