Drowsiness-Detection-

Core Skills/Tools used: I’ve skillfully applied some of the Machine Learning techniques: -Image/Video Preprocessing (NumPy): Demonstrated proficiency in facial image and video manipulation. Efficiently implemented techniques like face detection, eye tracking, grayscale conversion, and normalization using NumPy’s powerful array capabilities. -Deep Learning Model Development (PyTorch): Architected and trained Convolutional Neural Networks (CNNs) -Model Optimization (PyTorch): Enhanced model performance by employing techniques like quantization, pruning, or knowledge distillation in PyTorch to create lightweight models suitable for embedded systems. -Dataset Preparation and Augmentation (NumPy, PyTorch): Curated and preprocessed drowsy driving datasets, applying data augmentation (random cropping, flipping, noise injection) with NumPy and PyTorch to increase robustness and prevent overfitting. Successfully implemented a Drowsiness Detection system using OpenCV, Pytorch