This study focuses on optimizing neural network architectures for real-time detection of driver facial bounding boxes. Initially, we trained the Florence-2 model, which demonstrated high accuracy but proved too large for real-time applications. To address this, we employed model distillation, using Florence-2 as a teacher to train a more compact DINOv2 model. Our aim was to maintain high detection accuracy while minimizing memory usage and inference time, making the solution viable for real-time implementation on GPU and NPU devices. We present a comparative analysis of model performance in terms of IoU scores, memory consumption and inference times.