This project implements a deep learning model for real-time semantic segmentation optimized for mobile deployment. The architecture combines MobileNetV2 depthwise separable convolutions with a U-Net encoder-decoder structure to achieve 0.89 IoU on face/hair segmentation. It includes a training pipeline with dice coefficient loss, supports both PyTorch and TensorFlow, and exports to CoreML for iOS deployment and TensorFlow Lite for Android.