MobileNet is designed to be a lightweight convolutional neural network for use in mobile and embedded devices with limited computational resources. It uses depth-wise separable convolutions, which significantly reduce the number of parameters and computational operations compared to traditional convolutional layers. MobileNet has been widely applied in mobile vision tasks such as image classification, object detection, and face recognition on smartphones. Its low-power and small-footprint design enable real-time visual processing on devices with limited battery life and storage.
