MobileNetV1 Paper Walkthrough: The Tiny Giant

MobileNetV1 is a lightweight neural network architecture designed for efficient performance on mobile and embedded vision applications. The model's core innovation is the use of depthwise separable convolutions, which factorize a standard convolution into a depthwise convolution for spatial filtering and a pointwise convolution for combining channel information. This technique dramatically reduces the computational cost and number of parameters compared to traditional convolutional networks. The architecture also introduces width and resolution multipliers as hyperparameters, allowing developers to easily trade off between accuracy and latency to fit specific constraints.