MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks.
They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems.
The need for efficient deep learning models on mobile devices led researchers at Google to develop MobileNet.
As of October 2024[update], the family has four versions, each improving upon the previous one in terms of performance and efficiency.
It was first developed by Laurent Sifre during an internship at Google Brain in 2013 as an architectural variation on AlexNet to improve convergence speed and model size.
lead to smaller and faster models, but at the cost of reduced accuracy, and a resolution multiplier
Linear bottlenecks removes the typical ReLU activation function in the projection layers.
This was rationalized by arguing that that nonlinear activation loses information in lower-dimensional spaces, which is problematic when the number of channels is already small.
[8][9] It used piecewise-linear approximations of swish and sigmoid activation functions (which they called "h-swish" and "h-sigmoid"), squeeze-and-excitation modules,[10] and the inverted bottlenecks of MobileNetV2.