ShuffleNet is a convolutional neural network architecture designed specifically for mobile devices with very limited computing power. ShuffleNet aims to achieve high accuracy while minimizing computation cost. ShuffleNet introduces this operation to reduce computation. It combines pointwise convolutions, 1×1 convolutions with group convolutions. The group convolutions allow efficient parallelization across channels. It groups input channels… Continue reading ShuffleNet and SqueezeNet
Tag: cnn
MobileNetV2
MobileNetV2 is a convolutional neural network architecture designed to perform well on mobile devices. It is based on an inverted residual structure, where the residual connections occur between the bottleneck layers. The intermediate expansion layer employs lightweight depth wise convolutions to filter features, serving as a source of non-linearity. It aims to strike a balance… Continue reading MobileNetV2