Practical Analysis on Architecture of EfficientNet

Convolutional neural networks (CNNs) are now used in a variety of computer vision applications. However, it is quite hard to adopt them in real-time system due to the problem of increasing model size. Recently, some efficient networks which still have acceptable performance are proposed. Among them, EfficientNet is one of the state-of-the-art architectures. It can be considered a family of network models. EfficientNet could take its place among the state-of-the-art on the ImageNet challenge while still have much fewer parameters and computation cost. But given some of its subtleties, it is more efficient than most of its predecessors. It uses the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation modules (SE modules). This paper investigates the effect of SE modules on the performance of EfficientNet-B0, the fundamental network model in its family, by repositioning/removing the SE modules.