Efficient network structure: EfficientNet

EfficientNet is an efficient, scalable convolutional neural network structure with automatic model scaling. The core idea is to improve the performance of the model by increasing the depth, width and resolution of the network based on an efficient basic network structure. Compared with the tedious process of manually adjusting the network structure, this method not only improves the efficiency and accuracy of the model, but also avoids unnecessary work. Through the automatic model scaling method, EfficientNet can automatically adjust the size of the network according to the requirements of the task, so that the model can achieve better results in different scenarios. This makes EfficientNet a very practical neural network structure that can be widely used in various tasks in the field of computer vision.

EfficientNet’s model structure is based on three key components: depth, width and resolution. Depth refers to the number of layers in the network, while width refers to the number of channels in each layer. Resolution refers to the size of the input image. By balancing these three components, we are able to obtain an efficient and accurate model.

EfficientNet adopts a lightweight convolution block, called MBConv block, as its basic network structure. The MBConv block consists of three parts: a 1x1 convolution, a scalable depthwise separable convolution and a 1x1 convolution. 1x1 convolution is mainly used to adjust the number of channels, while depth-separable convolution is used to reduce the amount of calculation and the number of parameters. By stacking multiple MBConv blocks, an efficient basic network structure can be built. This design allows EfficientNet to have smaller model size and computational complexity while maintaining high performance.

In EfficientNet, the model scaling method can be divided into two main steps. First, the basic network structure is improved by increasing the depth, width, and resolution of the network. Second, the three components are balanced by using a composite scaling factor. These composite scaling factors include depth scaling factors, width scaling factors, and resolution scaling factors. These scaling factors are combined through a composite function to obtain the final scaling factor, which is used to adjust the model structure. In this way, EfficientNet can improve model efficiency and accuracy while maintaining model performance.

The EfficientNet model can be expressed as EfficientNetB{N} according to its size, where N is an integer used to represent the scale of the model. There is a positive correlation between model size and performance, i.e. the larger the model, the better the performance. However, as the model size increases, the computational and storage costs increase accordingly. Currently, EfficientNet provides seven models of different sizes from B0 to B7. Users can choose the appropriate model size according to specific task requirements.

In addition to the basic network structure, EfficientNet also uses some other technologies to improve the performance of the model. The most important of these is the Swish activation function, which has better performance than the commonly used ReLU activation function. In addition, EfficientNet also uses DropConnect technology to prevent overfitting and standardization technology to improve the stability of the model.

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