RepVGG is a convolutional neural network architecture that is inspired by the VGG architecture. It has several advantages over other convolutional neural networks.
The Plain Topology
One of the main advantages of RepVGG is its plain topology. Unlike other convolutional neural networks which have multiple branches, the model has a VGG-like plain topology without any branches. Every layer takes the output of its only preceding layer as input and feeds the output into its only following layer. This makes the model easier to understand and implement, as there are no complex branching structures.
Use of 3x3 Convolutions and ReLU
Another advantage of RepVGG is its use of only 3x3 convolutions and ReLU activation functions. These are computationally efficient blocks that can be easily implemented on modern hardware. The model takes advantage of this simplicity by using 3x3 convolutions, which means that the model is lighter and requires fewer computations compared to other convolutional neural networks which use larger convolutions. The use of ReLU activation functions also means that the model can easily learn complex non-linear functions.
No Automatic Search or Manual Refinement
RepVGG does not use any heavy designs such as automatic search or manual refinement to design the model's specific depth and layer widths. The model's architecture is instantiated without heavy designs or compound scaling. This means that the model is simpler and easier to implement than other convolutional neural networks.
RepVGG is a powerful convolutional neural network architecture that is both easy to implement and computationally efficient. With its plain topology, use of 3x3 convolutions and ReLU activation functions, and lack of heavy designs, it is a popular choice for many deep learning applications.
