Conventional ResNet50

The ResNet residual component has two types of blocks. One is Identity Block, and the other is Convolutional Block.

where x and H(x) are the input and output vectors of the layers. F(x, {Wi}) is the residual mapping function. {Wi} represents the convolutional layer weights. x is added to the result of F by the shortcut connection path.

Figure 1 illustrates the structure and detailed process of the Identity Block. Firstly, x is the input vector, and x has two paths to go. One is the main path where x will be transformed by the residual mapping function F. In the main path, F(x,{Wi}) is performed. The main path usually consists of two typical weight layers. One weight layer is composed of a 2D convolutional layer and a Batch-Norm layer which will conduct the Normalization of the channel axis. After the input x multiplied by the weights {Wi} in the first layer, the nonlinear activation function ‘ReLU’ will be used. And then the result will be into the next weight layer. At the same time, the input x will be fed into the other path called the shortcut path. In the shortcut connection, x will be directly added to the result from the main path. Finally, the combined result H(x) will be applied to the ReLU activation function.

where x and H(x) are the input and output vectors of the layers. F(x, {Wi}) is the residual mapping function. Different from Identity Block, in the shortcut path, the input x will also be transformed by the 2D convolutional layer in the Convolutional Block. Ws denotes the weights of the convolutional layer in the shortcut path.

Identity Block Structure

Figure 2 shows the structure and process of the Convolutional Block. The only difference from the Fig. 1 is the shortcut path. In Identity Block, the input x is directly added to the result of the main path. However, the input x will be fed into a weight layer then the result is combined with the main path.

Convolutional Block Structure