U-Net architecture adopted in the present study. Rectangular boxes with different colors represent 2D convolutional (blue), maximum pooling (purple), up-sampling (green), and output (orange) layers. The input image (2D density map of a spiral galaxy) is processed in these layers counterclockwise along this U-shaped architecture, and the dimensions of input and output data (channel numbers, etc.) are shown within each box for each layer. The two 3 × 3 2D convolutional layers with a ReLU each are followed by a 2 × 2 maximum pooling layer, and this image processing is repeated at the left (contracting). In the right (expanding) part of U-Net, the output from a convolutional layer is concatenated with that from an up-sampling layer, and this concatenation is indicated by orange rectangles. For example, two 2D convolutional layers with 128 channels within the third block from top in the right part of U-Net are followed by an up-sampling layer, and the output of the up-sampling layer is concatenated with the output from the second convolutional layer with 64 channels within the second block from top in the left part of U-Net, and then input into the first convolutional layer with 64 channels within the second block from the top at the right. A sigmoid activation function rather than softmax is used in the final output layer in the present segmentation tasks of spiral arms.