What is a stem network in CNN classifiers?

The stem network in CNN classifiers is the initial part of the convolutional neural network responsible for primary processing of the input image. It usually consists of several convolutional layers with large kernels and/or strides, as well as pooling layers.

The main task of the stem network is to quickly and efficiently extract basic features from the image (such as edges, textures), and to reduce the spatial dimensions of the data for subsequent processing by deeper layers.

For example, in ResNet architecture, the stem network might look like:

• Convolution with a 7x7 kernel and stride=2
• MaxPooling with a 3x3 kernel and stride=2

This reduces the input image size and prepares it for more complex operations.

Thus, the stem network acts as a preliminary filter and dimension reducer, improving the efficiency and quality of the classifier's training.