Instead of relying on parameter-heavy fully connected layers for final classification, the Network in Network (NiN) architecture utilizes a global average pooling layer. The final NiN block is configured to produce a number of output channels that exactly equals the target number of label classes. A global average pooling layer is then applied to these feature maps, which yields a vector of logits. This structural design significantly reduces the overall number of required model parameters, although it may potentially lead to an increase in training time.