Four-block convolutional neural network designed for 48x48 grayscale image classification with progressive filter expansion from 64 to 512 channels. Each block pairs Conv2D layers with BatchNorm for stable training, followed by MaxPooling for spatial reduction and Dropout for regularization. The architecture flows through GlobalAvgPooling into a classifier head with Dense layers, culminating in a 7-class Softmax output. This pattern demonstrates best practices for preventing overfitting while maintaining discriminative capacity across hierarchical feature levels. Fork this diagram to customize filter counts, adjust dropout rates, or adapt the input dimensions for your specific classification task.