Two-layer CNN architecture processing 224x224x3 RGB images through stacked convolutional, pooling, batch normalization, and dropout blocks. Layer 1 applies 32 filters with 3x3 kernels followed by 2x2 max pooling and batch norm, reducing spatial dimensions to 112x112x32; Layer 2 applies 64 filters with identical kernel size, further reducing to 56x56x64. The flattened feature maps (200,704 units) feed into a 512-unit dense layer with ReLU activation and 0.5 dropout, culminating in a 10-class softmax output layer for multi-class classification. This architecture demonstrates regularization best practices—batch normalization, dropout at 0.25 and 0.5 rates, and parameter-efficient convolutions—essential for preventing overfitting on moderate datasets. Fork and customize this diagram on Diagrams.so to adjust filter counts, kernel sizes, or add skip connections for your specific classification task. The explicit dimension tracking and parameter counts (896 params in Layer 1, 18,496 in Layer 2) help practitioners understand computational complexity and memory footprint.