We can trace the dimensionality transformations of an input image (e.g., with a spatial shape of $224 \times 224$) as it passes through the VGG-11 network. The architecture halves the spatial height and width at each of the five VGG blocks due to the max-pooling operations. The resolution systematically drops from $224 \times 224$ to $112 \times 112$, $56 \times 56$, $28 \times 28$, $14 \times 14$, and finally reaches $7 \times 7$. Meanwhile, the number of channels progressively expands up to $512$. The resulting $512 \times 7 \times 7$ feature map is then flattened into a $25088$-dimensional representation before being fed into the fully connected dense layers.