Passing a single-channel $28 \times 28$ image through LeNet-5 produces the following shapes at each layer:

1. Conv2d ($5 \times 5$, padding $2$, $6$ channels): output $1 \times 6 \times 28 \times 28$
2. Sigmoid: output $1 \times 6 \times 28 \times 28$
3. AvgPool2d ($2 \times 2$, stride $2$): output $1 \times 6 \times 14 \times 14$
4. Conv2d ($5 \times 5$, no padding, $16$ channels): output $1 \times 16 \times 10 \times 10$
5. Sigmoid: output $1 \times 16 \times 10 \times 10$
6. AvgPool2d ($2 \times 2$, stride $2$): output $1 \times 16 \times 5 \times 5$
7. Flatten: output $1 \times 400$
8. Linear ($120$) + Sigmoid: output $1 \times 120$
9. Linear ($84$) + Sigmoid: output $1 \times 84$
10. Linear ($10$): output $1 \times 10$

The first convolutional layer uses $2$ pixels of padding to preserve the spatial dimensions of the $28 \times 28$ input, compensating for the reduction that a $5 \times 5$ kernel would otherwise cause. The second convolutional layer uses no padding, reducing height and width by $4$ pixels each. As the stack deepens, the number of channels increases ($1 \to 6 \to 16$) while spatial dimensions shrink, until flattening yields a $400$-element vector.