Passing a single-channel $96 imes 96$ image through GoogLeNet produces the following output shapes at each module:

1. Module $b_1$ (Stem): output $1 imes 64 imes 24 imes 24$
2. Module $b_2$: output $1 imes 192 imes 12 imes 12$
3. Module $b_3$ (2 Inception blocks): output $1 imes 480 imes 6 imes 6$
4. Module $b_4$ (5 Inception blocks): output $1 imes 832 imes 3 imes 3$
5. Module $b_5$ (2 Inception blocks + global avg pool): output $1 imes 1024$
6. Linear (output layer): output $1 imes 10$

The input height and width are reduced from $224$ to $96$ to enable a reasonable training time on Fashion-MNIST. The spatial dimensions are progressively halved by max-pooling between modules ($96 o 24 o 12 o 6 o 3 o 1$), while the number of channels grows ($64 o 192 o 480 o 832 o 1024$). The global average pooling in Module $b_5$ collapses the spatial dimensions to $1 imes 1$.