# Why does the size reduce to $6 \times 6$ in the capsule networks?

I want to experiment with capsule networks on facial expression recognition (FER). For now, I am using fer2013 Kaggle dataset.

One thing that I didn't understand in capsule networks was in the first convolution layer, the size was reduced to 20x20 - having input image as 28x28 and filters as 9x9 with 1 stride. But, in the capsules, the size reduces to 6x6.

How did this happen? Because with the input size as 20x20 and filters as 9x9 and 2 strides, I couldn't get 6x6. Maybe I missed something.

For my experiment, the input size image is 48x48. Should I use the same hyperparameters for the start or are there any suggested hyperparameters that I can use?