Maxpool is basically a way of reducing computational load, as it reduces feature map size by 4 times (2x2). Therefore on small input sizes it’s much less important to downsize aggressively (224x224, which is standard ImageNet input size, is like 50x bigger than 28x28 MNIST input here). I think maxpool isn’t very common these days, but feel free to experiment with maxpool (and with conv stride) and see what works out best for you 