Soo the point is that original vgg was using that size. Network is “used to see” objects whose sizes are contained in a 112x112 image.
There is something called receptive field (rather than boring you with a shitty explanation I will link to a blog https://towardsdatascience.com/understand-local-receptive-fields-in-convolutional-neural-networks-f26d700be16c 5) soo if the input image is too large and the network is not deep enough, this may harm the results.
Soo imagine that you are a neuron and you have to describe what you see. If you are too closed to an object you will be able to provide fine details but without a “panoramic” context. However if you are too far, you will provide a good overall description but coarse details.
In that way it is said that first layers learn basic features (gradients) meanwhile deeper layers learn more abstract features.
Soo to address this problem several techniques were developed like pooling or dilated convolution