Thanks for the reply @Even and the confirmation.
I was thinking that's the reason why removing the last two layers worked better than removing just one - the last layer looked for breads of dogs and cats but the one before was more generalized (faces, ears, etc).
Although, that brings up a question...At some point, flowing up the model, should already be trained for things like circles and patterns which would be useful for this competition. Instead of retraining the entire model, just remove all layers up to that point, then add and train new layer(s).
But how does one know how many layers to remove? Trial and error? The art of data science?
In any case, I'll try out some things and do some googling of trained weights for VGG (thanks for the suggestion!)