Hi, I am really new to machine learning and have been playing around with an example from a book. Some models are trained with CGI datasets, and the Rock, Paper, Scissors Dataset works pretty well when I use it to predict real world images.
I tried to replicate training with CGI images using images I rendered myself and I am struggling to understand why my two similar datasets give wildly different results.
Dataset 1 2 works really well when using real world images 1, classifying all but one image correctly. But the other Dataset 2 1 does not work at all, with only one or two images correctly classified.
I randomly split the dataset into 20% validation and 80% training data. The code for training is here 1.
Is it pure luck that makes Dataset 1 work so well or is there a fundamental flaw in my understanding?
Thank you for advice.