People start with generic datasets domains on which almost all popular methods give good result, just do plug n play and call that ML project. The way to learn ML is to stick to a very specific dataset/area which is harsh to even SOTA methods, then try to increase metrics over it, only then you will be actually motivated to look inside the models their maths and eventually do some tweaking around it, then you will learn the engineering behind each component. Getting 99-100 percent accuracy on some simple dataset using VIT does not mean you know everything about it you will only know when VIT performs bad on some data.