If you are inference constrained, it might be better. Since a good chunk of the model is shared you need less compute and perhaps even less time, if you can't paralellize sufficiently. The other comments here have other good arguments.

No, and if there was one you wouldn't want it.

The reason is that with a model, any model, you only get guarantees for inputs that match the training data distribution. If the model has never seen a cogwheel before there's no telling what it will do.

If you find one, there's no promise how well it will do on your task. It might work, might work but only half the time or it might just give you wrong answers. To know this, you will need a dataset with your images and the desired labels. If you have this, you might as well make it a bit larger and train the model on part of it.

You will get better results if you start from a model that already was trained on data similar to your, or start with a pretrained model and tune it on your data.