Also, what is the reason for deep learning if you don't have the data? Have classical methods been unsuccessful?

If you have enough images of defects but are just lacking the labeling (probably easier to come by) one approach is to generate random morphological structures on your bottles (e.g. just some random circles and ellipses) and then apply cycleGAN or CUT to transform from this "segmented" image domain to the domain of real images. As said, you still need a lot of data but don't need labelling. Just generating useful data from noise (basic GAN idea) can work in theory but is extremely hard to train. I had way more success with the domain transfer approach (my case in medical imaging)

This is actually the best answer here. Diving straight into DL will pretty quickly demotivate and make it seem like an impossibility shortly after switching from an online toy example to something real world. I can confirm, this is a great book that also includes the necessary basics. Since it was published it has a well deserved space on my office table.