Hi! Hopefully this is a good place to ask.
I've been googling around a fair bit, but haven't had much luck- I'm either finding ELI5 type articles, or in depth tutorials on setting up a model to tell the difference between a frog and a dog. I'm not sure if those are relevant to my concept.
I would like to implement a ML algorithm to detect a particular type of defect on a production line. Our current camera system isn't quite up to the task, but gives good, consistent imagery, and I have a good historical dataset. The product moves past the camera, it snaps a single black and white image, then the product moves on. This means that most of my images are more or less the same. These defects are obvious to the human eye.
Could someone please give me, a noob, a bird's eye view of how I would go about using ML to create a model for this? There's so many choices of tools and tutorials that I don't know which would be best suited to this use case.
Thanks Esme.
Walking before you run is good advice, I just didn't know that what I was trying to do was running, not crawling!
When you say an architecture that suits me- how should I go about investigating which one which will suit me?
And is it better to train a simple binary 'pass/fail' initially? Or is it relatively trivial to introduce a scalar such as a score out of ten, or multiple reasons for failure, straight off the bat?