1) Did your Masters cover non-deep-learning vision (classical vision?) in sufficient detail? There is a ton of math in there. Going from being a shallow user of OpenCV to a deep one seems a big jump. I'm not sure a Masters focused solely on classical vision would get someone there (let alone one covering other things like ML, DL, etc.).
2) Did you end up training large models from scratch or is it all just fine-tuning? I am trying to do the former and I realize getting things to scale for from-scratch training is a whole other topic. I suspect getting things ready for inference would be similar.
1) I didn't want to be someone who only learned machine learning/deep learning solely because it was the sexy thing at the time (still is). I took a class that covered classical computer vision, as well as signal processing classes. So while I wouldn't say I'm an expert, I'm familiar with traditional approaches (enough to be dangerous). I would agree a Masters is not enough to become a deep expert. Unfortunately, I do think you just have to do a PhD to give yourself enough time to explore and absorb all of the complexities of this space.
2) I did a thesis for my Masters program, using generative adversarial networks (GANs) for image compression. It was by no means novel, or a breakthrough, but what I did learn (and this is so obvious it's painful to write this) is that you should pretty much never train from scratch and that you should always use transfer learning. As far as what I did at my last company, it was basically taking state of the art models from the MMDetection python package, fine tuning them to our use case, and then deploying them. So I wasn't really doing anything from scratch.
Happy to chat more about your specific use case if you're interested! You can email me at zbellay at gmail dot com.
1) Did your Masters cover non-deep-learning vision (classical vision?) in sufficient detail? There is a ton of math in there. Going from being a shallow user of OpenCV to a deep one seems a big jump. I'm not sure a Masters focused solely on classical vision would get someone there (let alone one covering other things like ML, DL, etc.).
2) Did you end up training large models from scratch or is it all just fine-tuning? I am trying to do the former and I realize getting things to scale for from-scratch training is a whole other topic. I suspect getting things ready for inference would be similar.
Thx!