I'm looking at isolating the foreground from a set of photos, and I think I want to use instance segmentation so that I can try to gauge how well-focused the main subject of each photo is.
I have rudimentary experience of Yolo/darknet (but want to move beyond simple bounding boxes). I've never used python, and am mainly a PHP developer. Happy to learn whatever's needed though, but I don't want to spend a day messing around only to find I've picked the wrong tool for the job, or that it's far too complex and I can't get my head around it!
I have a server with a 1080 GPU in it. Speed is kind of important, but so long as it's less than a couple of seconds per photo, that should be ok. Is that feasible? The photos are 10MP+ in size, but I'm guessing it's standard practice to size down to (say) 1MP first, or whatever.
I have millions of photos that I want to run this against, to help weed out poor-quality shots.
Where should I be looking to get started? I see a lot of mentions of Mask R-CNN, but I don't know if that's a good fit or if there's better/faster/easier options out there right now?
Furthermore, should I use existing yolo/darknet bounding boxes, and somehow import them into Mask R-CNN et al, or is it better to start afresh and let Mask R-CNN deal with all of it?