I've been working with some face detection in OpenCV. I have a couple projects I've done - one does face detection which uses a pre-built model. Some others do different things where I collect my own images and train my own models. When I do the latter, it's generally with much smaller datasets that what you'd use for face training.
On my face recognizer - many of the common faces I work with do not get detected properly (due to odd properties like masks, hats, goggles, glasses, etc). So I want to re-train my own model - but grabbing the gigantic "stock" datasets, adding my images to it may take a VERY long time.
So the question is: is there a way to start with an existing model (XML file) and run the trainer in a way that would just add my images to it?
This is called "Transfer Learning". Tenserflow (Keras) has a lot of support for this. It basically consists of taking a pre-existing model with pre-existing weights, "freezing" weights on certain layers, adding new layers in or below existing ones, and then retraining only the un-frozen layers.
it can't be used to readily just "continue" learning, but can be used to add additional things into the training - for newer aspects (like potentially, adding masked people to an already trained model of unmasked people, as in my original question)