(Cross-posting here from the data science stack exchange, as my question didn't get any replies. I hope it's okay!)
I've been playing around with YOLOv3 and obtaining some good results on the ~20 custom classes I trained. However, one or two classes look like they can use some additional training data (not a lot, say about 10% more data), which I can provide.
What is the most efficient way to train my model now? Do I need to start training from scratch? Can I just throw in my additional data (with the appropriate changes to the config files etc.) and run the training based on the weight matrix I already acquired, but for a small number of iterations? (1000?) Or is this more like a transfer learning problem now?
Thanks for all tips!