Online Learning for Yolo Network?

Question

I want to use the Yolo Network v3 for let's say detect 5 custom object classes, for which I already have data. So I'm going to use my train data of these classes to retrain the yolo network with pre-trained weights.

Now Imagine the case:

After some time I want to add another class to my model. Now I need to change the architecture of my model, therefore I would need to retrain my model with all 5 + 1 classes, right?

To avoid this situation I had the idea to set a maximum number of classes at the beginning, let's say 20. So I build a Yolo-architecture with 20 classes and train it with the first 5 classes for which data is available. If data of a new class is available, I will use Stochastic Gradient Descent for Online-Learning to train the model to detect the new class.

Here are my questions:

• Does the model correctly learn the 5 classes at the beginning, without having data from the other 15 classes?
• Is it possible by Stochastic Gradient Descent to learn new classes bit by bit?
• Is there any other convenient way to handle my problem?

Thanks for any advice!