I have successfully trained a Yolo model to recognize k classes. Now I want to train by adding k+1 class to the pre-trained weights (k classes) without forgetting previous k classes. Ideally, I want to keep adding classes and train over the previous weights, i.e., train only the new classes. If I have to train all classes (k+1) every time a new class is added, it would be too time-consuming, as training k classes would take $k*20000$ iterations, versus the $20000$ iterations per new class if I can add the classes incrementally.
The dataset is balanced (5000 images per classes for training).
I appreciated if you can throw some methods or techniques to do this continual training for Yolo.