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.

  • $\begingroup$ A general rule of thumb to avoid forgetting is to use a low learning rate $\endgroup$ – 0x5050 Jul 16 '19 at 17:19
  • $\begingroup$ @PradipPramanick , but lower learning rate can affect the future new class prediction accuracy right. $\endgroup$ – Troy Jul 18 '19 at 16:19

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