Hi, I’ve been working on building an image classifier on a personal dataset with the resnext101 architecture. The generic recipe prescribed in the first set of lessons advises:
fit.learn(lr, 3, cycle_len=1, cycle_mult=2)
I’ve found this to work fairly well when training frozen models, but unfrozen training with those tuning parameters overfits quite quickly, usually by the 3rd epoch. Would decreasing the learning rate help mitigate this? It’s a slow trial and error, which I am working through presently.