Hi All,

I am following lesson 2 and working on dogbreed dataset. I hve used all the mentioned techniques like:

1)LR =.005, it gives me 83.57%

learn.fit(lr, 2)

poch trn_loss val_loss accuracy

0 0.647937 0.553398 0.826321

1 0.62565 0.532092 0.835127

Then with augmentation = False and learn.fit(lr, 3, cycle_len=1, cycle_mult=2)

epoch trn_loss val_loss accuracy

0 0.640235 0.512987 0.840998

1 0.639211 0.509754 0.84002

2 0.599696 0.510067 0.84638

3 0.622977 0.508784 0.842955

4 0.522688 0.491936 0.84638

5 0.544614 0.498515 0.84002

6 0.601776 0.494757 0.844912

after unfreezing and using

lr=np.array([1e-4,1e-3,1e-2])

learn.fit(lr, 3, cycle_len=2, cycle_mult=2)

epoch trn_loss val_loss accuracy

0 0.542071 0.495063 0.840509

1 0.560871 0.495163 0.847358

2 0.530614 0.496824 0.840509

training loss is improved but still validation loss is high. It is still underfitâ€¦ What should i try now??

Just disclaimer i am still using sz = 224 and bs = 16