My ViT-b/16 fine-tuning accuracy has plateaued after 5 epochs

I am fine-tuning a ViT-b/16 model and have observed that the accuracy initially increases from 45% to 51% after 5 epochs. However, after this initial gain, the accuracy plateaus and never increases again. I have experimented with different batch sizes and learning rates using the Adam optimizer with a layer-wise learning rate scheduler. However, in all cases, the accuracy still drops after 5 epochs and never recovers.
Could you please help me identify the potential causes of this early plateauing and suggest strategies to improve the model’s performance?

few main causes for this could be.
1)model is not complex enough.
2)data is not enough(either in size or variation)
3)it is unable to come out from a local minimum.

for issue number 1 and 2 you can handle it.
for issue number 3 try learning rate restart… use a cosine annealing restart scheduler it should fix the problem if issue number is the main cause of this.