Overfitting in resnet-34 vs vgg-16

Hi All,

I trained a dataset (grey-scale ultrasound images. with 15 classes) on vgg-16 and resnet-34 .
vgg-16 gives me a validation accuracy of 92% where as I can only hit 83% with resnet-34 .

I handled overfitting in both architectures with dropout in FC layer and regularization in optimizer.
I don’t know why there would be minimal overfitting with vgg and not resnet . The resnet model train loss is 0.02 vs valid loss 0.67. Moreover resnet model doesn’t seem to improve beyond this loss range. I have tried hyper-parameter tuning on weight decay, learning rate, momentum, dropout.

The purpose of this exercise to improve our predictions on the dataset. Hence I started trying resnet-34 after vgg-16 .

I would love some suggestions on

  1. How to improve resnet-34 performance and address its overfitting.
  2. Any other architecture suggestions that would work for this type of dataset.