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