Hi,
I am trying to do binary classification using transfer learning.
In the process, I want to experiment with freezing/unfreezing different layers of different architectures but so far, I am able to freeze/unfreeze entire models only.
Can anyone help me in illustrating it with a couple of model architectures?
Below, I am using Timm
and a couple of architectures - convnext
and resnet
-
import timm
convnext = timm.create_model('convnext_tiny_in22k', pretrained=True,num_classes=2) # noqa
resnet = timm.create_model('resnet50d', pretrained=True,num_classes=2)
How to find which layers to freeze/unfreeze?
Thanks.