I want to be sure my understanding of the problem is correct. I want to do image classification and current state of the art in my field is achieved by transfer learning with VGG16.
Since image on this field are totaly different than image used by VGG16 trainig image, we should unfreeze "a lot" of layer.
Original VGG16 model has more than 130M parameter. it's has something like 13 hidden layer and 3 fully connected layer at the end. If I unfreeze only the fully connected layer i'll still have more than 80M parameter to train!! and i'll probably need to unfreeze some hidden layer to get better performance since my data are really different
So it's look like transfer learning with VGG16 require a lot of computation power
am I right? Is it possible to use transfer learning with image really different than training data without a lot of computational power?