I have just started learning pytorch please guide.
I am playing around with the pytorch implementation of Single Shot Detector trained on VGG16 using VOC2007 DataSet
I have a small data set which I have labelled in the same way as the VOC2007 image data. Which currently has only one class. Will be adding more classes for the training after this learning session with one class.
I came across this Transferred Learning according to which I have to freeze the initial few layers and then fine tune all the rest of the layers with the new set of data for training the network.
Nice explanation of how Single shot Detector works here
pytorch has transferred learning example page
ResNet fine tune model I found
I’m confused now on
How to freeze weights of initial few layers in pytorch of the VGG16 layer? How many layers should actually be frozen?
How do I change my classifier to detect this one class instead of the 20 classes for which it is actually trained for?