I'm new to this, know only the theory part of the stuff.
I want to train a neural network for detection of currently single class(will be extending to detect more classes)
I came across transferred learning, that explains as how to use a pre trained NN to train new data.
I selected a framework pytorch which has a nice tutorial explaining Transfer Learning
We have a pytorch implemented Single Shot Detector as well.
This is my current situation
- The data I want to train is different from the once trained already i.e 20 classes those have already been trained.
- I currently have a very limited labeleb data training set.
The solution is to freeze the weights of the initial few layers, and then train the NN.
I am confused as what exactly is meant by initial few layers?
This is a useful post I found online [Learning Note] Single Shot MultiBox Detector with Pytorch — Part 1 explaining how the Single Shot Detector works.
Can anyone help me how to perform these two tasks
- What are the initial few layers here in this case? How exactly can I freeze them?
- What are the changes I need to make while training the NN to classify one or more new classes?