I have a general question regarding fine-tuning and transfer learning, which came up when I tried to figure out how to best get yolo to detect my custom object (being hands).
I apologize for the long text possibily containing lots of false information. I would be glad if someone had the patience to read it and help me clear my confusion.
After lots of googling, I learned that many people regard fine-tuning to be a sub-class of transfer learning while others believe that they are to different approaches to training a model. At the same time, people differentiate between re-training only the last classifier layer of a model on a custom dataset vs. also re-training other layers of the model (and possbibly adding an enirely new classifier instead of retraining?). Both approaches use pre-trained models.
My final confusien lies here: I followed these instructions: https://github.com/thtrieu/darkflow to train tiny yolo via darkflow, using the command:
# Initialize yolo-new from yolo-tiny, then train the net on 100% GPU:
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights --train --gpu 1.0
But what happens here? I suppose I only retrain the classifier because the instructions say to change the number of classes in the last layer in the configuration file. But then again, it is also required to change the number of filters in the second last layer, a convolutional layer.
Lastly, the instructions provide an example of an alternative training:
# Completely initialize yolo-new and train it with ADAM optimizer
flow --model cfg/yolo-new.cfg --train --trainer adam and I don't understand at all how this relates to the different ways of transfer learning.
