Which part should be frozen during transfer learning? I want to use transfer learning and fine tuning and I need to decide which part of
the original model will be used and which part will be frozen. I'm thinking about four possilbe cases:
small/large new dataset and this set is similar/not similar to the original dataset. What should be done to achieve best results in each of the cases?
Ideally I follow the below rules for performing transfer learning, I could do successful object detection using Yolo model by traning only last layer and was able to do leaf classification by training good number of layers.