I am doing image classification with CNN, and I have a training set of 3200 imges and a training set of 400 images. I have used two different approches for doing this classification : Transfer learnoing and bulding a CNN from scratch.
In the case of transfer learning I get an accuracy of 85%, and in the case of building the network from scratch I get an accuracy of 89%.
But usually with a small dataset shouldn't transfer leanring have better performances?
One thing to consider first is that I used early-stopping as regularization technique, and in the case of transfer learning this stopped the training process at the epoch 18, while for the CNN from scratch I arrived at the epoch 100, which is the number of epochs I imposed for training.
Can someone help me with my doubt? Thanks in advance.