Fine Tuning of GoogLeNet Model

Question

I trained GoogLeNet model from scratch. But it didn\'t give me the promising results.
As an alternative, I would like to do fine tuning of GoogLeNet model on my dataset.

Answer 1

Fine-tuning is a very useful trick to achieve a promising accuracy compared to past manual feature. @Shai already posted a good tutorial for fine-tuning the Googlenet using Caffe, so I just want to give some recommends and tricks for fine-tuning for general cases.

In most of time, we face a task classification problem that new dataset (e.g. Oxford 102 flower dataset or Cat&Dog) has following four common situations CS231n:

1. New dataset is small and similar to original dataset.
2. New dataset is small but is different to original dataset (Most common cases)
3. New dataset is large and similar to original dataset.
4. New dataset is large but is different to original dataset.

In practice, most of time we do not have enough data to train the network from scratch, but may be enough for pre-trained model. Whatever which cases I mentions above only thing we must care about is that do we have enough data to train the CNN?

If yes, we can train the CNN from scratch. However, in practice it is still beneficial to initialize the weight from pre-trained model.

If no, we need to check whether data is very different from original datasets? If it is very similar, we can just fine-tune the fully connected neural network or fine-tune with SVM. However, If it is very different from original dataset, we may need to fine-tune the convolutional neural network to improve the generalization.