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I am trying to make a CNN model on different brands of logos . Firstly , I wrote a CNN from scratch and trained it on which I got 70% accuracy, I have total 40 classes and each class has 100 images . I know it is too short, that is why I am now moving towards pretrained model. My question is that I read on Internet that Transfer learning applied on only similar datasets like lets say VGG model trained on Cats and Dogs , so we can use it for training the tiger images also . Can I use different dataset like I am using MobileNet on different brands of logos . Will it give good accuracy ?

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  • $\begingroup$ Try it out and see what you get. $\endgroup$ Jan 24 at 8:59
  • $\begingroup$ But Can you tell me that what should be the result according to your perception ? $\endgroup$
    – Hamza
    Jan 24 at 9:01
  • $\begingroup$ I have no idea. The best way to find out is to try it out and see what you get. $\endgroup$ Jan 24 at 9:01
  • $\begingroup$ The answer is the same as the last time you asked about about this task: you have to try it and see. $\endgroup$
    – D.W.
    Jan 24 at 18:55
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Let me start by making an acknowledgement that in machine learning intuitions can be wrong and it is not possible to know beforehand what would work and what wouldn't work. That's why machine learning is very much an experiment based method. Having made this acknowledgement let me tell you my intuition. If you transfer learning from image processing problem to a different image processing problem then it shouldn't hurt if your network is deep enough. Most likely, a convolutional network's earlier layers find simple features that are useful to describe any image. Like horizontal lines and diagonal lines. Then, the deeper layers vary according to specificity of the dataset to find more complex features. Even if you are working on a wildly different image problem, what you can try to do is keep the earlier layers constant during the learning and just learn the deeper layers. After some learning you can allow the earlier layers to change as well to do some fine tuning.

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