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I have an image data set of around 180 images in 60 classes (3 images per class). I am able to build a classifier using feature matching. However, I want to try Convolution Neural Networks and see if I can improve.

I don't think I can train a CNN on just 120 images.

If that's true, what is the best way to approach this problem?

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The way I solved the problem was that I took a neural network trained on imagenet and then extracted the features from one of the last layers and applied SVM on the feature vectors from these layers.

These gave me 75% accuracy on the dataset.

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  • $\begingroup$ One of the tricks of the trade these days is to use the filters from the first convolutional layer to obtain features and stick them into an SVM: arxiv.org/abs/1403.6382 I am a bit worried about the 75% accuracy. I assume you have 60 output neurons and just do 0/1 loss? The generalization is quite good then. How do you regularize the network? Dropout can help a lot here or did you use features from another network? How was the SVM trained? One-vs-all? I am particularly interested because I might run into a similar problem soon. $\endgroup$
    – pAt84
    Commented Feb 16, 2016 at 7:32
  • $\begingroup$ In addition, it is always good to look beyond the accuracy or your measurement of choice. In your example it might be possible that 75% of all classes work and 25% don't work at all (a bit of an extreme example). But may your work be of scientific or industrial nature, this is usually a situation you would want to avoid. $\endgroup$
    – pAt84
    Commented Feb 16, 2016 at 7:38

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