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I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase?

For example, if I run a CNN with 100 data points, vary just one parameter (say add an extra layer, or increase a filter size), and get better results, can I reasonably expect better results with those parameters during the actual training phase?

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  • $\begingroup$ It's wrong that you need a lot of images to train a conv-net... I had only trained them with 22 images as trainset and 7 as validation and test.... that also works $\endgroup$
    – Aditya
    Commented Apr 5, 2018 at 2:35

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If you use pre-trained weights, you need significantly lesser data as the initial layers have already learned from a ton of data and you just need to fine tune the later ones.

What you said is not true, you can train on CIFAR10 and get 90%+ and that is not 100k+. It depends on the complexity of the data and how similar the features are. If they are easily Seperable -less data. If the disctintions are harder then the model needs a lot of examples to figure out which of features are seperate.

I would say you could IF you sample was representive of the population.

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