Sensible insights for a data-driven method to mannequin optimization
On this final a part of my sequence, I’ll share what I’ve realized on choosing a mannequin for picture classification and find out how to superb tune that mannequin. I will even present how one can leverage the mannequin to speed up your labelling course of, and eventually find out how to justify your efforts by producing utilization and efficiency statistics.
In Part 1, I mentioned the method of labelling your picture information that you just use in your picture classification challenge. I confirmed how outline “good” pictures and create sub-classes. In Part 2, I went over numerous information units, past the same old train-validation-test units, with benchmark units, plus find out how to deal with artificial information and duplicate pictures. In Half 3, I defined find out how to apply totally different analysis standards to a educated mannequin versus a deployed mannequin, and utilizing benchmarks to find out when to deploy a mannequin.
Mannequin choice
Thus far I’ve centered quite a lot of time on labelling and curating the set of pictures, and in addition evaluating mannequin efficiency, which is like placing the cart earlier than the horse. I’m not making an attempt to reduce what it takes to design an enormous neural community — this can be a essential a part of the appliance you might be constructing. In my case, I spent a couple of weeks experimenting with…
