On this final a part of my collection, I’ll share what I’ve discovered on deciding on a mannequin for picture classification and how you can wonderful tune that mannequin. I may even present how one can leverage the mannequin to speed up your labelling course of, and at last how you can justify your efforts by producing utilization and efficiency statistics.
In Part 1, I mentioned the method of labelling your picture knowledge that you just use in your picture classification undertaking. I confirmed how outline “good” pictures and create sub-classes. In Part 2, I went over numerous knowledge units, past the standard train-validation-test units, with benchmark units, plus how you can deal with artificial knowledge and duplicate pictures. In Half 3, I defined how you can 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
To date I’ve centered a variety 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 attempting to reduce what it takes to design an enormous neural community — this can be a essential a part of the applying you’re constructing. In my…