Hi Jermey... Great article. I have a labeled product image dataset (thumbnails, and various sizes) that I'd like to use for the model training. Can you recommend any lightweight methods for commodity hardware?
You can use the approach we used to win the CIFAR 10 training cost section of the competition. If you use fastai/pytorch, then it's ~5 lines of code. Check out lesson 1 of http://course.fast.ai for the basic approach, but when calling `fit()`, add the param `use_clr_beta=(20,20,0.95,0.85)` which will enable 1cycle, and should allow of super convergence. Then train with SGD with a really high learning rate (somewhere from 1-3, generally).