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One of the few things I remember from watching fastai course a few years ago, is to train a model with smaller data. For example, instead of 2k×2k images, downscale them to for ex 400×400. A network with a better design should still learn fastest on smaller data.

Also, one can train the network to a good accuracy, then change the input layer, and unfreeze the inner layers, that way the network will have a head start.

Not sure how universal this principle is, but it seemed reasonable, if I remember it correctly, of course.

The approach described in the article looks very smart. Also could be handy for integration testing of ML frameworks. I've been working on my own DL framework, and this data set looks like a good way to test the training and inference pipelines E2E.