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Does it mean you add new code whenever you want to change parameters or you save stuff just when you find something interesting? Also, I guess you don't fix your seeds!?
I just try to make sure that experiments at any point of time can be 100% reproduced in terms of the functioning but not necessarily to the point where the exact data is used (fixed seeds). Benchmarks do use the same exact images though, so effectively the same outcome there.
Key point is that I consider the benchmarks to be the more useful point of comparison, as opposed to validation/training loss, because the latter can easily be modified/changed and that won't be a problem.
How do you go about creating benchmarks? Some dataset with metrics you define? If so, how long did it take you to figure out how to create a good benchmark for your use case?
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