So in cases where ESS is still at ~9 after 2 full iterations, would you recommend increasing the number of full iterations, or the number of O-EM epochs? From looking at the log it seems like the ESS plateaus at ~13 during O-EM, then decreases to 12, then to 9 in the last full iterations. So I guess more full iterations through the data might be the way to go?
It would also be very helpful to be able to continue from a previous classification run. Having to restart the entire thing from scratch to test a different number of final iterations is not ideal
UPDATE: Changing to 10 full iterations improved matters a lot - both decreasing ESS, and dramatically improving the appearance/diversity of classes. I suspect these defaults could do with some tweaking based on experience so far.
@vperetroukhin It would also be very helpful to have an option to output volume series for every “full” iteration. It seems like more full iterations is better, but only up to a point - too many and classes start to become noisy, presumably from over-refinement. But having to run 5 different 18hr jobs with different numbers of full iterations is a waste - would be good to just run one, and then I can compare the classes over the full iterations and decide where the best point is to stop.