Tips and Tricks for sorting compositional heterogeneity in image stacks

[csparc_addict]

Hi Oli,

@olibclarke

Occasionally, we have found running classification for many more epochs than the default helpful (e.g. 20 rather than 2). Often in these situations, keeping the learning rate fixed allows the classification to converge more quickly.

Could you comment on how increasing the number of O-EM epochs and full iterations each impacts the classification, respectively? I came across another post from you, where you mentioned increasing the number of full iteration dramatically improved the results. I was wondering if tuning one has a much bigger effect than the other.

My particles are hardly separated unless “force hard classification” is turned on. Therefore, the ESS is always 1, and it’s hard for me to judge whether the results were improving along with more iterations. I’d really appreciate it if you could provide some insights.