Hi @apunjani,
We had a similar issue recently, leading (in retrospect!) to me finding this post - posting my experience here in case it is useful to anyone in the future.
We find that there is frequently a strong “attractor” effect in 2D classification in CryoSPARC, which seems to be related to the sigma annealing process, and which doesn’t seem very sensitive to the initial classification uncertainty factor.
Basically, for a large particle that goes to high resolution on gold grids, we see large junk classes in the first couple of iterations that seem to correspond to gold edges or other junk, but these classes gradually become smaller, and completely disappear by the time sigma has finished annealing - all the particles end up in “good”, high resolution classes.
If we switch sigma annealing off completely however, (by setting the iteration to start annealing to higher than the total number of iterations), these classes remain separated, and sure enough when we map them back to the micrographs they correspond to junk! Mostly intact particles but right next to gold edges, or overlapping with surface contaminants.
We have also found the same approach helpful when we have a mixture of particles, some of which are better ordered than others - otherwise the less well ordered ones have a habit of getting subsumed by the better ordered species.
Cheers
Oli