All algorithms
3D classification
Stage
2
of 8
Included on all plans
Automated Class3D Convergence
3D classification that stops as soon upon convergence, instead of running a fixed number of rounds that no longer change the result.
Up to 30%
Of 3D classification runtime saved
Automatic
No iteration count to guess
What it does
3D classification runs for a fixed number of iterations that you choose before you know how the data will behave. Set it too low and the classes have not settled. Set it high to be safe, and the last stretch of the run changes almost nothing while still costing compute and time.
Automated Class3D Convergence removes the guess. The job stops as soon as the iterations converge, which can save up to 30% of runtime.
How it works
Each iteration of classification reassigns particles between classes and updates the class maps. Early on, many particles move and the maps change a lot. As the classification settles, each iteration changes less, until further rounds stop changing the outcome.
That flattening point is convergence. Stopping there keeps the result of a full run while dropping the redundant iterations. How much that saves depends on the dataset: it is largest on data that settles early, where a fixed iteration count does the most unnecessary work.
Per class resolution and stability plotted against the iteration number. High class assignment stability and plateaus in resolution are good indicators of convergence.
Where it sits
It applies to 3D classification, and its parameters are accessible in the 3D classification job setup page.
Classification is one of the longer-running steps in a workflow and is often repeated, so time saved here compounds across a project.
At a glance
Stage
3D classification
Runs
Prior 3D classification
Hardware
Cloud compute
Inputs
A reference map from the data
Outputs
Junk classes for use as decoys
Replaces
Availability
Included
Stop paying for iterations that no longer change the answer.
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