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Sweep code for studying model population stats (1 of 2) #143
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This is a major update and introduces powerful new functionality to pycls.
The pycls codebase now provides powerful support for studying design spaces and more generally population statistics of models as introduced in On Network Design Spaces for Visual Recognition and Designing Network Design Spaces. This idea is that instead of planning a single pycls job (e.g., testing a specific model configuration), one can study the behavior of an entire population of models. This allows for quite powerful and succinct experimental design, and elevates the study of individual model behavior to the study of the behavior of model populations. Please see
SWEEP_INFO
for details.This is commit 1 of 2 for the sweep code. It is focused on the sweep config, setting up the sweep, and launching it.
Co-authored-by: Raj Prateek Kosaraju rajprateek@users.noreply.github.com
Co-authored-by: Piotr Dollar pdollar@gmail.com