Reviewing Nota AI’s NetsPresso Compression Toolkit
I was asked to write a review for Nota AI’s NetsPresso Compression Toolkit (NPTK). If you’re not familiar with NPTK, you can read an introduction to it and the compression methods it supports here.
I was struck by the ease of using Nota’s compression toolkit to compress models. Rather than needing to consult library documentation and optimize various pipeline and environmental parameters, I can upload a model to the Netspresso compression toolkit, toggle a few buttons to reflect my intended compression goal, and receive a compressed model with helpful compression metrics. The recommendation feature for structured pruning is especially helpful for users that may not have a strong theoretical background and simply want to optimize their model with minimal fuss.
In the research lab, I am working on a semantic segmentation task to extract biological features in the form of a segmentation map from an input electron microscopy image. One of the models I am currently working with is the DeepLab architecture, which did not go through compression successfully (as I later found out, due to incompatibilities in the architecture implementation).
I emailed the support team and got a quick response in less than a day detailing…
