Train neural network for semantic segmentation (deep lab V3) with pytorch in less then 50 lines of code

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SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

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This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper
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