pytorch-slimming and slimming
These are two independent implementations of the same ICCV 2017 paper on channel pruning, with the PyTorch version (A) being a more widely-adopted reimplementation of the original work (B), making them direct competitors rather than complementary tools.
About pytorch-slimming
foolwood/pytorch-slimming
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
This tool helps machine learning engineers and researchers make their deep learning models smaller and faster. It takes a pre-trained convolutional neural network and reduces its size by identifying and removing less important parts of the network. The output is a more efficient model that maintains high accuracy, suitable for deployment in resource-constrained environments.
About slimming
liuzhuang13/slimming
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
This project helps machine learning engineers or researchers optimize deep learning models for deployment. It takes a pre-trained convolutional neural network and reduces its size and computational requirements. The output is a smaller, more efficient model that maintains the original accuracy, ideal for environments with limited resources.
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