network-slimming and slimming
These are competing implementations of the same paper's method—both reproduce the ICCV 2017 Network Slimming approach for channel pruning via batch normalization scaling, so practitioners would typically choose one based on code quality or community activity rather than use both.
About network-slimming
Eric-mingjie/network-slimming
Network Slimming (Pytorch) (ICCV 2017)
This project helps machine learning engineers and researchers make their image classification models smaller and faster without losing accuracy. You provide an existing convolutional neural network (like VGG, ResNet, or DenseNet) trained on image datasets such as CIFAR-10 or CIFAR-100. The output is a significantly more compact version of your model that retains high classification performance, making it more efficient for deployment.
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work