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.
576 stars. No commits in the last 6 months.
Use this if you need to shrink your deep learning models to run faster or fit onto devices with less memory, like mobile phones or embedded systems, without losing performance.
Not ideal if you are developing a new model from scratch and are not concerned with its size or efficiency for deployment.
Stars
576
Forks
75
Language
Lua
License
MIT
Category
Last pushed
Jul 14, 2019
Commits (30d)
0
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