Skip to content
This repository has been archived by the owner on Nov 18, 2019. It is now read-only.

Latest commit

 

History

History
14 lines (12 loc) · 1.17 KB

README.md

File metadata and controls

14 lines (12 loc) · 1.17 KB
Raw

Quantized and binarized operations in mxnet

The structure should be fairly self explanatory.

  • src quantized operators that get compiled into the mxnet library
  • tools tools developed for working with binarized networks
    • model-converter: a tool to pack a trained binary model so that each weight just uses 1 bit of storage.
    • docker: a simple Dockerfile to setup a container for mxnet with all dependencies, build it with docker build -t mxnet, then run it with docker run -t -i mxnet
    • amalgamate_mxnet_mac.sh: this script will amalgamate the mxnet library into a single file and perform some modifications needed to compile on macOS and iOS
  • test unit tests for those operators // @todo
  • examples several projects demonstrating the binarized/quantized operators
    • binary_mnist train and predict with a LeNet on the MNIST dataset
    • binary-imagenet1k train and predict with a ResNet18 on the imagenet or cifar10 dataset
  • binary_models a collection of pre-trained binarized models over MNIST, CIFAR-10 and ImageNet dataset. The model accuracy has been described in our paper.