Skip to content
This repository has been archived by the owner on Jul 6, 2023. It is now read-only.

Implementation for the paper "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization"


Notifications You must be signed in to change notification settings


Folders and files

Last commit message
Last commit date

Latest commit



31 Commits

Repository files navigation

Rethinking Binarized Neural Network Optimization

arXiv:1906.02107 License: Apache 2.0 Code style: black

Implementation for paper "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization".

A poster illustrating the proposed algorithm and its relation to the previous BNN optimization strategy is included at ./poster.pdf.

Note: Bop is now added to Larq, the open source training library for BNNs. We recommend using the Larq implementation of Bop: it is compatible with more versions of TensorFlow and will be more actively maintained.


You can also check out one of our prebuilt docker images.


This is a complete Python module. To install it in your local Python environment, cd into the folder containing and run:

pip install -e .


To train a model locally, you can use the cli:

bnno train binarynet --dataset cifar10

Reproduce Paper Experiments

Hyperparameter Analysis (section 5.1)

To reproduce the runs exploring various hyperparameters, run:

bnno train binarynet \
    --dataset cifar10 \
    --preprocess-fn resize_and_flip \
    --hparams-set bop \
    --hparams threshold=1e-6,gamma=1e-3

where you use the appropriate values for threshold and gamma.

CIFAR-10 (section 5.2)

To achieve the accuracy in the paper of 91.3%, run:

bnno train binarynet \
    --dataset cifar10 \
    --preprocess-fn resize_and_flip \
    --hparams-set bop_sec52 \

ImageNet (section 5.3)

To reproduce the reported results on ImageNet, run:

bnno train alexnet --dataset imagenet2012 --hparams-set bop
bnno train xnornet --dataset imagenet2012 --hparams-set bop
bnno train birealnet --dataset imagenet2012 --hparams-set bop

This should give the results listed below. Click on the tensorboard icons to see training and validation accuracy curves of the reported runs.

Network Bop - top-1 accuracy
Binary Alexnet 41.1% tensorboard
XNOR-Net 45.9% tensorboard
Bi-Real Net 56.6% tensorboard