# Binarized Neural Networks on the ImageNet Classification Task

Source: [Binarized Neural Networks on the ImageNet Classification Task](<https://www.sota2.com/research/paper/68fa62ee1e209131ce9fbee2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:29:18.716Z

Authors: Xundong Wu, Yong Wu, Yong Zhao

Published: 2016-04-11T18:39:33.000Z

## Abstract

We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance. With a moderate size network of 13 layers, we obtained top-5 classification accuracy rate of 84.1 % on validation set through network distillation, much better than previous published results of 73.2% on XNOR network and 69.1% on binarized GoogleNET. We expect networks of better performance can be obtained by following our current strategies. We provide a detailed discussion and preliminary analysis on strategies used in the network training.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1604.03058>)

[PDF](<https://arxiv.org/pdf/1604.03058.pdf>)