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Ternarized Neural Network for Image Classification

This repository contains a Pytorch implementation of the paper "Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy".

If you find this project useful to you, please cite our work:

@article{he2018optimize,
  title={Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy},
  author={He, Zhezhi and Gong, Boqing and Fan, Deliang},
  journal={IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2019}
}

Table of Contents

Dependencies:

  • Python 3.6 (Anaconda)
  • Pytorch 4.1

Usage

For training the new model or evaluating the pretrained model, please use the following command in terminal. Remeber to revise the bash code with correct dataset/model path.

CIFAR-10:

bash train_CIFAR10.sh

ImageNet:

bash train_ImageNet.sh

In order to get the bash code run correctly, in train_ImageNet.sh file, please modify the PYTHON environment, imagenet_path imagenent dataset path, and pretrained_model trained model path. Use --evaluate to get validation accuracy.

#!/usr/bin/env sh

PYTHON=/home/elliot/anaconda3/envs/pytorch_041/bin/python
imagenet_path=
pretrained_model=

############ directory to save result #############
DATE=`date +%Y-%m-%d`

if [ ! -d "$DIRECTORY" ]; then
    mkdir ./save
    mkdir ./save/${DATE}/
fi

############ Configurations ###############
model=resnet18b_fq_lq_tern_tex_4
dataset=imagenet
epochs=50
batch_size=256
optimizer=Adam
# add more labels as additional info into the saving path
label_info=test

$PYTHON main.py --dataset ${dataset} \
    --data_path ${imagenet_path}  --arch ${model} \ 
    --save_path ./save/${DATE}/${dataset}_${model}_${epochs}_${label_info} \
    --epochs ${epochs} --learning_rate 0.0001 --optimizer ${optimizer} \
    --schedule 30 40 45  --gammas 0.2 0.2 0.5 \
    --batch_size ${batch_size} --workers 8 --ngpu 2  \
    --print_freq 100 --decay 0.000005 \
    --resume ${pretrained_model} --evaluate\
    --model_only  --fine_tune\

Results

Trained models can be downloaded with the links provided (Google Drive).

ResNet-20/32/44/56 on CIFAR-10:

The entire network is ternarized (including first and last layer) for ResNet-20/32/44/56 on CIFAR-10. Note that, all the CIFAR-10 experiments are directly training from scratch, where no pretrained model is used. Users can ternarized the model from the pretrained model. Since CIFAR-10 is a toy dataset, I did not upload the trained model.

ResNet-20 ResNet-32 ResNet-44 ResNet-56
Full-Precison 91.70% 92.36% 92.47% 92.68%
Ternarized 91.65% 92.48% 92.71% 92.86%

AlexNet on ImageNet:

First and Last Layer Top1/Top5 Accuracy
AlexNet (Full-Precision) Full-Precision 61.78%/82.87%
AlexNet (Ternarized) Full-Precision 58.59%/80.44%
AlexNet (Ternarized) Ternarized 57.21%/79.41%

ResNet-18/34/50/101 on ImageNet:

The pretrained models of full-precision baselines are from Pytorch.

ResNet-18 ResNet-34 ResNet-50
Full-Precision 69.75%/89.07% 73.31%/91.42% 76.13%/92.86%
Ternarized 66.01%/86.78% 70.95%/89.89% 74.00%/91.77%

ResNet-18 on ImageNet with Residual Expansion Layer (REL): For reducing the accuracy drop caused by the aggresive model compression, we append the residual expansion layers to compensate the accuracy gap. Considering the aforementioned ternarized ResNet-18 is t_ex=1 (i.e. without REL).

ResNet-18 first and last layer Top1/Top5 Accuracy
t_ex=2 Tern 68.35%/88.20%
t_ex=4 Tern 69.44%/88.91%

Task list

  • Upload Trained models for CIFAR-10 and ImageNet datasets.

  • Encoding the weights of residual expansion layers to further reduce the model size (i.e., memory usage).

  • Optimizing the thresholds chosen for the residual expansion layers.