电子学报 ›› 2023, Vol. 51 ›› Issue (2): 355-364.DOI: 10.12263/DZXB.20210559
许新征1,2, 李杉1
收稿日期:
2021-05-05
修回日期:
2021-07-21
出版日期:
2023-02-25
作者简介:
基金资助:
XU Xin-zheng1,2, LI Shan1
Received:
2021-05-05
Revised:
2021-07-21
Online:
2023-02-25
Published:
2023-04-14
Supported by:
摘要:
本文从卷积神经网络模型的网络结构入手,利用特征复用的思想,设计了高效的特征膨胀卷积模块.该模块减少了标准卷积模块的输出通道数,引入了多分支结构.通过各个分支上的廉价操作对标准卷积操作的输出特征图进行变换和融合,产生新的特征图.模块的最终输出由各个分支上生成的特征图进行合并连接得到.特征膨胀卷积模块利用特征复用思想复用模型中的特征,在降低模型计算量的同时,丰富了特征图隐含的信息,提高了模型的性能.最后,将特征膨胀卷积模块代替标准卷积模块,设计了轻量化的VGG16(Visual Geometry Group 16-Layer)模型和残差结构,并且在CIFAR数据集和ILSVRC2012(ImageNet Large Scale Visual Recognition Challenge 2012)数据集上取得了较好的分类效果.
中图分类号:
许新征, 李杉. 基于特征膨胀卷积模块的轻量化技术研究[J]. 电子学报, 2023, 51(2): 355-364.
Xin-zheng XU, Shan LI . Research of Lightweight Convolution Neural Network Based on Feature Expansion Convolution[J]. Acta Electronica Sinica, 2023, 51(2): 355-364.
分支类型 | ||||
---|---|---|---|---|
4 | 1 | 2 | 25 | 50 |
4 | 2 | 1 | 50 | 25 |
8 | 1 | 6 | 12.5 | 75 |
8 | 2 | 5 | 25 | 62.5 |
8 | 3 | 4 | 37.5 | 50 |
8 | 4 | 3 | 50 | 37.5 |
8 | 5 | 2 | 62.5 | 25 |
8 | 6 | 1 | 75 | 12.5 |
表1 Ghost特征与Fuse特征的组合比例
分支类型 | ||||
---|---|---|---|---|
4 | 1 | 2 | 25 | 50 |
4 | 2 | 1 | 50 | 25 |
8 | 1 | 6 | 12.5 | 75 |
8 | 2 | 5 | 25 | 62.5 |
8 | 3 | 4 | 37.5 | 50 |
8 | 4 | 3 | 50 | 37.5 |
8 | 5 | 2 | 62.5 | 25 |
8 | 6 | 1 | 75 | 12.5 |
Layer | Module | 𝑚, [ |
---|---|---|
Block1 | FExpand4 | 16×4, [1,1,2] |
Block2 | FExpand4 | 16×4, [1,1,2] |
Pooling | MaxPooling | |
Block3 | FExpand4 | 32×4, [1,1,2] |
Block4 | FExpand4 | 32×4, [1,1,2] |
Pooling | MaxPooling | |
Block5 | FExpand4 | 64×4, [1,1,2] |
Block6 | FExpand4 | 64×4, [1,1,2] |
Block7 | FExpand4 | 64×4, [1,1,2] |
Pooling | MaxPooling | |
Block8 | FExpand4 | 64×4, [1,1,2] |
Block9 | FExpand4 | 64×8, [1,1,6] |
Block10 | FExpand8 | 64×8, [1,1,6] |
Pooling | MaxPooling | |
Block11 | FExpand8 | 64×8, [1,1,6] |
Block12 | FExpand8 | 64×8, [1,1,6] |
Block13 | FExpand8 | 64×8, [1,1,6] |
Pooling | AvePooling | |
Classfier | Fully Connected+Softmax |
表2 基于特征膨胀卷积模块的最优轻量化VGG16模型
Layer | Module | 𝑚, [ |
---|---|---|
Block1 | FExpand4 | 16×4, [1,1,2] |
Block2 | FExpand4 | 16×4, [1,1,2] |
Pooling | MaxPooling | |
Block3 | FExpand4 | 32×4, [1,1,2] |
Block4 | FExpand4 | 32×4, [1,1,2] |
Pooling | MaxPooling | |
Block5 | FExpand4 | 64×4, [1,1,2] |
Block6 | FExpand4 | 64×4, [1,1,2] |
Block7 | FExpand4 | 64×4, [1,1,2] |
Pooling | MaxPooling | |
Block8 | FExpand4 | 64×4, [1,1,2] |
Block9 | FExpand4 | 64×8, [1,1,6] |
Block10 | FExpand8 | 64×8, [1,1,6] |
Pooling | MaxPooling | |
Block11 | FExpand8 | 64×8, [1,1,6] |
Block12 | FExpand8 | 64×8, [1,1,6] |
Block13 | FExpand8 | 64×8, [1,1,6] |
Pooling | AvePooling | |
Classfier | Fully Connected+Softmax |
( | Weights(M) | Reduce(%) | Acc(%) | ||
---|---|---|---|---|---|
FExpand4 | 1,1,2 | 50% | 3.91 | 74.18 | 92.7 |
FExpand4 | 1,2,1 | 25% | 3.80 | 73.44 | 92.7 |
FExpand8 | 1,1,6 | 75% | 2.02 | 86.28 | 92.5 |
FExpand8 | 1,2,5 | 62.5% | 1.99 | 86.48 | 92.5 |
FExpand8 | 1,3,4 | 50% | 1.96 | 86.68 | 92.4 |
FExpand8 | 1,4,3 | 37.5 | 1.94 | 86.82 | 91.9 |
VGG16 | - | - | 14.72 | - | 92.3 |
表3 基于特征膨胀卷积模块的轻量化VGG16模型在CIFAR10数据集上的实验结果
( | Weights(M) | Reduce(%) | Acc(%) | ||
---|---|---|---|---|---|
FExpand4 | 1,1,2 | 50% | 3.91 | 74.18 | 92.7 |
FExpand4 | 1,2,1 | 25% | 3.80 | 73.44 | 92.7 |
FExpand8 | 1,1,6 | 75% | 2.02 | 86.28 | 92.5 |
FExpand8 | 1,2,5 | 62.5% | 1.99 | 86.48 | 92.5 |
FExpand8 | 1,3,4 | 50% | 1.96 | 86.68 | 92.4 |
FExpand8 | 1,4,3 | 37.5 | 1.94 | 86.82 | 91.9 |
VGG16 | - | - | 14.72 | - | 92.3 |
Layer | Weights(M) | Acc(%) |
---|---|---|
j=1 | 2.023 | 92.5 |
j=2 | 2.023 | 92.5 |
j=3 | 2.033 | 92.5 |
j=4 | 2.053 | 92.5 |
j=5 | 2.093 | 92.5 |
j=6 | 2.173 | 92.6 |
j=7 | 2.243 | 92.6 |
j=8 | 2.403 | 92.6 |
j=9 | 2.703 | 92.7 |
j=10 | 3.003 | 92.7 |
j=11 | 3.313 | 92.7 |
j=12 | 3.613 | 92.7 |
j=13 | 3.913 | 92.7 |
表4 基于两种特征膨胀卷积模块的改进VGG16模型在CIFAR10数据集上的实验结果
Layer | Weights(M) | Acc(%) |
---|---|---|
j=1 | 2.023 | 92.5 |
j=2 | 2.023 | 92.5 |
j=3 | 2.033 | 92.5 |
j=4 | 2.053 | 92.5 |
j=5 | 2.093 | 92.5 |
j=6 | 2.173 | 92.6 |
j=7 | 2.243 | 92.6 |
j=8 | 2.403 | 92.6 |
j=9 | 2.703 | 92.7 |
j=10 | 3.003 | 92.7 |
j=11 | 3.313 | 92.7 |
j=12 | 3.613 | 92.7 |
j=13 | 3.913 | 92.7 |
Models | Weights(M) | GMAC | CIFAR10(%) | CIFAR100(%) |
---|---|---|---|---|
VGG-16 [ | 14.72 | 0.316 | 92.3 | 72.3 |
FitNet [ | 2.5 | 0.382 | 91.61 | 64.96 |
Highway Network [ | 2.3 | 0.372 | 92.24 | 67.76 |
DenseNet121[ | 15.3 | 0.143 | 94.81 | 80.36 |
ResNet-50 [ | 25.557 | 0.086 | 93.03 | 77.78 |
SE-Net [ | 47.7 | 0.259 | 94.79 | 80.02 |
CondenseNet [ | 0.52 | 0.006 | 94.48 | 76.36 |
Ghost-VGG-16 [ | 7.387 | 0.156 | 93.7 | 68.80 |
Ghost-ResNet50 [ | 12.36 | 0.02 | 92.7 | 72.6 |
VGG16-our | 2.703 | 0.189 | 92.7 | 73.4 |
ResNet50-our | 5.48 | 0.01 | 93.3 | 78.1 |
表5 轻量化VGG16模型在CIFAR10和CIFAR100数据集上的测试结果
Models | Weights(M) | GMAC | CIFAR10(%) | CIFAR100(%) |
---|---|---|---|---|
VGG-16 [ | 14.72 | 0.316 | 92.3 | 72.3 |
FitNet [ | 2.5 | 0.382 | 91.61 | 64.96 |
Highway Network [ | 2.3 | 0.372 | 92.24 | 67.76 |
DenseNet121[ | 15.3 | 0.143 | 94.81 | 80.36 |
ResNet-50 [ | 25.557 | 0.086 | 93.03 | 77.78 |
SE-Net [ | 47.7 | 0.259 | 94.79 | 80.02 |
CondenseNet [ | 0.52 | 0.006 | 94.48 | 76.36 |
Ghost-VGG-16 [ | 7.387 | 0.156 | 93.7 | 68.80 |
Ghost-ResNet50 [ | 12.36 | 0.02 | 92.7 | 72.6 |
VGG16-our | 2.703 | 0.189 | 92.7 | 73.4 |
ResNet50-our | 5.48 | 0.01 | 93.3 | 78.1 |
Models | Top-1 error | Top-5 error |
---|---|---|
VGG-16 [ | 71.93 | 90.67 |
DenseNet121[ | 76.39 | 93.34 |
ResNet-50 [ | 77.15 | 93.29 |
Ghost-ResNet [ | 75 | 92.3 |
VGG16-our | 75.68 | 92.6 |
ResNet50-our | 77.16 | 93.34 |
表6 轻量化VGG16模型在ImageNet数据集上的测试结果 (%)
Models | Top-1 error | Top-5 error |
---|---|---|
VGG-16 [ | 71.93 | 90.67 |
DenseNet121[ | 76.39 | 93.34 |
ResNet-50 [ | 77.15 | 93.29 |
Ghost-ResNet [ | 75 | 92.3 |
VGG16-our | 75.68 | 92.6 |
ResNet50-our | 77.16 | 93.34 |
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