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This repository provides code for paper 《Lightweight Deep Neural Network Model With Padding-free Downsampling》

Framework & Performance

framework performa

Experiment

I. For the CIFAR-100 dataset, using the first set of hyperparameters
I. For the CIFAR-100 dataset, using the second set of hyperparameters
II. For Stanford Dogs dataset
III. For ImageNet dataset
IV. Inference Latency
V. Ablation Experiments on CIFAR-100
VI. Comparison with other downsampling (EfficientFormerv2)
VII. For VegFru-292 dataset

Instructions for use

Getting started
Take MobileNetv3 as an example, when using our module
Acknowledgement

For the CIFAR-100 dataset, using the first set of hyperparameters

The first set of hyperparameters follows the settings of Haase et al.

《Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets》
 Daniel Haase∗   Manuel Amthor∗
ZEISS Microscopy   ZEISS Microscopy

Orig——CIFAR-100

Model Parameters FLOPs Accuracy
MobileNetv3-large 3.066M 68.5M 75.37%
MobileNetv3-large(BSConv-S) 3.066M 68.5M 77.87%
ResNet-20 0.278M 41.4M 68.12%
ResNet-110(BSConv-U) 0.245M 41.8M 71.58%
WideResNet-40-3 5.056M 735.8M 76.23%
WideResNet-40-8(BSConv-U) 4.286M 675.1M 77.79%

Ours——CIFAR-100

Model Parameters FLOPs Accuracy
MobileNetv3-large 3.067M 54.6M ↓ 75.71%
MobileNetv3-large(BSConv-S) 3.067M 54.6M ↓ 78.36%
ResNet-20 0.282M 37.8M ↓ 68.30%
ResNet-110(BSConv-U) 0.249M 38.6M ↓ 71.62%
WideResNet-40-3 5.287M 668.7M ↓ 76.28%
WideResNet-40-8(BSConv-U) 4.457M 615.6M ↓ 78.05%

For the CIFAR-100 dataset, using the second set of hyperparameters

Orig——CIFAR-100

Model Parameters FLOPs Accuracy
MobileNetv3-large 4.330M 68.8M 76.00%
Parc-MobileNet-v2 2.348M 91.3M 76.20%
GhostNet 4.029M 44.6M 74.00%
ShuffleNet-v2 1.356M 46.2M 70.90%

Ours——CIFAR-100

Model Parameters FLOPs Accuracy
MobileNetv3-large 4.331M 54.7M ↓ 76.60%
Parc-MobileNet-v2 2.348M 73.0M ↓ 76.60%
GhostNet 4.030M 34.8M ↓ 74.10%
ShuffleNet-v2 1.358M 35.7M ↓ 71.50%

For Stanford Dogs dataset

Orig——Stanford Dogs

Model Parameters FLOPs Accuracy
MobileNetv3-large 3.086M 230.1M 51.07%
MobileNetv3-large-bsconvs 3.086M 230.1M 59.68%

Ours——Stanford Dogs

Model Parameters FLOPs Accuracy
MobileNetv3-large 3.087M 212.6M ↓ 54.11%
MobileNetv3-large-bsconvs 3.087M 212.6M ↓ 60.79%

For ImageNet dataset

Orig——ImageNet

Model Parameters FLOPs Accuracy
MobileNetv3-large 5.480M 232.5M 69.50%

Ours——ImageNet

Model Parameters FLOPs Accuracy
MobileNetv3-large 5.481M 214.9M ↓ 69.50%

Inference Latency

Orig——Latency

Model AMD Ryzen 5 5600H MediaTek Tiangui 1000+
MobileNetv3-large 8.5ms 27.0ms
Parc-MobileNet-v2 8.7ms 37.4ms
GhostNet 11.4ms 36.6ms
ShuffleNet-v2 6.2ms 19.4ms

Ours——Latency

Model AMD Ryzen 5 5600H MediaTek Tiangui 1000+
MobileNetv3-large 9.0ms 26.3ms ↓
Parc-MobileNet-v2 9.3ms 34.0ms ↓
GhostNet 11.7ms 26.8ms ↓
ShuffleNet-v2 7.4ms 18.8ms ↓

Ablation Experiments on CIFAR-100

Model orig +Stem +Downsampling ours
MobileNetv3-large 76.0% 75.9% 76.4% 76.6%↑
Parc-MobileNet-v2 76.2% 76.6% 76.4% 76.6%↑
GhostNet 76.0% 74.2% 73.8% 74.1%↑
ShuffleNet-v2 70.9% 72.0% 70.4% 71.5%↑

Comparison with other downsampling (EfficientFormerv2)

《Rethinking Vision Transformers for MobileNet Size and Speed》
 Yanyu Li
Snap Inc. Northeastern University

EfficientFormerv2-Downsampling——CIFAR-100

Model Parameters FLOPs Accuracy
MobileNetv3-large 4.317M 78.0M 75.80%
Parc-MobileNet-v2 2.558M 97.5M 75.70%
GhostNet 4.092M 58.3M 74.30%
ShuffleNet-v2 2.804M 84.1M 70.60%

Ours——CIFAR-100

Model Parameters FLOPs Accuracy
MobileNetv3-large 4.331M 54.7M 76.60%
Parc-MobileNet-v2 2.348M 73.0M 76.60%
GhostNet 4.030M 34.8M 74.10%
ShuffleNet-v2 1.358M 35.7M 71.50%

For VegFru-292 dataset

Orig——VegFru-292

Model Parameters FLOPs Accuracy
MobileNetv3-large 4.576M 224.5M 89.20%
Parc-MobileNet-v2 2.605M 314.8M 89.10%
GhostNet 4.276M 147.9M 89.60%
ShuffleNet-v2 1.553M 148.1M 88.40%

Ours——VegFru-292

Model Parameters FLOPs Accuracy
MobileNetv3-large 4.577M 205.7M ↓ 89.90%
Parc-MobileNet-v2 2.605M 305.5M ↓ 90.00%
GhostNet 4.276M 136.9M ↓ 90.30%
ShuffleNet-v2 1.554M 130.7M ↓ 87.70%

Getting started

  • For the BSConv folder, when using it for the first time, use "--download" to download the dataset.
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_mobilenetv3_large_w1 --download --gpu-id 0
  • "--data-root" is the dataset path, "--dataset" is the dataset name, "--architecture" is the model name.
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_mobilenetv3_large_w1 --gpu-id 0
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_mobilenetv3_large_w1_bsconvs_p1d6 --gpu-id 0
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_wrn40_3 --gpu-id 0
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_wrn40_8_bsconvu --gpu-id 0
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_resnet20 --gpu-id 0
python bsconv_pytorch_train.py --data-root cifar100 --dataset cifar100 --architecture cifar_resnet110_bsconvu --gpu-id 0

Take MobileNetv3 as an example, when using our module.

  • Replace init_conv on line 321 in the mobilenet.py file with our stem layer.
self.backbone.add_module("init_conv", StemBlock(in_channels, init_conv_channels))
  • Uncomment the if stride==2 on lines 157, 168, 237, and 261 in the common.py file.
        if stride == 2:
          self.maxx = nn.MaxPool2d(kernel_size=3, stride=2,padding=0)
        if self.stride == 2:
          b = self.maxx(b)
          return x + b
    if stride ==2:
          return ConvBlock(
             in_channels=channels,
             out_channels=channels,
             kernel_size=3,
             stride=stride,
             padding=0,
             groups=channels,
             use_bn=use_bn,
             activation=activation)
    if stride ==2 :
        return ConvBlock(
             in_channels=channels,
             out_channels=channels,
             kernel_size=5,
             stride=stride,
             padding=1,
             groups=channels,
             use_bn=use_bn,
             activation=activation)

Acknowledgement

Thanks to BSConv and EfficientFormerv2. Our code is based on the BSConv library and EfficientFormerv2 library.