| 模型 Model | 参数量 (M) No. of parameters | 计算量 (G) Floating point operations | 准确率/% Accuracy | |
| Top5 | Top1 | |||
| ShuffleNet-V2 0.5 | 0.4 | 0.04 | 72.74 | 41.83 |
| ShuffleNet-V2 1.0 | 1.4 | 0.15 | 86.21 | 59.65 |
| ShuffleNet-V2 1.5 | 2.6 | 0.30 | 90.08 | 66.56 |
| ShuffleNet-V2 2.0 | 5.6 | 0.56 | 93.06 | 72.79 |
| SqueezeNet 1.0 | 0.8 | 0.75 | 78.48 | 49.68 |
| SqueezeNet 1.1 | 0.8 | 0.30 | 78.12 | 50.14 |
| MobileNet-V3-Small | 1.6 | 0.06 | 87.90 | 61.74 |
| MobileNet-V2 | 2.4 | 0.31 | 91.69 | 69.16 |
| MobileNet-V3-Large | 4.3 | 0.23 | 93.57 | 73.27 |
| MnasNet 0.5 | 1.1 | 0.11 | 88.13 | 62.60 |
| MnasNet 0.75 | 2.0 | 0.22 | 91.44 | 69.20 |
| MnasNet 1.0 | 3.2 | 0.32 | 92.81 | 72.70 |
| MnasNet 1.3 | 5.1 | 0.54 | 94.41 | 76.64 |
| EfficientNet B0 | 4.1 | 0.40 | 94.63 | 76.00 |
| EfficientNet B1 | 6.6 | 0.60 | 94.95 | 77.96 |
| ResNet 18 | 11.2 | 1.80 | 94.66 | 76.85 |
| VGG 11 | 129.2 | 7.60 | 94.25 | 75.82 |
| VGG 13 | 129.4 | 11.30 | 94.38 | 76.46 |
| VGG 16 | 134.7 | 15.50 | 94.63 | 78.19 |
| VGG 19 | 140.0 | 19.60 | 95.25 | 78.19 |
| MobileViT-XXS | 1.0 | 0.33 | 84.98 | 55.96 |
| MobileViT-XS | 2.0 | 0.90 | 89.55 | 64.34 |
| MobileViT-S | 5.1 | 1.75 | 93.64 | 72.93 |
| M2CNet-S | 1.8 | 0.23 | 92.46 | 71.09 |
| M2CNet-B | 3.5 | 0.39 | 94.16 | 75.32 |
| M2CNet-L | 5.8 | 0.60 | 95.31 | 78.39 |
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CIFAR100数据集模型对比结果
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IP102 数据集害虫分级分类体系
作物
Crop害虫类别
Pest class训练集
Training set测试集
Test set水稻 Rice 14 6734 1683 玉米 Corn 13 11212 2803 小麦 Wheat 9 2734 684 甜菜 Sugarbeet 8 3536 884 苜蓿 Alfalfa 13 8312 2078 葡萄 Grape 16 14041 3510 柑橘 Orange 19 5818 1455 芒果 Mango 10 7790 1948 总计 Total 102 60177 15045 -
M2CNet-S/B/L的网络架构 1)
阶段
Stage输出尺寸
Output size层名称
Name of layerM2CNet-S M2CNet-B M2CNet-L 1 $ 56\times 56 $ Conv.下采样 $ 4\times \mathrm{4,36},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\;4 $ $ 4\times \mathrm{4,48},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\;4 $ $ 56\times 56 $ 深度可分离卷积 $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,36}\\ 3\times \mathrm{1,1}\times \mathrm{3,36}\\ \begin{array}{c}{H}_{1}=1,{s}_{1}=4\\ {R}_{1}=4\end{array}\end{array} \right]\times 1 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,48}\\ 3\times \mathrm{1,1}\times \mathrm{3,48}\\ \begin{array}{c}{H}_{1}=1,{s}_{1}=4\\ {R}_{1}=4\end{array}\end{array} \right]\times 1 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,48}\\ 3\times \mathrm{1,1}\times \mathrm{3,48}\\ \begin{array}{c}{H}_{1}=1,{s}_{1}=4\\ {R}_{1}=4\end{array}\end{array} \right]\times 1 $ 多层循环全连接 全局子采样注意力 轻量级前馈网络 2 $ 28\times 28 $ Conv.下采样 $ 2\times \mathrm{2,72},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\; 2 $ $ 2\times \mathrm{2,96},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\; 2 $ $ 28\times 28 $ 深度可分离卷积 $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,72}\\ 3\times \mathrm{1,1}\times \mathrm{3,72}\\ \begin{array}{c}{H}_{1}=2,{s}_{1}=2\\ {R}_{1}=4\end{array}\end{array} \right]\times 2 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,96}\\ 3\times \mathrm{1,1}\times \mathrm{3,96}\\ \begin{array}{c}{H}_{1}=2,{s}_{1}=2\\ {R}_{1}=4\end{array}\end{array} \right]\times 1 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,96}\\ 3\times \mathrm{1,1}\times \mathrm{3,96}\\ \begin{array}{c}{H}_{1}=2,{s}_{1}=2\\ {R}_{1}=4\end{array}\end{array} \right]\times 2 $ 多层循环全连接 全局子采样注意力 轻量级前馈网络 3 $ 14\times 14 $ Conv.下采样 $ 2\times \mathrm{2,144},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\; 2 $ $ 2\times \mathrm{2,192},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\; 2 $ $ 14\times 14 $ 深度可分离卷积 $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,144}\\ 3\times \mathrm{1,1}\times \mathrm{3,144}\\ \begin{array}{c}{H}_{1}=4,{s}_{1}=2\\ {R}_{1}=4\end{array}\end{array} \right]\times 3 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,192}\\ 3\times \mathrm{1,1}\times \mathrm{3,192}\\ \begin{array}{c}{H}_{1}=4,{s}_{1}=2\\ {R}_{1}=4\end{array}\end{array} \right]\times 4 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,192}\\ 3\times \mathrm{1,1}\times \mathrm{3,192}\\ \begin{array}{c}{H}_{1}=4,{s}_{1}=2\\ {R}_{1}=4\end{array}\end{array} \right]\times 6 $ 多层循环全连接 全局子采样注意力 轻量级前馈网络 4 $ 7\times 7 $ Conv.下采样 $ 2\times \mathrm{2,288},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e}\; 2 $ $ 2\times \mathrm{2,384},\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{d}\mathrm{e} \;2 $ $ 7\times 7 $ 深度可分离卷积 $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,288}\\ 3\times \mathrm{1,1}\times \mathrm{3,288}\\ \begin{array}{c}{H}_{1}=8,{s}_{1}=1\\ {R}_{1}=4\end{array}\end{array} \right]\times 2 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,384}\\ 3\times \mathrm{1,1}\times \mathrm{3,384}\\ \begin{array}{c}{H}_{1}=8,{s}_{1}=1\\ {R}_{1}=4\end{array}\end{array} \right]\times 2 $ $ \left[ \begin{array}{c}3\times \mathrm{3,1}\times \mathrm{1,384}\\ 3\times \mathrm{1,1}\times \mathrm{3,384}\\ \begin{array}{c}{H}_{1}=8,{s}_{1}=1\\ {R}_{1}=4\end{array}\end{array} \right]\times 4 $ 多层循环全连接 全局子采样注意力 轻量级前馈网络 输出 Output $ 1\times 1 $ 全连接 100 参数量(M) No. of parameters 1.83 3.52 5.76 计算量(G) Floating point operations 0.23 0.39 0.60 1)输入图像大小默认为224像素×224像素，Conv.代表卷积操作，stride表示卷积的步幅，Hi和Si是第i个全局子采样注意力的头数和次采样大小，Ri是第i个轻量级前馈网络的特征尺寸缩放比
1) The input image size is 224×224 by default, Conv. stands for convolution operation, stride stands for convolution step, Hi and Si are the number of heads and subsampling size of the ith global subsampling, and Ri is the scaling ratio of the feature size of the ith lightweight feedforward network -
CIFAR100数据集模型对比结果
模型
Model参数量 (M)
No. of
parameters计算量 (G)
Floating
point
operations准确率/%
AccuracyTop5 Top1 ShuffleNet-V2 0.5 0.4 0.04 72.74 41.83 ShuffleNet-V2 1.0 1.4 0.15 86.21 59.65 ShuffleNet-V2 1.5 2.6 0.30 90.08 66.56 ShuffleNet-V2 2.0 5.6 0.56 93.06 72.79 SqueezeNet 1.0 0.8 0.75 78.48 49.68 SqueezeNet 1.1 0.8 0.30 78.12 50.14 MobileNet-V3-Small 1.6 0.06 87.90 61.74 MobileNet-V2 2.4 0.31 91.69 69.16 MobileNet-V3-Large 4.3 0.23 93.57 73.27 MnasNet 0.5 1.1 0.11 88.13 62.60 MnasNet 0.75 2.0 0.22 91.44 69.20 MnasNet 1.0 3.2 0.32 92.81 72.70 MnasNet 1.3 5.1 0.54 94.41 76.64 EfficientNet B0 4.1 0.40 94.63 76.00 EfficientNet B1 6.6 0.60 94.95 77.96 ResNet 18 11.2 1.80 94.66 76.85 VGG 11 129.2 7.60 94.25 75.82 VGG 13 129.4 11.30 94.38 76.46 VGG 16 134.7 15.50 94.63 78.19 VGG 19 140.0 19.60 95.25 78.19 MobileViT-XXS 1.0 0.33 84.98 55.96 MobileViT-XS 2.0 0.90 89.55 64.34 MobileViT-S 5.1 1.75 93.64 72.93 M2CNet-S 1.8 0.23 92.46 71.09 M2CNet-B 3.5 0.39 94.16 75.32 M2CNet-L 5.8 0.60 95.31 78.39
