147 lines
17 KiB
Markdown
147 lines
17 KiB
Markdown
# 移动端系列
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## 概述
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MobileNetV1是Google于2017年发布的用于移动设备或嵌入式设备中的网络。该网络将传统的卷积操作替换深度可分离卷积，即Depthwise卷积和Pointwise卷积的组合，相比传统的卷积操作，该组合可以大大节省参数量和计算量。与此同时，MobileNetV1也可以用于目标检测、图像分割等其他视觉任务中。
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MobileNetV2是Google继MobileNetV1提出的一种轻量级网络。相比MobileNetV1，MobileNetV2提出了Linear bottlenecks与Inverted residual block作为网络基本结构，通过大量地堆叠这些基本模块，构成了MobileNetV2的网络结构。最终，在FLOPS只有MobileNetV1的一半的情况下取得了更高的分类精度。
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ShuffleNet系列网络是旷视提出的轻量化网络结构，到目前为止，该系列网络一共有两种典型的结构，即ShuffleNetV1与ShuffleNetV2。ShuffleNet中的Channel Shuffle操作可以将组间的信息进行交换，并且可以实现端到端的训练。在ShuffleNetV2的论文中，作者提出了设计轻量级网络的四大准则，并且根据四大准则与ShuffleNetV1的不足，设计了ShuffleNetV2网络。
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MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络，为了进一步提升效果，将relu和sigmoid激活函数分别替换为hard_swish与hard_sigmoid激活函数，同时引入了一些专门减小网络计算量的改进策略。
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GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通过引入ghost module，大大减缓了传统深度网络中特征的冗余计算问题，使得网络的参数量和计算量大大降低。
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目前PaddleClas开源的的移动端系列的预训练模型一共有35个，其指标如图所示。从图片可以看出，越新的轻量级模型往往有更优的表现，MobileNetV3代表了目前主流的轻量级神经网络结构。在MobileNetV3中，作者为了获得更高的精度，在global-avg-pooling后使用了1x1的卷积。该操作大幅提升了参数量但对计算量影响不大，所以如果从存储角度评价模型的优异程度，MobileNetV3优势不是很大，但由于其更小的计算量，使得其有更快的推理速度。此外，我们模型库中的ssld蒸馏模型表现优异，从各个考量角度下，都刷新了当前轻量级模型的精度。由于MobileNetV3模型结构复杂，分支较多，对GPU并不友好，GPU预测速度不如MobileNetV1。GhostNet于2020年提出，通过引入ghost的网络设计理念，大大降低了计算量和参数量，同时在精度上也超过前期最高的MobileNetV3网络结构。
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## 精度、FLOPS和参数量
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| MobileNetV1_x0_25 | 0.514 | 0.755 | 0.506 | | 0.070 | 0.460 |
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| MobileNetV1_x0_5 | 0.635 | 0.847 | 0.637 | | 0.280 | 1.310 |
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| MobileNetV1_x0_75 | 0.688 | 0.882 | 0.684 | | 0.630 | 2.550 |
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| MobileNetV1 | 0.710 | 0.897 | 0.706 | | 1.110 | 4.190 |
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| MobileNetV1_ssld | 0.779 | 0.939 | | | 1.110 | 4.190 |
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| MobileNetV2_x0_25 | 0.532 | 0.765 | | | 0.050 | 1.500 |
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| MobileNetV2_x0_5 | 0.650 | 0.857 | 0.654 | 0.864 | 0.170 | 1.930 |
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| MobileNetV2_x0_75 | 0.698 | 0.890 | 0.698 | 0.896 | 0.350 | 2.580 |
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| MobileNetV2 | 0.722 | 0.907 | 0.718 | 0.910 | 0.600 | 3.440 |
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| MobileNetV2_x1_5 | 0.741 | 0.917 | | | 1.320 | 6.760 |
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| MobileNetV2_x2_0 | 0.752 | 0.926 | | | 2.320 | 11.130 |
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| MobileNetV2_ssld | 0.7674 | 0.9339 | | | 0.600 | 3.440 |
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| MobileNetV3_large_<br>x1_25 | 0.764 | 0.930 | 0.766 | | 0.714 | 7.440 |
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| MobileNetV3_large_<br>x1_0 | 0.753 | 0.923 | 0.752 | | 0.450 | 5.470 |
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| MobileNetV3_large_<br>x0_75 | 0.731 | 0.911 | 0.733 | | 0.296 | 3.910 |
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| MobileNetV3_large_<br>x0_5 | 0.692 | 0.885 | 0.688 | | 0.138 | 2.670 |
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| MobileNetV3_large_<br>x0_35 | 0.643 | 0.855 | 0.642 | | 0.077 | 2.100 |
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| MobileNetV3_small_<br>x1_25 | 0.707 | 0.895 | 0.704 | | 0.195 | 3.620 |
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| MobileNetV3_small_<br>x1_0 | 0.682 | 0.881 | 0.675 | | 0.123 | 2.940 |
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| MobileNetV3_small_<br>x0_75 | 0.660 | 0.863 | 0.654 | | 0.088 | 2.370 |
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| MobileNetV3_small_<br>x0_5 | 0.592 | 0.815 | 0.580 | | 0.043 | 1.900 |
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| MobileNetV3_small_<br>x0_35 | 0.530 | 0.764 | 0.498 | | 0.026 | 1.660 |
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| MobileNetV3_small_<br>x0_35_ssld | 0.556 | 0.777 | 0.498 | | 0.026 | 1.660 |
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| MobileNetV3_large_<br>x1_0_ssld | 0.790 | 0.945 | | | 0.450 | 5.470 |
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| MobileNetV3_large_<br>x1_0_ssld_int8 | 0.761 | | | | | |
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| MobileNetV3_small_<br>x1_0_ssld | 0.713 | 0.901 | | | 0.123 | 2.940 |
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| ShuffleNetV2 | 0.688 | 0.885 | 0.694 | | 0.280 | 2.260 |
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| ShuffleNetV2_x0_25 | 0.499 | 0.738 | | | 0.030 | 0.600 |
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| ShuffleNetV2_x0_33 | 0.537 | 0.771 | | | 0.040 | 0.640 |
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| ShuffleNetV2_x0_5 | 0.603 | 0.823 | 0.603 | | 0.080 | 1.360 |
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| ShuffleNetV2_x1_5 | 0.716 | 0.902 | 0.726 | | 0.580 | 3.470 |
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| ShuffleNetV2_x2_0 | 0.732 | 0.912 | 0.749 | | 1.120 | 7.320 |
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| ShuffleNetV2_swish | 0.700 | 0.892 | | | 0.290 | 2.260 |
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| GhostNet_x0_5 | 0.668 | 0.869 | 0.662 | 0.866 | 0.082 | 2.600 |
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| GhostNet_x1_0 | 0.740 | 0.916 | 0.739 | 0.914 | 0.294 | 5.200 |
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| GhostNet_x1_3 | 0.757 | 0.925 | 0.757 | 0.927 | 0.440 | 7.300 |
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| GhostNet_x1_3_ssld | 0.794 | 0.945 | 0.757 | 0.927 | 0.440 | 7.300 |
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## 基于SD855的预测速度和存储大小
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| Models | Batch Size=1(ms) | Storage Size(M) |
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| MobileNetV1_x0_25 | 3.220 | 1.900 |
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| MobileNetV1_x0_5 | 9.580 | 5.200 |
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| MobileNetV1_x0_75 | 19.436 | 10.000 |
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| MobileNetV1 | 32.523 | 16.000 |
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| MobileNetV1_ssld | 32.523 | 16.000 |
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| MobileNetV2_x0_25 | 3.799 | 6.100 |
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| MobileNetV2_x0_5 | 8.702 | 7.800 |
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| MobileNetV2_x0_75 | 15.531 | 10.000 |
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| MobileNetV2 | 23.318 | 14.000 |
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| MobileNetV2_x1_5 | 45.624 | 26.000 |
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| MobileNetV2_x2_0 | 74.292 | 43.000 |
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| MobileNetV2_ssld | 23.318 | 14.000 |
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| MobileNetV3_large_x1_25 | 28.218 | 29.000 |
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| MobileNetV3_large_x1_0 | 19.308 | 21.000 |
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| MobileNetV3_large_x0_75 | 13.565 | 16.000 |
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| MobileNetV3_large_x0_5 | 7.493 | 11.000 |
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| MobileNetV3_large_x0_35 | 5.137 | 8.600 |
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| MobileNetV3_small_x1_25 | 9.275 | 14.000 |
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| MobileNetV3_small_x1_0 | 6.546 | 12.000 |
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| MobileNetV3_small_x0_75 | 5.284 | 9.600 |
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| MobileNetV3_small_x0_5 | 3.352 | 7.800 |
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| MobileNetV3_small_x0_35 | 2.635 | 6.900 |
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| MobileNetV3_small_x0_35_ssld | 2.635 | 6.900 |
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| MobileNetV3_large_x1_0_ssld | 19.308 | 21.000 |
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| MobileNetV3_large_x1_0_ssld_int8 | 14.395 | 10.000 |
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| MobileNetV3_small_x1_0_ssld | 6.546 | 12.000 |
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| ShuffleNetV2 | 10.941 | 9.000 |
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| ShuffleNetV2_x0_25 | 2.329 | 2.700 |
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| ShuffleNetV2_x0_33 | 2.643 | 2.800 |
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| ShuffleNetV2_x0_5 | 4.261 | 5.600 |
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| ShuffleNetV2_x1_5 | 19.352 | 14.000 |
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| ShuffleNetV2_x2_0 | 34.770 | 28.000 |
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| ShuffleNetV2_swish | 16.023 | 9.100 |
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| GhostNet_x0_5 | 5.714 | 10.000 |
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| GhostNet_x1_0 | 13.558 | 20.000 |
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| GhostNet_x1_3 | 19.982 | 29.000 |
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| GhostNet_x1_3_ssld | 19.982 | 29.000 |
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## 基于T4 GPU的预测速度
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| Models | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
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|-----------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
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| MobileNetV1_x0_25 | 0.68422 | 1.13021 | 1.72095 | 0.67274 | 1.226 | 1.84096 |
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| MobileNetV1_x0_5 | 0.69326 | 1.09027 | 1.84746 | 0.69947 | 1.43045 | 2.39353 |
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| MobileNetV1_x0_75 | 0.6793 | 1.29524 | 2.15495 | 0.79844 | 1.86205 | 3.064 |
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| MobileNetV1 | 0.71942 | 1.45018 | 2.47953 | 0.91164 | 2.26871 | 3.90797 |
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| MobileNetV1_ssld | 0.71942 | 1.45018 | 2.47953 | 0.91164 | 2.26871 | 3.90797 |
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| MobileNetV2_x0_25 | 2.85399 | 3.62405 | 4.29952 | 2.81989 | 3.52695 | 4.2432 |
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| MobileNetV2_x0_5 | 2.84258 | 3.1511 | 4.10267 | 2.80264 | 3.65284 | 4.31737 |
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| MobileNetV2_x0_75 | 2.82183 | 3.27622 | 4.98161 | 2.86538 | 3.55198 | 5.10678 |
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| MobileNetV2 | 2.78603 | 3.71982 | 6.27879 | 2.62398 | 3.54429 | 6.41178 |
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| MobileNetV2_x1_5 | 2.81852 | 4.87434 | 8.97934 | 2.79398 | 5.30149 | 9.30899 |
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| MobileNetV2_x2_0 | 3.65197 | 6.32329 | 11.644 | 3.29788 | 7.08644 | 12.45375 |
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| MobileNetV2_ssld | 2.78603 | 3.71982 | 6.27879 | 2.62398 | 3.54429 | 6.41178 |
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| MobileNetV3_large_x1_25 | 2.34387 | 3.16103 | 4.79742 | 2.35117 | 3.44903 | 5.45658 |
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| MobileNetV3_large_x1_0 | 2.20149 | 3.08423 | 4.07779 | 2.04296 | 2.9322 | 4.53184 |
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| MobileNetV3_large_x0_75 | 2.1058 | 2.61426 | 3.61021 | 2.0006 | 2.56987 | 3.78005 |
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| MobileNetV3_large_x0_5 | 2.06934 | 2.77341 | 3.35313 | 2.11199 | 2.88172 | 3.19029 |
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| MobileNetV3_large_x0_35 | 2.14965 | 2.7868 | 3.36145 | 1.9041 | 2.62951 | 3.26036 |
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| MobileNetV3_small_x1_25 | 2.06817 | 2.90193 | 3.5245 | 2.02916 | 2.91866 | 3.34528 |
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| MobileNetV3_small_x1_0 | 1.73933 | 2.59478 | 3.40276 | 1.74527 | 2.63565 | 3.28124 |
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| MobileNetV3_small_x0_75 | 1.80617 | 2.64646 | 3.24513 | 1.93697 | 2.64285 | 3.32797 |
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| MobileNetV3_small_x0_5 | 1.95001 | 2.74014 | 3.39485 | 1.88406 | 2.99601 | 3.3908 |
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| MobileNetV3_small_x0_35 | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 |
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| MobileNetV3_small_x0_35_ssld | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 |
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| MobileNetV3_large_x1_0_ssld | 2.20149 | 3.08423 | 4.07779 | 2.04296 | 2.9322 | 4.53184 |
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| MobileNetV3_small_x1_0_ssld | 1.73933 | 2.59478 | 3.40276 | 1.74527 | 2.63565 | 3.28124 |
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| ShuffleNetV2 | 1.95064 | 2.15928 | 2.97169 | 1.89436 | 2.26339 | 3.17615 |
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| ShuffleNetV2_x0_25 | 1.43242 | 2.38172 | 2.96768 | 1.48698 | 2.29085 | 2.90284 |
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| ShuffleNetV2_x0_33 | 1.69008 | 2.65706 | 2.97373 | 1.75526 | 2.85557 | 3.09688 |
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| ShuffleNetV2_x0_5 | 1.48073 | 2.28174 | 2.85436 | 1.59055 | 2.18708 | 3.09141 |
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| ShuffleNetV2_x1_5 | 1.51054 | 2.4565 | 3.41738 | 1.45389 | 2.5203 | 3.99872 |
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| ShuffleNetV2_x2_0 | 1.95616 | 2.44751 | 4.19173 | 2.15654 | 3.18247 | 5.46893 |
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| ShuffleNetV2_swish | 2.50213 | 2.92881 | 3.474 | 2.5129 | 2.97422 | 3.69357 |
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