Mobile.md

# 移动端系列

## 概述
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。

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。

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。

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。

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。

![](../../images/models/mobile_arm_top1.png)

![](../../images/models/mobile_arm_storage.png)

![](../../images/models/T4_benchmark/t4.fp32.bs4.mobile_trt.flops.png)

![](../../images/models/T4_benchmark/t4.fp32.bs4.mobile_trt.params.png)


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。


## 精度、FLOPS和参数量

| Models                               | Top1    | Top5    | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| MobileNetV1_x0_25                    | 0.514   | 0.755   | 0.506             |                   | 0.070        | 0.460             |
| MobileNetV1_x0_5                     | 0.635   | 0.847   | 0.637             |                   | 0.280        | 1.310             |
| MobileNetV1_x0_75                    | 0.688   | 0.882   | 0.684             |                   | 0.630        | 2.550             |
| MobileNetV1                          | 0.710   | 0.897   | 0.706             |                   | 1.110        | 4.190             |
| MobileNetV1_ssld                     | 0.779   | 0.939   |                   |                   | 1.110        | 4.190             |
| MobileNetV2_x0_25                    | 0.532   | 0.765   |                   |                   | 0.050        | 1.500             |
| MobileNetV2_x0_5                     | 0.650   | 0.857   | 0.654             | 0.864             | 0.170        | 1.930             |
| MobileNetV2_x0_75                    | 0.698   | 0.890   | 0.698             | 0.896             | 0.350        | 2.580             |
| MobileNetV2                          | 0.722   | 0.907   | 0.718             | 0.910             | 0.600        | 3.440             |
| MobileNetV2_x1_5                     | 0.741   | 0.917   |                   |                   | 1.320        | 6.760             |
| MobileNetV2_x2_0                     | 0.752   | 0.926   |                   |                   | 2.320        | 11.130            |
| MobileNetV2_ssld                     | 0.7674  | 0.9339  |                   |                   | 0.600        | 3.440             |
| MobileNetV3_large_<br>x1_25          | 0.764   | 0.930   | 0.766             |                   | 0.714        | 7.440             |
| MobileNetV3_large_<br>x1_0           | 0.753   | 0.923   | 0.752             |                   | 0.450        | 5.470             |
| MobileNetV3_large_<br>x0_75          | 0.731   | 0.911   | 0.733             |                   | 0.296        | 3.910             |
| MobileNetV3_large_<br>x0_5           | 0.692   | 0.885   | 0.688             |                   | 0.138        | 2.670             |
| MobileNetV3_large_<br>x0_35          | 0.643   | 0.855   | 0.642             |                   | 0.077        | 2.100             |
| MobileNetV3_small_<br>x1_25          | 0.707   | 0.895   | 0.704             |                   | 0.195        | 3.620             |
| MobileNetV3_small_<br>x1_0           | 0.682   | 0.881   | 0.675             |                   | 0.123        | 2.940             |
| MobileNetV3_small_<br>x0_75          | 0.660   | 0.863   | 0.654             |                   | 0.088        | 2.370             |
| MobileNetV3_small_<br>x0_5           | 0.592   | 0.815   | 0.580             |                   | 0.043        | 1.900             |
| MobileNetV3_small_<br>x0_35          | 0.530   | 0.764   | 0.498             |                   | 0.026        | 1.660             |
| MobileNetV3_small_<br>x0_35_ssld          | 0.556   | 0.777   | 0.498             |                   | 0.026        | 1.660             |
| MobileNetV3_large_<br>x1_0_ssld      | 0.790   | 0.945   |                   |                   | 0.450        | 5.470             |
| MobileNetV3_large_<br>x1_0_ssld_int8 | 0.761   |         |                   |                   |              |                   |
| MobileNetV3_small_<br>x1_0_ssld      | 0.713   | 0.901   |                   |                   | 0.123        | 2.940             |
| ShuffleNetV2                         | 0.688   | 0.885   | 0.694             |                   | 0.280        | 2.260             |
| ShuffleNetV2_x0_25                   | 0.499   | 0.738   |                   |                   | 0.030        | 0.600             |
| ShuffleNetV2_x0_33                   | 0.537   | 0.771   |                   |                   | 0.040        | 0.640             |
| ShuffleNetV2_x0_5                    | 0.603   | 0.823   | 0.603             |                   | 0.080        | 1.360             |
| ShuffleNetV2_x1_5                    | 0.716   | 0.902   | 0.726             |                   | 0.580        | 3.470             |
| ShuffleNetV2_x2_0                    | 0.732   | 0.912   | 0.749             |                   | 1.120        | 7.320             |
| ShuffleNetV2_swish                   | 0.700   | 0.892   |                   |                   | 0.290        | 2.260             |
| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.082        | 2.600             |
| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.294        | 5.200             |
| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.440        | 7.300             |


## 基于SD855的预测速度和存储大小

| Models                               | Batch Size=1(ms) | Storage Size(M) |
|:--:|:--:|:--:|
| MobileNetV1_x0_25                    | 3.220            | 1.900           |
| MobileNetV1_x0_5                     | 9.580            | 5.200           |
| MobileNetV1_x0_75                    | 19.436           | 10.000          |
| MobileNetV1                          | 32.523           | 16.000          |
| MobileNetV1_ssld                     | 32.523           | 16.000          |
| MobileNetV2_x0_25                    | 3.799            | 6.100           |
| MobileNetV2_x0_5                     | 8.702            | 7.800           |
| MobileNetV2_x0_75                    | 15.531           | 10.000          |
| MobileNetV2                          | 23.318           | 14.000          |
| MobileNetV2_x1_5                     | 45.624           | 26.000          |
| MobileNetV2_x2_0                     | 74.292           | 43.000          |
| MobileNetV2_ssld                     | 23.318           | 14.000          |
| MobileNetV3_large_x1_25          | 28.218           | 29.000          |
| MobileNetV3_large_x1_0           | 19.308           | 21.000          |
| MobileNetV3_large_x0_75          | 13.565           | 16.000          |
| MobileNetV3_large_x0_5           | 7.493            | 11.000          |
| MobileNetV3_large_x0_35          | 5.137            | 8.600           |
| MobileNetV3_small_x1_25          | 9.275            | 14.000          |
| MobileNetV3_small_x1_0           | 6.546            | 12.000          |
| MobileNetV3_small_x0_75          | 5.284            | 9.600           |
| MobileNetV3_small_x0_5           | 3.352            | 7.800           |
| MobileNetV3_small_x0_35          | 2.635            | 6.900           |
| MobileNetV3_small_x0_35_ssld          | 2.635            | 6.900           |
| MobileNetV3_large_x1_0_ssld      | 19.308           | 21.000          |
| MobileNetV3_large_x1_0_ssld_int8 | 14.395           | 10.000          |
| MobileNetV3_small_x1_0_ssld      | 6.546            | 12.000          |
| ShuffleNetV2                         | 10.941           | 9.000           |
| ShuffleNetV2_x0_25                   | 2.329            | 2.700           |
| ShuffleNetV2_x0_33                   | 2.643            | 2.800           |
| ShuffleNetV2_x0_5                    | 4.261            | 5.600           |
| ShuffleNetV2_x1_5                    | 19.352           | 14.000          |
| ShuffleNetV2_x2_0                    | 34.770           | 28.000          |
| ShuffleNetV2_swish                   | 16.023           | 9.100           |
| GhostNet_x0_5                   | 5.714           | 10.000           |
| GhostNet_x1_0                   | 13.558           | 20.000           |
| GhostNet_x1_3                   | 19.982           | 29.000           |


## 基于T4 GPU的预测速度

| 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) |
|-----------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
| MobileNetV1_x0_25           | 0.68422               | 1.13021               | 1.72095               | 0.67274               | 1.226                 | 1.84096               |
| MobileNetV1_x0_5            | 0.69326               | 1.09027               | 1.84746               | 0.69947               | 1.43045               | 2.39353               |
| MobileNetV1_x0_75           | 0.6793                | 1.29524               | 2.15495               | 0.79844               | 1.86205               | 3.064                 |
| MobileNetV1                 | 0.71942               | 1.45018               | 2.47953               | 0.91164               | 2.26871               | 3.90797               |
| MobileNetV1_ssld            | 0.71942               | 1.45018               | 2.47953               | 0.91164               | 2.26871               | 3.90797               |
| MobileNetV2_x0_25           | 2.85399               | 3.62405               | 4.29952               | 2.81989               | 3.52695               | 4.2432                |
| MobileNetV2_x0_5            | 2.84258               | 3.1511                | 4.10267               | 2.80264               | 3.65284               | 4.31737               |
| MobileNetV2_x0_75           | 2.82183               | 3.27622               | 4.98161               | 2.86538               | 3.55198               | 5.10678               |
| MobileNetV2                 | 2.78603               | 3.71982               | 6.27879               | 2.62398               | 3.54429               | 6.41178               |
| MobileNetV2_x1_5            | 2.81852               | 4.87434               | 8.97934               | 2.79398               | 5.30149               | 9.30899               |
| MobileNetV2_x2_0            | 3.65197               | 6.32329               | 11.644                | 3.29788               | 7.08644               | 12.45375              |
| MobileNetV2_ssld            | 2.78603               | 3.71982               | 6.27879               | 2.62398               | 3.54429               | 6.41178               |
| MobileNetV3_large_x1_25     | 2.34387               | 3.16103               | 4.79742               | 2.35117               | 3.44903               | 5.45658               |
| MobileNetV3_large_x1_0      | 2.20149               | 3.08423               | 4.07779               | 2.04296               | 2.9322                | 4.53184               |
| MobileNetV3_large_x0_75     | 2.1058                | 2.61426               | 3.61021               | 2.0006                | 2.56987               | 3.78005               |
| MobileNetV3_large_x0_5      | 2.06934               | 2.77341               | 3.35313               | 2.11199               | 2.88172               | 3.19029               |
| MobileNetV3_large_x0_35     | 2.14965               | 2.7868                | 3.36145               | 1.9041                | 2.62951               | 3.26036               |
| MobileNetV3_small_x1_25     | 2.06817               | 2.90193               | 3.5245                | 2.02916               | 2.91866               | 3.34528               |
| MobileNetV3_small_x1_0      | 1.73933               | 2.59478               | 3.40276               | 1.74527               | 2.63565               | 3.28124               |
| MobileNetV3_small_x0_75     | 1.80617               | 2.64646               | 3.24513               | 1.93697               | 2.64285               | 3.32797               |
| MobileNetV3_small_x0_5      | 1.95001               | 2.74014               | 3.39485               | 1.88406               | 2.99601               | 3.3908                |
| MobileNetV3_small_x0_35     | 2.10683               | 2.94267               | 3.44254               | 1.94427               | 2.94116               | 3.41082               |
| MobileNetV3_small_x0_35_ssld     | 2.10683               | 2.94267               | 3.44254               | 1.94427               | 2.94116               | 3.41082               |
| MobileNetV3_large_x1_0_ssld | 2.20149               | 3.08423               | 4.07779               | 2.04296               | 2.9322                | 4.53184               |
| MobileNetV3_small_x1_0_ssld | 1.73933               | 2.59478               | 3.40276               | 1.74527               | 2.63565               | 3.28124               |
| ShuffleNetV2                | 1.95064               | 2.15928               | 2.97169               | 1.89436               | 2.26339               | 3.17615               |
| ShuffleNetV2_x0_25          | 1.43242               | 2.38172               | 2.96768               | 1.48698               | 2.29085               | 2.90284               |
| ShuffleNetV2_x0_33          | 1.69008               | 2.65706               | 2.97373               | 1.75526               | 2.85557               | 3.09688               |
| ShuffleNetV2_x0_5           | 1.48073               | 2.28174               | 2.85436               | 1.59055               | 2.18708               | 3.09141               |
| ShuffleNetV2_x1_5           | 1.51054               | 2.4565                | 3.41738               | 1.45389               | 2.5203                | 3.99872               |
| ShuffleNetV2_x2_0           | 1.95616               | 2.44751               | 4.19173               | 2.15654               | 3.18247               | 5.46893               |
| ShuffleNetV2_swish          | 2.50213               | 2.92881               | 3.474                 | 2.5129                | 2.97422               | 3.69357               |