# 移动端系列
## 概述
所有模型在预测时，图像的crop_size设置为224，resize_short_size设置为256。
更多的模型概述正在持续更新中。
## 精度、FLOPS和参数量
| Models | Top1 | Top5 | Reference
top1 | Reference
top5 | FLOPS
(G) | Parameters
(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_
x1_25 | 0.764 | 0.930 | 0.766 | | 0.714 | 7.440 |
| MobileNetV3_large_
x1_0 | 0.753 | 0.753 | 0.752 | | 0.450 | 5.470 |
| MobileNetV3_large_
x0_75 | 0.731 | 0.911 | 0.733 | | 0.296 | 3.910 |
| MobileNetV3_large_
x0_5 | 0.692 | 0.885 | 0.688 | | 0.138 | 2.670 |
| MobileNetV3_large_
x0_35 | 0.643 | 0.855 | 0.642 | | 0.077 | 2.100 |
| MobileNetV3_small_
x1_25 | 0.707 | 0.895 | 0.704 | | 0.195 | 3.620 |
| MobileNetV3_small_
x1_0 | 0.682 | 0.881 | 0.675 | | 0.123 | 2.940 |
| MobileNetV3_small_
x0_75 | 0.660 | 0.863 | 0.654 | | 0.088 | 2.370 |
| MobileNetV3_small_
x0_5 | 0.592 | 0.815 | 0.580 | | 0.043 | 1.900 |
| MobileNetV3_small_
x0_35 | 0.530 | 0.764 | 0.498 | | 0.026 | 1.660 |
| MobileNetV3_large_
x1_0_ssld | 0.790 | 0.945 | | | 0.450 | 5.470 |
| MobileNetV3_large_
x1_0_ssld_int8 | 0.761 | | | | | |
| MobileNetV3_small_
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 |
## FP16预测速度
| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) |
|:--:|:--:|:--:|:--:|:--:|
| MobileNetV1_x0_25 | 0.236 | 0.258 | 0.281 | 0.556 |
| MobileNetV1_x0_5 | 0.246 | 0.318 | 0.364 | 0.845 |
| MobileNetV1_x0_75 | 0.303 | 0.380 | 0.512 | 1.164 |
| MobileNetV1 | 0.340 | 0.426 | 0.601 | 1.578 |
| MobileNetV1_ssld | 0.340 | 0.426 | 0.601 | 1.578 |
| MobileNetV2_x0_25 | 0.432 | 0.488 | 0.532 | 0.967 |
| MobileNetV2_x0_5 | 0.475 | 0.564 | 0.654 | 1.296 |
| MobileNetV2_x0_75 | 0.553 | 0.653 | 0.821 | 1.761 |
| MobileNetV2 | 0.610 | 0.738 | 0.931 | 2.115 |
| MobileNetV2_x1_5 | 0.731 | 0.966 | 1.252 | 3.152 |
| MobileNetV2_x2_0 | 0.870 | 1.123 | 1.494 | 3.910 |
| MobileNetV2_ssld | 0.610 | 0.738 | 0.931 | 2.115 |
| MobileNetV3_large_
x1_25 | 2.004 | 2.223 | 2.433 | 5.954 |
| MobileNetV3_large_
x1_0 | 1.943 | 2.203 | 2.113 | 4.823 |
| MobileNetV3_large_
x0_75 | 2.107 | 2.266 | 2.120 | 3.968 |
| MobileNetV3_large_
x0_5 | 1.942 | 2.178 | 2.179 | 2.936 |
| MobileNetV3_large_
x0_35 | 1.994 | 2.407 | 2.285 | 2.420 |
| MobileNetV3_small_
x1_25 | 1.876 | 2.141 | 2.118 | 3.423 |
| MobileNetV3_small_
x1_0 | 1.751 | 2.160 | 2.203 | 2.830 |
| MobileNetV3_small_
x0_75 | 1.856 | 2.235 | 2.166 | 2.464 |
| MobileNetV3_small_
x0_5 | 1.773 | 2.304 | 2.242 | 2.133 |
| MobileNetV3_small_
x0_35 | 1.870 | 2.392 | 2.323 | 2.101 |
| MobileNetV3_large_
x1_0_ssld | 1.943 | 2.203 | 2.113 | 4.823 | |
| MobileNetV3_small_
x1_0_ssld | 1.751 | 2.160 | 2.203 | 2.830 |
| ShuffleNetV2 | 1.134 | 1.068 | 1.199 | 2.558 |
| ShuffleNetV2_x0_25 | 0.911 | 0.953 | 0.948 | 1.327 |
| ShuffleNetV2_x0_33 | 0.853 | 1.072 | 0.958 | 1.398 |
| ShuffleNetV2_x0_5 | 0.858 | 1.059 | 1.084 | 1.620 |
| ShuffleNetV2_x1_5 | 1.040 | 1.153 | 1.394 | 3.452 |
| ShuffleNetV2_x2_0 | 1.061 | 1.316 | 1.694 | 4.485 |
| ShuffleNetV2_swish | 1.688 | 1.958 | 1.707 | 3.711 |
## FP32预测速度
| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) |
|:--:|:--:|:--:|:--:|:--:|
| MobileNetV1_x0_25 | 0.233 | 0.372 | 0.424 | 0.930 |
| MobileNetV1_x0_5 | 0.281 | 0.532 | 0.677 | 1.808 |
| MobileNetV1_x0_75 | 0.344 | 0.733 | 0.960 | 2.920 |
| MobileNetV1 | 0.420 | 0.963 | 1.462 | 4.769 |
| MobileNetV1_ssld | 0.420 | 0.963 | 1.462 | 4.769 |
| MobileNetV2_x0_25 | 0.718 | 0.738 | 0.775 | 1.482 |
| MobileNetV2_x0_5 | 0.818 | 0.975 | 1.107 | 2.481 |
| MobileNetV2_x0_75 | 0.830 | 1.104 | 1.514 | 3.629 |
| MobileNetV2 | 0.889 | 1.346 | 1.875 | 4.711 |
| MobileNetV2_x1_5 | 1.221 | 1.982 | 2.951 | 7.645 |
| MobileNetV2_x2_0 | 1.546 | 2.625 | 3.734 | 10.429 |
| MobileNetV2_ssld | 0.889 | 1.346 | 1.875 | 4.711 |
| MobileNetV3_large_
x1_25 | 2.113 | 2.377 | 3.114 | 7.332 |
| MobileNetV3_large_
x1_0 | 1.991 | 2.380 | 2.517 | 5.826 |
| MobileNetV3_large_
x0_75 | 2.105 | 2.454 | 2.336 | 4.611 |
| MobileNetV3_large_
x0_5 | 1.978 | 2.603 | 2.291 | 3.306 |
| MobileNetV3_large_
x0_35 | 2.017 | 2.469 | 2.316 | 2.558 |
| MobileNetV3_small_
x1_25 | 1.915 | 2.411 | 2.295 | 3.742 |
| MobileNetV3_small_
x1_0 | 1.915 | 2.889 | 2.862 | 3.022 |
| MobileNetV3_small_
x0_75 | 1.941 | 2.358 | 2.232 | 2.602 |
| MobileNetV3_small_
x0_5 | 1.872 | 2.364 | 2.238 | 2.061 |
| MobileNetV3_small_
x0_35 | 1.889 | 2.407 | 2.328 | 2.127 |
| MobileNetV3_large_
x1_0_ssld | 1.991 | 2.380 | 2.517 | 5.826 |
| MobileNetV3_small_
x1_0_ssld | 1.915 | 2.889 | 2.862 | 3.022 |
| ShuffleNetV2 | 1.328 | 1.211 | 1.440 | 3.210 |
| ShuffleNetV2_x0_25 | 0.905 | 0.908 | 0.924 | 1.284 |
| ShuffleNetV2_x0_33 | 0.871 | 1.073 | 0.891 | 1.416 |
| ShuffleNetV2_x0_5 | 0.852 | 1.150 | 1.093 | 1.702 |
| ShuffleNetV2_x1_5 | 0.874 | 1.470 | 1.889 | 4.490 |
| ShuffleNetV2_x2_0 | 1.443 | 1.908 | 2.556 | 6.864 |
| ShuffleNetV2_swish | 1.694 | 1.856 | 2.101 | 3.942 |
## CPU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_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 |