# 其他模型
## 概述
DarkNet53在预测时，图像的crop_size设置为256，resize_short_size设置为256；其余模型在预测时，图像的crop_size设置为224，resize_short_size设置为256。
更多的模型简介正在持续更新中。
## 精度、FLOPS和参数量
| Models | Top1 | Top5 | Reference
top1 | Reference
top5 | FLOPS
(G) | Parameters
(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| AlexNet | 0.567 | 0.792 | 0.5720 | | 1.370 | 61.090 |
| SqueezeNet1_0 | 0.596 | 0.817 | 0.575 | | 1.550 | 1.240 |
| SqueezeNet1_1 | 0.601 | 0.819 | | | 0.690 | 1.230 |
| VGG11 | 0.693 | 0.891 | | | 15.090 | 132.850 |
| VGG13 | 0.700 | 0.894 | | | 22.480 | 133.030 |
| VGG16 | 0.720 | 0.907 | 0.715 | 0.901 | 30.810 | 138.340 |
| VGG19 | 0.726 | 0.909 | | | 39.130 | 143.650 |
| DarkNet53 | 0.780 | 0.941 | 0.772 | 0.938 | 18.580 | 41.600 |
| ResNet50_ACNet | 0.767 | 0.932 | | | 10.730 | 33.110 |
| ResNet50_ACNet
_deploy | 0.767 | 0.932 | | | 8.190 | 25.550 |
## FP16预测速度
| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) |
|:--:|:--:|:--:|:--:|:--:|
| AlexNet | 0.684 | 0.740 | 0.810 | 1.481 |
| SqueezeNet1_0 | 0.545 | 0.841 | 1.146 | 3.501 |
| SqueezeNet1_1 | 0.473 | 0.575 | 0.805 | 1.862 |
| VGG11 | 1.096 | 1.655 | 2.396 | 6.728 |
| VGG13 | 1.216 | 2.059 | 3.056 | 9.468 |
| VGG16 | 1.518 | 2.594 | 4.019 | 12.145 |
| VGG19 | 1.817 | 3.124 | 4.886 | 14.958 |
| DarkNet53 | 2.150 | 2.627 | 3.422 | 10.092 | |
| ResNet50_ACNet
_deploy | 2.748 | 3.178 | 3.823 | 8.369 |
## FP32预测速度
| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) |
|:--:|:--:|:--:|:--:|:--:|
| AlexNet | 0.682 | 0.875 | 1.196 | 3.196 |
| SqueezeNet1_0 | 0.530 | 1.072 | 1.652 | 5.338 |
| SqueezeNet1_1 | 0.439 | 0.787 | 1.164 | 2.973 |
| VGG11 | 1.575 | 3.638 | 6.427 | 23.227 |
| VGG13 | 1.859 | 4.832 | 8.832 | 32.946 |
| VGG16 | 2.316 | 6.420 | 11.936 | 44.719 |
| VGG19 | 2.775 | 8.013 | 14.925 | 57.272 |
| DarkNet53 | 2.648 | 5.727 | 9.616 | 33.664 | |
| ResNet50_ACNet
_deploy | 4.544 | 6.873 | 9.627 | 28.283 |