# EfficientNet与ResNeXt101_wsl系列
## 概述
概述正在持续更新中......
在预测时，图像的crop_size和resize_short_size如下表所示。
| Models | crop_size | resize_short_size |
|:--:|:--:|:--:|
| ResNeXt101_32x8d_wsl | 224 | 224 |
| ResNeXt101_32x16d_wsl | 224 | 224 |
| ResNeXt101_32x32d_wsl | 224 | 224 |
| ResNeXt101_32x48d_wsl | 224 | 224 |
| Fix_ResNeXt101_32x48d_wsl | 320 | 320 |
| EfficientNetB0 | 224 | 256 |
| EfficientNetB1 | 240 | 272 |
| EfficientNetB2 | 260 | 292 |
| EfficientNetB3 | 300 | 332 |
| EfficientNetB4 | 380 | 412 |
| EfficientNetB5 | 456 | 488 |
| EfficientNetB6 | 528 | 560 |
| EfficientNetB7 | 600 | 632 |
| EfficientNetB0_small | 224 | 256 |
## 精度、FLOPS和参数量
| Models | Top1 | Top5 | Reference
top1 | Reference
top5 | FLOPS
(G) | Parameters
(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| ResNeXt101_
32x8d_wsl | 0.826 | 0.967 | 0.822 | 0.964 | 29.140 | 78.440 |
| ResNeXt101_
32x16d_wsl | 0.842 | 0.973 | 0.842 | 0.972 | 57.550 | 152.660 |
| ResNeXt101_
32x32d_wsl | 0.850 | 0.976 | 0.851 | 0.975 | 115.170 | 303.110 |
| ResNeXt101_
32x48d_wsl | 0.854 | 0.977 | 0.854 | 0.976 | 173.580 | 456.200 |
| Fix_ResNeXt101_
32x48d_wsl | 0.863 | 0.980 | 0.864 | 0.980 | 354.230 | 456.200 |
| EfficientNetB0 | 0.774 | 0.933 | 0.773 | 0.935 | 0.720 | 5.100 |
| EfficientNetB1 | 0.792 | 0.944 | 0.792 | 0.945 | 1.270 | 7.520 |
| EfficientNetB2 | 0.799 | 0.947 | 0.803 | 0.950 | 1.850 | 8.810 |
| EfficientNetB3 | 0.812 | 0.954 | 0.817 | 0.956 | 3.430 | 11.840 |
| EfficientNetB4 | 0.829 | 0.962 | 0.830 | 0.963 | 8.290 | 18.760 |
| EfficientNetB5 | 0.836 | 0.967 | 0.837 | 0.967 | 19.510 | 29.610 |
| EfficientNetB6 | 0.840 | 0.969 | 0.842 | 0.968 | 36.270 | 42.000 |
| EfficientNetB7 | 0.843 | 0.969 | 0.844 | 0.971 | 72.350 | 64.920 |
| EfficientNetB0_
small | 0.758 | 0.926 | | | 0.720 | 4.650 |
## FP16预测速度
| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) |
|:--:|:--:|:--:|:--:|:--:|
| ResNeXt101_
32x8d_wsl | 16.063 | 16.342 | 24.914 | 45.035 |
| ResNeXt101_
32x16d_wsl | 16.471 | 25.235 | 30.762 | 67.869 |
| ResNeXt101_
32x32d_wsl | 29.425 | 37.149 | 50.834 | |
| ResNeXt101_
32x48d_wsl | 40.311 | 58.414 | | |
| Fix_ResNeXt101_
32x48d_wsl | 43.960 | 86.514 | | |
| EfficientNetB0 | 1.759 | 2.748 | 3.761 | 10.178 |
| EfficientNetB1 | 2.592 | 4.122 | 5.829 | 16.262 |
| EfficientNetB2 | 2.866 | 4.715 | 7.064 | 20.954 |
| EfficientNetB3 | 3.869 | 6.815 | 10.672 | 34.097 |
| EfficientNetB4 | 5.626 | 11.937 | 19.753 | 67.436 |
| EfficientNetB5 | 8.907 | 21.685 | 37.248 | 134.185 |
| EfficientNetB6 | 13.591 | 34.093 | 60.976 | |
| EfficientNetB7 | 20.963 | 56.397 | 103.971 | |
| EfficientNetB0_
small | 1.039 | 1.665 | 2.493 | 7.748 |
## FP32预测速度
| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) |
|:--:|:--:|:--:|:--:|:--:|
| ResNeXt101_
32x8d_wsl | 16.325 | 25.633 | 37.196 | 108.535 |
| ResNeXt101_
32x16d_wsl | 25.224 | 40.929 | 62.898 | |
| ResNeXt101_
32x32d_wsl | 41.047 | 79.575 | | |
| ResNeXt101_
32x48d_wsl | 60.610 | | | |
| Fix_ResNeXt101_
32x48d_wsl | 80.280 | | | |
| EfficientNetB0 | 1.902 | 3.296 | 4.361 | 11.319 |
| EfficientNetB1 | 2.908 | 5.093 | 6.900 | 18.015 |
| EfficientNetB2 | 3.324 | 5.832 | 8.357 | 23.371 |
| EfficientNetB3 | 4.557 | 8.526 | 12.485 | 38.124 |
| EfficientNetB4 | 6.767 | 14.742 | 23.218 | 77.590 |
| EfficientNetB5 | 11.097 | 26.642 | 43.590 | |
| EfficientNetB6 | 17.582 | 42.408 | 74.336 | |
| EfficientNetB7 | 26.529 | 70.337 | 126.839 | |
| EfficientNetB0_
small | 1.171 | 2.026 | 2.906 | 8.506 |