# ResNet及其Vd系列 ## 概述 ![](../../images/models/ResNet.png) 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) | |:--:|:--:|:--:|:--:|:--:|:--:|:--:| | ResNet18 | 0.710 | 0.899 | 0.696 | 0.891 | 3.660 | 11.690 | | ResNet18_vd | 0.723 | 0.908 | | | 4.140 | 11.710 | | ResNet34 | 0.746 | 0.921 | 0.732 | 0.913 | 7.360 | 21.800 | | ResNet34_vd | 0.760 | 0.930 | | | 7.390 | 21.820 | | ResNet50 | 0.765 | 0.930 | 0.760 | 0.930 | 8.190 | 25.560 | | ResNet50_vc | 0.784 | 0.940 | | | 8.670 | 25.580 | | ResNet50_vd | 0.791 | 0.944 | 0.792 | 0.946 | 8.670 | 25.580 | | ResNet50_vd_v2 | 0.798 | 0.949 | | | 8.670 | 25.580 | | ResNet101 | 0.776 | 0.936 | 0.776 | 0.938 | 15.520 | 44.550 | | ResNet101_vd | 0.802 | 0.950 | | | 16.100 | 44.570 | | ResNet152 | 0.783 | 0.940 | 0.778 | 0.938 | 23.050 | 60.190 | | ResNet152_vd | 0.806 | 0.953 | | | 23.530 | 60.210 | | ResNet200_vd | 0.809 | 0.953 | | | 30.530 | 74.740 | | ResNet50_vd_ssld | 0.824 | 0.961 | | | 8.670 | 25.580 | ## FP16预测速度 | Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | |:--:|:--:|:--:|:--:|:--:| | ResNet18 | 0.966 | 1.076 | 1.263 | 2.656 | | ResNet18_vd | 1.002 | 1.163 | 1.392 | 3.045 | | ResNet34 | 1.798 | 1.959 | 2.269 | 4.716 | | ResNet34_vd | 1.839 | 2.011 | 2.482 | 4.767 | | ResNet50 | 1.892 | 2.146 | 2.692 | 6.411 | | ResNet50_vc | 1.903 | 2.094 | 2.677 | 6.096 | | ResNet50_vd | 1.918 | 2.273 | 2.833 | 6.978 | | ResNet50_vd_v2 | 1.918 | 2.273 | 2.833 | 6.978 | | ResNet101 | 3.790 | 4.128 | 4.789 | 10.913 | | ResNet101_vd | 3.853 | 4.229 | 5.001 | 11.437 | | ResNet152 | 5.523 | 5.871 | 6.710 | 15.258 | | ResNet152_vd | 5.503 | 6.003 | 7.001 | 15.716 | | ResNet200_vd | 7.270 | 7.595 | 8.802 | 19.516 | | ResNet50_vd_ssld | 1.918 | 2.273 | 2.833 | 6.978 | ## FP32预测速度 | Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | |:--:|:--:|:--:|:--:|:--:| | ResNet18 | 1.127 | 1.428 | 2.352 | 7.780 | | ResNet18_vd | 1.142 | 1.532 | 2.584 | 8.441 | | ResNet34 | 1.936 | 2.409 | 4.197 | 14.442 | | ResNet34_vd | 1.948 | 2.526 | 4.403 | 15.133 | | ResNet50 | 2.630 | 4.393 | 6.491 | 20.449 | | ResNet50_vc | 2.728 | 4.413 | 6.618 | 21.183 | | ResNet50_vd | 2.649 | 4.522 | 6.771 | 21.552 | | ResNet50_vd_v2 | 2.649 | 4.522 | 6.771 | 21.552 | | ResNet101 | 4.747 | 8.015 | 11.555 | 36.739 | | ResNet101_vd | 4.735 | 8.111 | 11.820 | 37.155 | | ResNet152 | 6.618 | 11.471 | 16.580 | 51.792 | | ResNet152_vd | 6.626 | 11.613 | 16.843 | 53.645 | | ResNet200_vd | 8.540 | 14.770 | 21.554 | 69.053 | | ResNet50_vd_ssld | 2.649 | 4.522 | 6.771 | 21.552 |