# DPN与DenseNet系列
## 概述
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该系列模型的FLOPS、参数量以及FP32预测耗时如下图所示。
所有模型在预测时，图像的crop_size设置为224，resize_short_size设置为256。
## 精度、FLOPS和参数量
| Models | Top1 | Top5 | Reference
top1 | Reference
top5 | FLOPS
(G) | Parameters
(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| DenseNet121 | 0.757 | 0.926 | 0.750 | | 5.690 | 7.980 |
| DenseNet161 | 0.786 | 0.941 | 0.778 | | 15.490 | 28.680 |
| DenseNet169 | 0.768 | 0.933 | 0.764 | | 6.740 | 14.150 |
| DenseNet201 | 0.776 | 0.937 | 0.775 | | 8.610 | 20.010 |
| DenseNet264 | 0.780 | 0.939 | 0.779 | | 11.540 | 33.370 |
| DPN68 | 0.768 | 0.934 | 0.764 | 0.931 | 4.030 | 10.780 |
| DPN92 | 0.799 | 0.948 | 0.793 | 0.946 | 12.540 | 36.290 |
| DPN98 | 0.806 | 0.951 | 0.799 | 0.949 | 22.220 | 58.460 |
| DPN107 | 0.809 | 0.953 | 0.802 | 0.951 | 35.060 | 82.970 |
| DPN131 | 0.807 | 0.951 | 0.801 | 0.949 | 30.510 | 75.360 |
## FP32预测速度
| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) |
|-------------|-----------|-------------------|--------------------------|
| DenseNet121 | 224 | 256 | 4.371 |
| DenseNet161 | 224 | 256 | 8.863 |
| DenseNet169 | 224 | 256 | 6.391 |
| DenseNet201 | 224 | 256 | 8.173 |
| DenseNet264 | 224 | 256 | 11.942 |
| DPN68 | 224 | 256 | 11.805 |
| DPN92 | 224 | 256 | 17.840 |
| DPN98 | 224 | 256 | 21.057 |
| DPN107 | 224 | 256 | 28.685 |
| DPN131 | 224 | 256 | 28.083 |