DPN_DenseNet.md

# DPN与DenseNet系列

## 概述

![](../../images/models/DPN.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<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(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            |


## FP16预测速度

| Models      | batch_size=1<br>(ms) | batch_size=4<br>(ms) | batch_size=8<br>(ms) | batch_size=32<br>(ms) |
|:--:|:--:|:--:|:--:|:--:|
| DenseNet121 | 3.653                | 4.560                | 5.574                | 11.517                |
| DenseNet161 | 7.826                | 8.936                | 10.970               | 22.554                |
| DenseNet169 | 5.625                | 6.698                | 7.876                | 14.983                |
| DenseNet201 | 7.243                | 8.537                | 10.111               | 18.928                |
| DenseNet264 | 10.882               | 12.539               | 14.645               | 26.455                |
| DPN68       | 10.310               | 11.060               | 14.299               | 29.618                |
| DPN92       | 16.335               | 17.373               | 23.197               | 45.210                |
| DPN98       | 18.975               | 23.073               | 28.902               | 66.280                |
| DPN107      | 24.932               | 28.607               | 37.513               | 89.112                |
| DPN131      | 25.425               | 29.874               | 37.355               | 88.583                |


## FP32预测速度

| Models      | batch_size=1<br>(ms) | batch_size=4<br>(ms) | batch_size=8<br>(ms) | batch_size=32<br>(ms) |
|:--:|:--:|:--:|:--:|:--:|
| DenseNet121 | 3.732                | 6.614                | 8.517                | 21.755                |
| DenseNet161 | 8.282                | 14.438               | 19.336               | 51.953                |
| DenseNet169 | 5.705                | 10.074               | 12.432               | 28.756                |
| DenseNet201 | 7.315                | 13.830               | 16.941               | 38.654                |
| DenseNet264 | 10.986               | 21.460               | 25.724               | 56.501                |
| DPN68       | 10.357               | 11.025               | 14.903               | 34.380                |
| DPN92       | 16.067               | 21.315               | 26.176               | 62.126                |
| DPN98       | 18.455               | 26.710               | 36.009               | 104.084               |
| DPN107      | 24.164               | 37.691               | 51.307               | 148.041               |
| DPN131      | 24.761               | 35.806               | 48.401               | 133.233               |