Add brief information about other models (#407)

e7ade6cd · Tingquan Gao · GitHub · bbf99cf2 · e7ade6cd · e7ade6cd
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README.md README.md +21 -0

README_cn.md README_cn.md +23 -1

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--- a/README.md
+++ b/README.md
@@ -61,6 +61,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
    - [Inception series](#Inception_series)
    - [EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series)
    - [ResNeSt and RegNet series](#ResNeSt_and_RegNet_series)
+    - [Others](#Others)
    - HS-ResNet: arxiv link: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf). Code and models are coming soon!
 - Model training/evaluation
    - [Data preparation](./docs/en/tutorials/data_en.md)
@@ -311,6 +312,26 @@ Accuracy and inference time metrics of ResNeSt and RegNet series models are show
 | RegNetX_4GF            | 0.785     | 0.9416    |    6.46478              |      11.19862           | 8        | 22.1      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams)            |


+<a name="Others"></a>
+### Others
+
+Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series, DarkNet53, ResNet50_ACNet and ResNet50_ACNet_deploy models are shown as follows. More detailed information can be refered to [Others](./docs/en/models/Others_en.md).
+
+
+| Model                    | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address                                                                                                 |
+|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
+| AlexNet                   | 0.567  | 0.792  | 1.44993           | 2.46696           | 1.370        | 61.090            | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar) |
+| SqueezeNet1_0             | 0.596  | 0.817  | 0.96736           | 2.53221           | 1.550        | 1.240             | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_0_pretrained.tar) |
+| SqueezeNet1_1             | 0.601  | 0.819  | 0.76032           | 1.877             | 0.690        | 1.230             | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_1_pretrained.tar) |
+| VGG11                     | 0.693  | 0.891  | 3.90412           | 9.51147           | 15.090       | 132.850           | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.tar) |
+| VGG13                     | 0.700  | 0.894  | 4.64684           | 12.61558          | 22.480       | 133.030           | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.tar) |
+| VGG16                     | 0.720  | 0.907  | 5.61769           | 16.40064          | 30.810       | 138.340           | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar) |
+| VGG19                     | 0.726  | 0.909  | 6.65221           | 20.4334           | 39.130       | 143.650           | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar) |
+| DarkNet53                 | 0.780  | 0.941  | 4.10829           | 12.1714           | 18.580       | 41.600            | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar) |
+| ResNet50_ACNet            | 0.767  | 0.932  | 5.33395           | 10.96843          | 10.730       | 33.110            | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_pretrained.tar) |
+| ResNet50_ACNet<br>_deploy | 0.767  | 0.932  | 3.49161           | 7.78374           | 8.190        | 25.550            | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_deploy_pretrained.tar) |
+
+
 <a name="License"></a>
 ## License


--- a/README_cn.md
+++ b/README_cn.md
@@ -63,10 +63,11 @@
    - [移动端系列](#移动端系列)
    - [SEResNeXt与Res2Net系列](#SEResNeXt与Res2Net系列)
    - [DPN与DenseNet系列](#DPN与DenseNet系列)
-    - [HRNet](HRNet系列)
+    - [HRNet](#HRNet系列)
    - [Inception系列](#Inception系列)
    - [EfficientNet与ResNeXt101_wsl系列](#EfficientNet与ResNeXt101_wsl系列)
    - [ResNeSt与RegNet系列](#ResNeSt与RegNet系列)
+    - [其他模型](#其他模型)
    - HS-ResNet: arxiv文章链接: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf)。 代码和预训练模型即将开源，敬请期待。
 - 模型训练/评估
    - [数据准备](./docs/zh_CN/tutorials/data.md)
@@ -318,6 +319,27 @@ ResNeSt与RegNet系列模型的精度、速度指标如下表所示，更多关
 | RegNetX_4GF            | 0.785     | 0.9416    |    6.46478              |      11.19862           | 8        | 22.1      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams)            |


+<a name="其他模型"></a>
+### 其他模型
+
+AlexNet、SqueezeNet系列、VGG系列、DarkNet53、ResNet50_ACNet与ResNet50_ACNet
+_deploy模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](./docs/zh_CN/models/Others.md)。
+
+
+| 模型                     | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | 下载地址                                                                                                 |
+|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
+| AlexNet                   | 0.567  | 0.792  | 1.44993           | 2.46696           | 1.370        | 61.090            | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar) |
+| SqueezeNet1_0             | 0.596  | 0.817  | 0.96736           | 2.53221           | 1.550        | 1.240             | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_0_pretrained.tar) |
+| SqueezeNet1_1             | 0.601  | 0.819  | 0.76032           | 1.877             | 0.690        | 1.230             | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_1_pretrained.tar) |
+| VGG11                     | 0.693  | 0.891  | 3.90412           | 9.51147           | 15.090       | 132.850           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.tar) |
+| VGG13                     | 0.700  | 0.894  | 4.64684           | 12.61558          | 22.480       | 133.030           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.tar) |
+| VGG16                     | 0.720  | 0.907  | 5.61769           | 16.40064          | 30.810       | 138.340           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar) |
+| VGG19                     | 0.726  | 0.909  | 6.65221           | 20.4334           | 39.130       | 143.650           | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar) |
+| DarkNet53                 | 0.780  | 0.941  | 4.10829           | 12.1714           | 18.580       | 41.600            | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar) |
+| ResNet50_ACNet            | 0.767  | 0.932  | 5.33395           | 10.96843          | 10.730       | 33.110            | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_pretrained.tar) |
+| ResNet50_ACNet<br>_deploy | 0.767  | 0.932  | 3.49161           | 7.78374           | 8.190        | 25.550            | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_ACNet_deploy_pretrained.tar) |
+
+
 <a name="许可证书"></a>
 ## 许可证书
 本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleCLS/blob/master/LICENSE">Apache 2.0 license</a>许可认证。