Add GhostNet_x1_3_ssld_pretrained in dygraph branch. (#419)

add GhostNet_x1_3_ssld_pretrained

README.md

@@ -7,6 +7,7 @@
 PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
 
 **Recent update**
+- 2020.11.23 Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.38%.
 - 2020.11.09 Add `InceptionV3` architecture and pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.1%.
 - 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%.
 - 2020.10.12 Add Paddle-Lite demo。
@@ -184,6 +185,7 @@ Accuracy and inference time metrics of Mobile series models are shown as follows
 | GhostNet_<br>x0_5                    | 0.6688    | 0.8695    | 5.7143                 | 0.082    | 2.6       | 10      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)               |
 | GhostNet_<br>x1_0                    | 0.7402    | 0.9165    | 13.5587                | 0.294    | 5.2       | 20      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)               |
 | GhostNet_<br>x1_3                    | 0.7579    | 0.9254    | 19.9825                | 0.44     | 7.3       | 29      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)               |
+| GhostNet_<br>x1_3_ssld                    | 0.7938    | 0.9449    | 19.9825                | 0.44     | 7.3       | 29      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_ssld_pretrained.tar)               |
 
 
 <a name="SEResNeXt_and_Res2Net_series"></a>

README_cn.md

@@ -8,6 +8,7 @@
 
 
 **近期更新**
+- 2020.11.23 添加`GhostNet_x1_3_ssld `模型，在ImageNet-1k上Top-1 Acc可达79.38%。
 - 2020.11.18 添加图像分类[常见问题2020第一季第三期](./docs/zh_CN/faq_series/faq_2020_s1.md) 5个新问题，并且计划以后每周会更新，欢迎大家持续关注。
 - 2020.11.09 添加`InceptionV3 `结构和模型，在ImageNet-1k上Top-1 Acc可达79.14%。
 - 2020.10.20 添加 `Res2Net50_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.1%；添加 `Res2Net101_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.9%。
@@ -186,6 +187,7 @@ ResNet及其Vd系列模型的精度、速度指标如下表所示，更多关于
 | GhostNet_<br>x0_5                    | 0.6688    | 0.8695    | 5.7143                 | 0.082    | 2.6       | 10      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)               |
 | GhostNet_<br>x1_0                    | 0.7402    | 0.9165    | 13.5587                | 0.294    | 5.2       | 20      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)               |
 | GhostNet_<br>x1_3                    | 0.7579    | 0.9254    | 19.9825                | 0.44     | 7.3       | 29      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)               |
+| GhostNet_<br>x1_3_ssld                    | 0.7938    | 0.9449    | 19.9825                | 0.44     | 7.3       | 29      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_ssld_pretrained.tar)               |
 
 
 <a name="SEResNeXt与Res2Net系列"></a>

docs/en/models/Mobile_en.md

@@ -62,6 +62,7 @@ Currently there are 32 pretrained models of the mobile series open source by Pad
 | GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.082        | 2.600             |
 | GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.294        | 5.200             |
 | GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.440        | 7.300             |
+| GhostNet_x1_3_ssld                        | 0.794   | 0.945   | 0.757             | 0.927             | 0.440        | 7.300             |
 
 
 ## Inference speed and storage size based on SD855
@@ -104,6 +105,7 @@ Currently there are 32 pretrained models of the mobile series open source by Pad
 | GhostNet_x0_5                   | 5.714           | 10.000           |
 | GhostNet_x1_0                   | 13.558           | 20.000           |
 | GhostNet_x1_3                   | 19.982           | 29.000           |
+| GhostNet_x1_3_ssld                   | 19.982           | 29.000           |
 
 
 ## Inference speed based on T4 GPU

docs/en/models/models_intro_en.md

@@ -98,6 +98,7 @@ python tools/infer/predict.py \
     - [GhostNet_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)
     - [GhostNet_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)
     - [GhostNet_x1_3](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)
+    - [GhostNet_x1_3_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_ssld_pretrained.tar)
 
 
 - SEResNeXt and Res2Net series

docs/en/update_history_en.md

 # Release Notes
 
+- 2020.11.23
+    * Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.38%.
+    
 - 2020.11.09
     * Add `InceptionV3` architecture and pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.1%.
     

docs/zh_CN/models/Mobile.md

@@ -63,6 +63,7 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通
 | GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.082        | 2.600             |
 | GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.294        | 5.200             |
 | GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.440        | 7.300             |
+| GhostNet_x1_3_ssld                        | 0.794   | 0.945   | 0.757             | 0.927             | 0.440        | 7.300             |
 
 
 ## 基于SD855的预测速度和存储大小
@@ -105,6 +106,7 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通
 | GhostNet_x0_5                   | 5.714           | 10.000           |
 | GhostNet_x1_0                   | 13.558           | 20.000           |
 | GhostNet_x1_3                   | 19.982           | 29.000           |
+| GhostNet_x1_3_ssld                   | 19.982           | 29.000           |
 
 
 ## 基于T4 GPU的预测速度

docs/zh_CN/models/models_intro.md

@@ -98,6 +98,7 @@ python tools/infer/predict.py \
     - [GhostNet_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)
     - [GhostNet_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)
     - [GhostNet_x1_3](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)
+    - [GhostNet_x1_3_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_ssld_pretrained.tar)
 
 
 - SEResNeXt与Res2Net系列

docs/zh_CN/update_history.md

 # 更新日志
 
+- 2020.11.23
+    * 添加GhostNet_x1_3_ssld模型，在ImageNet上Top-1 Acc可达0.7938。
+
 - 2020.11.09
     * 添加InceptionV3结构和模型，在ImageNet上Top-1 Acc可达0.791。