add ssld pretrained models (#506)

* add pretrained models

* fix typo

README.md

@@ -57,6 +57,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
 - [Quick start PaddleClas in 30 minutes](./docs/en/tutorials/quick_start_en.md)
 - [Model introduction and model zoo](./docs/en/models/models_intro_en.md)
     - [Model zoo overview](#Model_zoo_overview)
+    - [SSLD pretrained models](#SSLD_pretrained_series)
     - [ResNet and Vd series](#ResNet_and_Vd_series)
     - [Mobile series](#Mobile_series)
     - [SEResNeXt and Res2Net series](#SEResNeXt_and_Res2Net_series)
@@ -114,6 +115,28 @@ Curves of accuracy to the inference time and storage size of common mobile-side
 
 ![](./docs/images/models/mobile_arm_top1.png)
 
+<a name="SSLD_pretrained_series"></a>
+### SSLD pretrained models
+Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to [SSLD distillation tutorial](./docs/en/advanced_tutorials/distillation/distillation_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                                                                                         |
+|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|
+| ResNet34_vd_ssld         | 0.7972    | 0.9490    | 2.43427               | 6.22257              | 7.39     | 21.82     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_ssld_pretrained.pdparams)         |
+| ResNet50_vd_<br>ssld    | 0.8239    | 0.9610    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams)    |
+| ResNet50_vd_<br>ssld_v2 | 0.8300    | 0.9640    | 3.53131               | 8.09057              | 8.67     | 25.58     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_v2_pretrained.pdparams) |
+| ResNet101_vd_<br>ssld   | 0.8373    | 0.9669    | 6.11704               | 13.76222             | 16.1     | 44.57     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_ssld_pretrained.pdparams)   |
+| MobileNetV1_<br>ssld                 | 0.7789    | 0.9394    | 32.523048              | 1.11     | 4.19      | 16      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams)                 |
+| MobileNetV2_<br>ssld                 | 0.7674    | 0.9339    | 23.317699              | 0.6      | 3.44      | 14      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams)                 |
+| MobileNetV3_<br>small_x0_35_ssld          | 0.5555    | 0.7771    | 2.6352                 | 0.026    | 1.66      | 6.9     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_ssld_pretrained.pdparams)          |
+| MobileNetV3_<br>large_x1_0_ssld      | 0.7896    | 0.9448    | 19.30835               | 0.45     | 5.47      | 21      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)      |
+| MobileNetV3_small_<br>x1_0_ssld      | 0.7129    | 0.9010    | 6.5463                 | 0.123    | 2.94      | 12      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_ssld_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/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams)               |
+| Res2Net50_vd_<br>26w_4s_ssld | 0.831    | 0.966    | 4.52712              | 9.65722             | 8.37     | 25.06     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| Res2Net101_vd_<br>26w_4s_ssld | 0.839    | 0.971    | 8.08729              | 17.31208             | 16.67    | 45.22     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| Res2Net200_vd_<br>26w_4s_ssld | 0.8513    | 0.9742    | 14.67806              | 32.35032             | 31.49    | 76.21     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| HRNet_W18_C_ssld | 0.81162    | 0.95804    | 7.40636          | 13.29752         | 4.14     | 21.29     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_ssld_pretrained.pdparams) |
+| HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) |
+| SE_HRNet_W64_C_ssld | 0.8475    |  0.9726    |    31.69770      |     94.99546      | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
 
 
 <a name="ResNet_and_Vd_series"></a>

README_cn.md

@@ -59,6 +59,7 @@
 - [30分钟玩转PaddleClas](./docs/zh_CN/tutorials/quick_start.md)
 - [模型库介绍和预训练模型](./docs/zh_CN/models/models_intro.md)
     - [模型库概览图](#模型库概览图)
+    - [SSLD知识蒸馏系列](#SSLD知识蒸馏系列)
     - [ResNet及其Vd系列](#ResNet及其Vd系列)
     - [移动端系列](#移动端系列)
     - [SEResNeXt与Res2Net系列](#SEResNeXt与Res2Net系列)
@@ -118,6 +119,29 @@
 ![](./docs/images/models/mobile_arm_top1.png)
 
 
+<a name="SSLD知识蒸馏系列"></a>
+### SSLD知识蒸馏预训练模型
+基于SSLD知识蒸馏的预训练模型列表如下所示，更多关于SSLD知识蒸馏方案的介绍可以参考：[SSLD知识蒸馏文档](./docs/zh_CN/advanced_tutorials/distillation/distillation.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                                                                                         |
+|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|
+| ResNet34_vd_ssld         | 0.7972    | 0.9490    | 2.43427               | 6.22257              | 7.39     | 21.82     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_ssld_pretrained.pdparams)         |
+| ResNet50_vd_<br>ssld    | 0.8239    | 0.9610    | 3.53131               | 8.09057              | 8.67     | 25.58     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams)    |
+| ResNet50_vd_<br>ssld_v2 | 0.8300    | 0.9640    | 3.53131               | 8.09057              | 8.67     | 25.58     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_v2_pretrained.pdparams) |
+| ResNet101_vd_<br>ssld   | 0.8373    | 0.9669    | 6.11704               | 13.76222             | 16.1     | 44.57     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_ssld_pretrained.pdparams)   |
+| MobileNetV1_<br>ssld                 | 0.7789    | 0.9394    | 32.523048              | 1.11     | 4.19      | 16      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_ssld_pretrained.pdparams)                 |
+| MobileNetV2_<br>ssld                 | 0.7674    | 0.9339    | 23.317699              | 0.6      | 3.44      | 14      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams)                 |
+| MobileNetV3_<br>small_x0_35_ssld          | 0.5555    | 0.7771    | 2.6352                 | 0.026    | 1.66      | 6.9     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_ssld_pretrained.pdparams)          |
+| MobileNetV3_<br>large_x1_0_ssld      | 0.7896    | 0.9448    | 19.30835               | 0.45     | 5.47      | 21      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_ssld_pretrained.pdparams)      |
+| MobileNetV3_small_<br>x1_0_ssld      | 0.7129    | 0.9010    | 6.5463                 | 0.123    | 2.94      | 12      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_ssld_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/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams)               |
+| Res2Net50_vd_<br>26w_4s_ssld | 0.831    | 0.966    | 4.52712              | 9.65722             | 8.37     | 25.06     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| Res2Net101_vd_<br>26w_4s_ssld | 0.839    | 0.971    | 8.08729              | 17.31208             | 16.67    | 45.22     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| Res2Net200_vd_<br>26w_4s_ssld | 0.8513    | 0.9742    | 14.67806              | 32.35032             | 31.49    | 76.21     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| HRNet_W18_C_ssld | 0.81162    | 0.95804    | 7.40636          | 13.29752         | 4.14     | 21.29     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_ssld_pretrained.pdparams) |
+| HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) |
+| SE_HRNet_W64_C_ssld | 0.8475    |  0.9726    |    31.69770      |     94.99546      | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
+
 <a name="ResNet及其Vd系列"></a>
 ### ResNet及其Vd系列
 
@@ -260,7 +284,7 @@ HRNet系列模型的精度、速度指标如下表所示，更多关于该系列
 | HRNet_W40_C | 0.7877    | 0.9447    | 12.12202         | 25.68184         | 25.41    | 57.55     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams) |
 | HRNet_W44_C | 0.7900    | 0.9451    | 13.19858         | 32.25202         | 29.79    | 67.06     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams) |
 | HRNet_W48_C | 0.7895    | 0.9442    | 13.70761         | 34.43572         | 34.58    | 77.47     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
-| HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
+| HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams) |
 | HRNet_W64_C | 0.7930    | 0.9461    | 17.57527         | 47.9533          | 57.83    | 128.06    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) |
 | SE_HRNet_W64_C_ssld | 0.8475    |  0.9726    |    31.69770      |     94.99546      | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |