add res2net200_vd_26w_4s_ssld doc

docs/en/advanced_tutorials/distillation/distillation_en.md

@@ -82,6 +82,7 @@ Training process is carried out on the large-scale dataset with 5 million images
 | MobileNetV3_small_x1_0 | 360 | 1e-5 |  5760/24 | 3.65625 | cosine_decay_warmup | 70.11% |
 | ResNet50_vd | 360 | 7e-5 | 1024/32 | 0.4 | cosine_decay_warmup | 82.07% |
 | ResNet101_vd | 360 | 7e-5 | 1024/32 | 0.4 | cosine_decay_warmup | 83.41% |
+| Res2Net200_vd_26w_4s | 360 | 4e-5 | 1024/32 | 0.4 | cosine_decay_warmup | 84.82% |
 
 ## finetuning using ImageNet1k
 
@@ -96,6 +97,7 @@ Finetuning is carried out on ImageNet1k dataset to restore distribution between
 | MobileNetV3_small_x1_0 | 30 | 1e-5 |  6400/32 | 0.025 | cosine_decay_warmup | 71.28% |
 | ResNet50_vd | 60 | 7e-5 | 1024/32 | 0.004 | cosine_decay_warmup | 82.39% |
 | ResNet101_vd | 30 | 7e-5 | 1024/32 | 0.004 | cosine_decay_warmup | 83.73% |
+| Res2Net200_vd_26w_4s | 360 | 4e-5 | 1024/32 | 0.004 | cosine_decay_warmup | 85.13% |
 
 ## Data agmentation and Fix strategy
 

docs/en/models/SEResNext_and_Res2Net_en.md

@@ -33,6 +33,7 @@ At present, there are a total of 24 pretrained models of the three categories op
 | Res2Net50_14w_8s      | 0.795  | 0.947  | 0.781             | 0.939             | 9.010        | 25.720            |
 | Res2Net101_vd_26w_4s  | 0.806  | 0.952  |                   |                   | 16.670       | 45.220            |
 | Res2Net200_vd_26w_4s  | 0.812  | 0.957  |                   |                   | 31.490       | 76.210            |
+| Res2Net200_vd_26w_4s_ssld  | **0.851**  | 0.974  |                   |                   | 31.490       | 76.210            |
 | ResNeXt50_32x4d       | 0.778  | 0.938  | 0.778             |                   | 8.020        | 23.640            |
 | ResNeXt50_vd_32x4d    | 0.796  | 0.946  |                   |                   | 8.500        | 23.660            |
 | ResNeXt50_64x4d       | 0.784  | 0.941  |                   |                   | 15.060       | 42.360            |

docs/en/models/models_intro_en.md

@@ -130,6 +130,7 @@ python tools/infer/predict.py \
     - [Res2Net50_14w_8s](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_14w_8s_pretrained.tar)
     - [Res2Net101_vd_26w_4s](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net101_vd_26w_4s_pretrained.tar)
     - [Res2Net200_vd_26w_4s](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_pretrained.tar)
+    - [Res2Net200_vd_26w_4s_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_ssld_pretrained.tar)
 
 
 - Inception series
@@ -259,4 +260,4 @@ python tools/infer/predict.py \
 
 <a name="ref22">[22]</a> Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920.
 
-<a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.
\ No newline at end of file
+<a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.

docs/en/update_history_en.md

 # Release Notes
 
+* 2020.07.14
+    * Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 85.13%.
+    * Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 84.00%.
+
 * 2020.06.17
     * Add English documents。
 

docs/zh_CN/advanced_tutorials/distillation/distillation.md

@@ -85,6 +85,7 @@ SSLD的流程图如下图所示。
 | MobileNetV3_small_x1_0 | 360 | 1e-5 |  5760/24 | 3.65625 | cosine_decay_warmup | 70.11% |
 | ResNet50_vd | 360 | 7e-5 | 1024/32 | 0.4 | cosine_decay_warmup | 82.07% |
 | ResNet101_vd | 360 | 7e-5 | 1024/32 | 0.4 | cosine_decay_warmup | 83.41% |
+| Res2Net200_vd_26w_4s | 360 | 4e-5 | 1024/32 | 0.4 | cosine_decay_warmup | 84.82% |
 
 ## 3.3 ImageNet1k训练集finetune
 
@@ -99,6 +100,7 @@ SSLD的流程图如下图所示。
 | MobileNetV3_small_x1_0 | 30 | 1e-5 |  6400/32 | 0.025 | cosine_decay_warmup | 71.28% |
 | ResNet50_vd | 60 | 7e-5 | 1024/32 | 0.004 | cosine_decay_warmup | 82.39% |
 | ResNet101_vd | 30 | 7e-5 | 1024/32 | 0.004 | cosine_decay_warmup | 83.73% |
+| Res2Net200_vd_26w_4s | 360 | 4e-5 | 1024/32 | 0.004 | cosine_decay_warmup | 85.13% |
 
 
 ## 3.4 数据增广以及基于Fix策略的微调

docs/zh_CN/models/SEResNext_and_Res2Net.md

@@ -32,6 +32,7 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案，该方案可
 | Res2Net50_14w_8s      | 0.795  | 0.947  | 0.781             | 0.939             | 9.010        | 25.720            |
 | Res2Net101_vd_26w_4s  | 0.806  | 0.952  |                   |                   | 16.670       | 45.220            |
 | Res2Net200_vd_26w_4s  | 0.812  | 0.957  |                   |                   | 31.490       | 76.210            |
+| Res2Net200_vd_26w_4s  | **0.851**  | 0.974  |                   |                   | 31.490       | 76.210            |
 | ResNeXt50_32x4d       | 0.778  | 0.938  | 0.778             |                   | 8.020        | 23.640            |
 | ResNeXt50_vd_32x4d    | 0.796  | 0.946  |                   |                   | 8.500        | 23.660            |
 | ResNeXt50_64x4d       | 0.784  | 0.941  |                   |                   | 15.060       | 42.360            |

docs/zh_CN/models/models_intro.md

@@ -130,6 +130,7 @@ python tools/infer/predict.py \
     - [Res2Net50_14w_8s](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net50_14w_8s_pretrained.tar)
     - [Res2Net101_vd_26w_4s](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net101_vd_26w_4s_pretrained.tar)
     - [Res2Net200_vd_26w_4s](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_pretrained.tar)
+    - [Res2Net200_vd_26w_4s_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_ssld_pretrained.tar)
 
 
 - Inception系列

docs/zh_CN/update_history.md

 # 更新日志
 
+* 2020.07.14
+    * 添加Res2Net200_vd_26w_4s_ssld模型，在ImageNet上Top-1 Acc可达85.13%。
+    * 添加Fix_ResNet50_vd_ssld_v2模型，，在ImageNet上Top-1 Acc可达84.0%。
+
 * 2020.06.17
     * 添加英文文档。