docs: update benchmark curve

docs/en/algorithm_introduction/ImageNet_models_en.md

@@ -8,14 +8,17 @@ Based on the ImageNet-1k classification dataset, the 35 classification network s
 
 Curves of accuracy to the inference time of common server-side models are shown as follows.
 
-![](../images/models/T4_benchmark/t4.fp32.bs1.main_fps_top1.png)
-
+![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
 
 Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.
 
-![](../images/models/mobile_arm_storage.png)
+![](../../images/models/mobile_arm_storage.png)
 
-![](../images/models/mobile_arm_top1.png)
+![](../../images/models/mobile_arm_top1.png)
+
+Curves of accuracy to the inference time of some VisionTransformer models are shown as follows.
+
+![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
 
 <a name="SSLD_pretrained_series"></a>
 ### SSLD pretrained models

docs/en/models/models_intro_en.md

@@ -5,7 +5,7 @@
 Based on the ImageNet1k classification dataset, the 29 classification network structures supported by PaddleClas and the corresponding 134 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters.
 
 ## Evaluation environment
-* CPU evaluation environment is based on Snapdragon 855 (SD855).
+* Arm evaluation environment is based on Snapdragon 855 (SD855).
 * The GPU evaluation environment is based on V100 and TensorRT, and the evaluation script is as follows.
 
 ```shell
@@ -23,12 +23,11 @@ python tools/infer/predict.py \
     --batch_size=1
 ```
 
-![](../../images/models/T4_benchmark/t4.fp32.bs4.main_fps_top1.png)
-
-![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.jpg)
+![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
 
 ![](../../images/models/mobile_arm_top1.png)
 
+![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
 
 > If you think this document is helpful to you, welcome to give a star to our project:[https://github.com/PaddlePaddle/PaddleClas](https://github.com/PaddlePaddle/PaddleClas)
 

docs/zh_CN/algorithm_introduction/ImageNet_models.md

@@ -44,8 +44,7 @@
 
 常见服务器端模型的精度指标与其预测耗时的变化曲线如下图所示。
 
-![](../../images/models/T4_benchmark/t4.fp32.bs1.main_fps_top1.png)
-
+![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
 
 常见移动端模型的精度指标与其预测耗时、模型存储大小的变化曲线如下图所示。
 
@@ -53,6 +52,10 @@
 
 ![](../../images/models/mobile_arm_top1.png)
 
+部分VisionTransformer模型的精度指标与其预测耗时的变化曲线如下图所示。
+
+![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
+
 <a name="2"></a>
 
 ## 2. SSLD 知识蒸馏预训练模型

docs/zh_CN/models/models_intro.md

@@ -16,17 +16,16 @@
  <a name='2'></a>
 
 ## 2. 评估环境
-* CPU 的评估环境基于骁龙 855（SD855）。
+* Arm 的评估环境基于骁龙 855（SD855）。
 * Intel CPU 的评估环境基于 Intel(R) Xeon(R) Gold 6148。
 * GPU 评估环境基于 V100 和 TensorRT。
 
 
-![](../../images/models/T4_benchmark/t4.fp32.bs4.main_fps_top1.png)
-
-![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.jpg)
+![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
 
 ![](../../images/models/mobile_arm_top1.png)
 
+![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
 
 > 如果您觉得此文档对您有帮助，欢迎 star 我们的项目：[https://github.com/PaddlePaddle/PaddleClas](https://github.com/PaddlePaddle/PaddleClas)