docs: resize pic

docs/en/algorithm_introduction/ImageNet_models_en.md

@@ -8,17 +8,21 @@ 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/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png" width="800">
+</div>
 
-Curves of accuracy to the inference time and storage size of common mobile-side models are shown as follows.
+Curves of accuracy to the inference time of common mobile-side models are shown as follows.
 
-![](../../images/models/mobile_arm_storage.png)
-
-![](../../images/models/mobile_arm_top1.png)
+<div align="center">
+    <img src="../../images/models/mobile_arm_top1.png" width="800">
+</div>
 
 Curves of accuracy to the inference time of some VisionTransformer models are shown as follows.
 
-![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png" width="800">
+</div>
 
 <a name="SSLD_pretrained_series"></a>
 ### SSLD pretrained models

docs/en/models/models_intro_en.md

@@ -23,11 +23,17 @@ python tools/infer/predict.py \
     --batch_size=1
 ```
 
-![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png" width="800">
+</div>
 
-![](../../images/models/mobile_arm_top1.png)
+<div align="center">
+    <img src="../../images/models/mobile_arm_top1.png" width="800">
+</div>
 
-![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png" width="800">
+</div>
 
 > 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

@@ -44,17 +44,21 @@
 
 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/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png" width="800">
+</div>
 
-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。
+常见移动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/mobile_arm_storage.png)
-
-![](../../images/models/mobile_arm_top1.png)
+<div align="center">
+    <img src="../../images/models/mobile_arm_top1.png" width="800">
+</div>
 
 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)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png" width="800">
+</div>
 
 <a name="2"></a>
 

docs/zh_CN/models/models_intro.md

@@ -20,12 +20,17 @@
 * 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。
 
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png" width="800">
+</div>
 
-![](../../images/models/V100_benchmark/v100.fp32.bs1.main_fps_top1_s.png)
+<div align="center">
+    <img src="../../images/models/mobile_arm_top1.png" width="800">
+</div>
 
-![](../../images/models/mobile_arm_top1.png)
-
-![](../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png)
+<div align="center">
+    <img src="../../images/models/V100_benchmark/v100.fp32.bs1.visiontransformer.png" width="800">
+</div>
 
 > 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)