docs: add the description and link of vdl

docs/en/models_training/classification_en.md

@@ -74,6 +74,8 @@ After the data is determined, the model often determines the upper limit of the
 
 After preparing the data and model, you can start training the model and update the parameters of the model. After many iterations, a trained model can finally be obtained for image classification tasks. The training process of image classification requires a lot of experience and involves the setting of many hyperparameters. PaddleClas provides a series of [training tuning methods](./train_strategy_en.md), which can quickly help you obtain a high-precision model.
 
+PaddleClas support training with VisualDL to visualize the metric. VisualDL is a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters, and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. For more information, please refer to [VisualDL](../others/VisualDL_en.md).
+
 <a name="2.4"></a>
 
 ### 2.4 Evaluation

docs/en/models_training/recognition_en.md

@@ -138,6 +138,8 @@ cd ../../
 
 For training and evaluation on a single GPU, the `tools/train.py` and `tools/eval.py` scripts are recommended.
 
+PaddleClas support training with VisualDL to visualize the metric. VisualDL is a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters, and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. For more information, please refer to [VisualDL](../others/VisualDL_en.md).
+
 <a name="2.2.1"></a>  
 
 #### 2.2.1 Model Training

docs/zh_CN/models_training/classification.md

@@ -67,6 +67,8 @@ CIFAR-10 数据集由 10 个类的 60000 个彩色图像组成，图像分辨率
 
 在准备好数据、模型后，便可以开始迭代模型并更新模型的参数。经过多次迭代最终可以得到训练好的模型来做图像分类任务。图像分类的训练过程需要很多经验，涉及很多超参数的设置，PaddleClas 提供了一些列的[训练调优方法](./train_strategy.md)，可以快速助你获得高精度的模型。
 
+同时，PaddleClas 还支持使用VisualDL 可视化训练过程。VisualDL 是飞桨可视化分析工具，以丰富的图表呈现训练参数变化趋势、模型结构、数据样本、高维数据分布等。可帮助用户更清晰直观地理解深度学习模型训练过程及模型结构，进而实现高效的模型优化。更多细节请查看[VisualDL](../others/VisualDL.md)。
+
 <a name="2.4"></a>
 ### 2.4 模型评估
 

docs/zh_CN/models_training/recognition.md

@@ -134,6 +134,8 @@ cd ../../
 
 在基于单卡 GPU 上训练与评估，推荐使用 `tools/train.py` 与 `tools/eval.py` 脚本。
 
+PaddleClas 支持使用 VisualDL 可视化训练过程。VisualDL 是飞桨可视化分析工具，以丰富的图表呈现训练参数变化趋势、模型结构、数据样本、高维数据分布等。可帮助用户更清晰直观地理解深度学习模型训练过程及模型结构，进而实现高效的模型优化。更多细节请查看[VisualDL](../others/VisualDL.md)。
+
 <a name="2.2.1"></a>
 
 #### 2.2.1 特征模型训练