diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md
index c37485843..2045bbdc3 100644
--- a/deploy/hubserving/readme.md
+++ b/deploy/hubserving/readme.md
@@ -25,7 +25,13 @@ pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/s
 分类推理模型权重文件：./inference/cls_infer.pdiparams
 ```  
 
-**模型路径可在`params.py`中查看和修改。** 我们也提供了大量基于ImageNet-1k数据集的预训练模型，模型列表及下载地址详见[模型库概览](../../docs/zh_CN/models/models_intro.md)，也可以替换成自己训练转换好的模型。
+**注意**：
+* 模型路径可在`./PaddleClas/deploy/hubserving/clas/params.py`中查看和修改。
+  ```python
+  cfg.model_file = "./inference/cls_infer.pdmodel"
+  cfg.params_file = "./inference/cls_infer.pdiparams"
+  ```
+* 我们也提供了大量基于ImageNet-1k数据集的预训练模型，模型列表及下载地址详见[模型库概览](../../docs/zh_CN/models/models_intro.md)，也可以使用自己训练转换好的模型。
 
 ### 3. 安装服务模块
 针对Linux环境和Windows环境，安装命令如下。
@@ -111,14 +117,21 @@ hub serving start -c deploy/hubserving/clas/config.json
 
 ```python tools/test_hubserving.py server_url image_path```  
 
-需要给脚本传递2个参数：  
+需要给脚本传递2个必须参数：
 - **server_url**：服务地址，格式为  
 `http://[ip_address]:[port]/predict/[module_name]`  
-- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径
+- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径。
 - **top_k**：[**可选**] 返回前 `top_k` 个 `score` ，默认为 `1`。
+- **batch_size**：[**可选**] 以`batch_size`大小为单位进行预测，默认为`1`。
+- **resize_short**：[**可选**] 将图像等比例缩放到最短边为`resize_short`，默认为`256`。
+- **resize**：[**可选**] 将图像resize到`resize * resize`尺寸，默认为`224`。
+- **normalize**：[**可选**] 是否对图像进行normalize处理，默认为`True`。
+
+**注意**：如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸。需要指定`--resize_short=384 --resize=384`。
+
 
 访问示例：  
-```python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG 5```
+```python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG --top_k 5```
 
 ### 返回结果格式说明
 返回结果为列表（list），包含top-k个分类结果，以及对应的得分，还有此图片预测耗时，具体如下：
diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md
index 0f34fd347..5863ce97c 100644
--- a/deploy/hubserving/readme_en.md
+++ b/deploy/hubserving/readme_en.md
@@ -113,15 +113,22 @@ After the service starts, you can use the following command to send a prediction
 python tools/test_hubserving.py server_url image_path
 ```  
 
-Two parameters need to be passed to the script:
-- **server_url**：service address，format of which is
+Two required parameters need to be passed to the script:
+- **server_url**: service address，format of which is
 `http://[ip_address]:[port]/predict/[module_name]`  
-- **image_path**：Test image path, can be a single image path or an image directory path
-- **top_k**：[**Optional**] Return the top `top_k` 's scores ,default by `1`.
+- **image_path**: Test image path, can be a single image path or an image directory path
+- **top_k**: [**Optional**] Return the top `top_k` 's scores ,default by `1`.
+- **batch_size**: [**Optional**] batch_size. Default by `1`.
+- **resize_short**: [**Optional**] Resize the input image according to short size. Default by `256`.
+- **resize**: [**Optional**] Resize the input image. Default by `224`.
+- **normalize**: [**Optional**] Whether normalize the input image. Default by `True`.
+
+**Notice**:
+If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `--resize_short=384`, `--resize=384`.
 
 **Eg.**
 ```shell
-python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG 5
+python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG --top_k 5
 ```
 
 ### Returned result format