From f3a7cd42a709f958a0077d68e7689f1746cb412f Mon Sep 17 00:00:00 2001
From: gaotingquan <gaotingquan@baidu.com>
Date: Tue, 7 Jun 2022 11:49:27 +0000
Subject: [PATCH] docs: update

---
 docs/en/inference_deployment/whl_deploy_en.md | 4 ++--
 docs/zh_CN/inference_deployment/whl_deploy.md | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/en/inference_deployment/whl_deploy_en.md b/docs/en/inference_deployment/whl_deploy_en.md
index 224d41a7..9fd72232 100644
--- a/docs/en/inference_deployment/whl_deploy_en.md
+++ b/docs/en/inference_deployment/whl_deploy_en.md
@@ -212,14 +212,14 @@ You can save the prediction result(s) as pre-label, only need to use `pre_label_
 ```python
 from paddleclas import PaddleClas
 clas = PaddleClas(model_name='ResNet50', save_dir='./output_pre_label/')
-infer_imgs = 'docs/images/inference_deployment/whl_' # it can be infer_imgs folder path which contains all of images you want to predict.
+infer_imgs = 'docs/images/' # it can be infer_imgs folder path which contains all of images you want to predict.
 result=clas.predict(infer_imgs)
 print(next(result))
 ```
 
 * CLI
 ```bash
-paddleclas --model_name='ResNet50' --infer_imgs='docs/images/inference_deployment/whl_' --save_dir='./output_pre_label/'
+paddleclas --model_name='ResNet50' --infer_imgs='docs/images/' --save_dir='./output_pre_label/'
 ```
 
 <a name="4.8"></a>
diff --git a/docs/zh_CN/inference_deployment/whl_deploy.md b/docs/zh_CN/inference_deployment/whl_deploy.md
index 14582ace..e6ad7090 100644
--- a/docs/zh_CN/inference_deployment/whl_deploy.md
+++ b/docs/zh_CN/inference_deployment/whl_deploy.md
@@ -18,7 +18,7 @@ PaddleClas 支持 Python Whl 包方式进行预测，目前 Whl 包方式仅支
    - [4.6 对 `NumPy.ndarray` 格式数据进行预测](#4.6)
    - [4.7 保存预测结果](#4.7)
    - [4.8 指定 label name](#4.8)
-   
+
 
 <a name="1"></a>
 ## 1. 安装 paddleclas
@@ -212,14 +212,14 @@ print(next(result))
 ```python
 from paddleclas import PaddleClas
 clas = PaddleClas(model_name='ResNet50', save_dir='./output_pre_label/')
-infer_imgs = 'docs/images/whl/' # it can be infer_imgs folder path which contains all of images you want to predict.
+infer_imgs = 'docs/images/' # it can be infer_imgs folder path which contains all of images you want to predict.
 result=clas.predict(infer_imgs)
 print(next(result))
 ```
 
 * CLI
 ```bash
-paddleclas --model_name='ResNet50' --infer_imgs='docs/images/whl/' --save_dir='./output_pre_label/'
+paddleclas --model_name='ResNet50' --infer_imgs='docs/images/' --save_dir='./output_pre_label/'
 ```
 
 <a name="4.8"></a>
-- 
GitLab