diff --git a/examples/incremental_learning/helmet_detection/README.md b/examples/incremental_learning/helmet_detection/README.md
index e96c695a1..fff82d4c0 100644
--- a/examples/incremental_learning/helmet_detection/README.md
+++ b/examples/incremental_learning/helmet_detection/README.md
@@ -12,49 +12,50 @@ and updates models based on the data generated at the edge.
 Follow the [Sedna installation document](/docs/setup/install.md) to install Sedna.
 
 ### Prepare Model
-In this example, we need to prepare base model and deploy model in advance.
+In this example, we need to prepare base model and deploy model in advance. 
+download [models](https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/model.tar.gz), including base model and deploy model.  
 
-
-
-download [models](https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/model.tar.gz), including base model and deploy model.
 ```
 cd /
 wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/models.tar.gz
 tar -zxvf models.tar.gz
 ```
 ### Prepare for Inference Worker
-in this example, we simulate a inference worker for helmet detection, the worker will upload hard examples to `HE_SAVED_URL`, while
-it inferences data from local video. we need to make following preparations:
-* make sure following localdirs exist
+in this example, we simulate a inference worker for helmet detection, the worker will upload hard examples to `HE_SAVED_URL`, while 
+it inferences data from local video. we need to make following preparations:  
+
+* make sure following localdirs exist  
 ```
 mkdir -p /incremental_learning/video/
 mkdir -p /incremental_learning/he/
 mkdir -p /data/helmet_detection
 mkdir /output
-
 ```
-* download [video](https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/video.tar.gz), unzip video.tar.gz, and put it into `/incremental_learning/video/`
+
+* download [video](https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/video.tar.gz), unzip video.tar.gz, and put it into `/incremental_learning/video/`  
 
 ```
 cd /incremental_learning/video/
 wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/helmet-detection/video.tar.gz
 tar -zxvf video.tar.gz
 ```
+
 ### Prepare Image
 this example use the image:  
 ```
 kubeedge/sedna-example-incremental-learning-helmet-detection:v0.3.0
 ```
-This image is generated by the script [build_images.sh](/examples/build_image.sh), used for creating training, eval and inference worker.
 
-### Create Incremental Job
-in this example, `$WORKER_NODE` is a custom node, you can fill it which you actually run.
+This image is generated by the script [build_images.sh](/examples/build_image.sh), used for creating training, eval and inference worker.  
 
+### Create Incremental Job
+in this example, `$WORKER_NODE` is a custom node, you can fill it which you actually run.  
 
 ```
 WORKER_NODE="edge-node" 
 ```
-Create Dataset
+
+Create Dataset  
 
 ```
 kubectl create -f - <<EOF
@@ -69,7 +70,7 @@ spec:
 EOF
 ```
 
-Create Initial Model to simulate the initial model in incremental learning scenario.
+Create Initial Model to simulate the initial model in incremental learning scenario.  
 
 ```
 kubectl create -f - <<EOF
@@ -83,7 +84,7 @@ spec:
 EOF
 ```
 
-Create Deploy Model
+Create Deploy Model  
 
 ```
 kubectl create -f - <<EOF
@@ -97,10 +98,9 @@ spec:
 EOF
 ```
 
-Start The Incremental Learning Job
+Start The Incremental Learning Job  
 
 ```
-
 kubectl create -f - <<EOF
 apiVersion: sedna.io/v1alpha1
 kind: IncrementalLearningJob
@@ -207,16 +207,19 @@ spec:
   outputDir: "/output"
 EOF
 ```
+
 1. The `Dataset` describes data with labels and `HE_SAVED_URL` indicates the address of the deploy container for uploading hard examples. Users will mark label for the hard examples in the address.
 2. Ensure that the path of outputDir in the YAML file exists on your node. This path will be directly mounted to the container.
 
-
 ### Check Incremental Learning Job
-query the service status
+query the service status  
+
 ```
 kubectl get incrementallearningjob helmet-detection-demo
 ```
-In the `IncrementalLearningJob` resource helmet-detection-demo, the following trigger is configured:
+
+In the `IncrementalLearningJob` resource helmet-detection-demo, the following trigger is configured:  
+
 ```
 trigger:
   checkPeriodSeconds: 60
diff --git a/examples/storage/s3/README.md b/examples/storage/s3/README.md
index 8ded5e19b..324a3fbdd 100644
--- a/examples/storage/s3/README.md
+++ b/examples/storage/s3/README.md
@@ -3,6 +3,7 @@
 This example based on the example: [Using Incremental Learning Job in Helmet Detection Scenario](/examples/incremental_learning/helmet_detection/README.md)
 
 ### Create a secret with your S3 user credential.
+
 ```yaml
 apiVersion: v1
 kind: Secret
@@ -15,15 +16,17 @@ stringData: # use `stringData` for raw credential string or `data` for base64 en
   ACCESS_KEY_ID: XXXX
   SECRET_ACCESS_KEY: XXXXXXXX
 ```
-   
+
 ### Attach the created secret to the Model/Dataset/Job.
-`EDGE_NODE` and `CLOUD_NODE` are custom nodes, you can fill it which you actually run.
+`EDGE_NODE` and `CLOUD_NODE` are custom nodes, you can fill it which you actually run.  
+
 ```
 EDGE_NODE="edge-node" 
 CLOUD_NODE="cloud-node"
 ```
 
-* Attach the created secret to the Model.
+* Attach the created secret to the Model.  
+
 ```yaml
 kubectl create -f - <<EOF
 apiVersion: sedna.io/v1alpha1
@@ -50,8 +53,8 @@ spec:
 EO
 ```
 
-* Attach the created secret to the Dataset.
-    
+* Attach the created secret to the Dataset.  
+
 ```yaml
 kubectl $action -f - <<EOF
 apiVersion: sedna.io/v1alpha1
@@ -173,4 +176,4 @@ spec:
               type: Directory
   outputDir: "/incremental_learning/output"
 EOF
-```
\ No newline at end of file
+```