diff --git a/lane_det/README.md b/lane_det/README.md
index 8dce1ba..b8916fe 100644
--- a/lane_det/README.md
+++ b/lane_det/README.md
@@ -3,7 +3,7 @@
 The Pytorch implementation is [Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection).
 
 ## How to Run
-
+```
 1. generate lane.wts and lane.onnx from pytorch with tusimple_18.pth
 
 git clone https://github.com/wang-xinyu/tensorrtx.git
@@ -14,20 +14,20 @@ git clone https://github.com/cfzd/Ultra-Fast-Lane-Detection.git
 // go to Ultra-Fast-Lane-Detection
 python gen_wts.py
 // a file 'lane.wts' will be generated.
-// then 
+// then ( not necessary )
 python pth2onnx.py
 //a file 'lane.onnx' will be generated.
 
 2. build tensorrtx/lane_det and run
 
-  ```
-  mkdir build
-  cd build
-  cmake ..
-  make
-  sudo ./lane_det -s             // serialize model to plan file i.e. 'DBNet.engine'
-  sudo ./lane_det -d  ../data // deserialize plan file and run inference, the images in data will be processed.
-  ```
+mkdir build
+cd build
+cmake ..
+make
+sudo ./lane_det -s             // serialize model to plan file i.e. 'lane.engine'
+sudo ./lane_det -d  ../data // deserialize plan file and run inference, the images in data will be processed.
+
+```
 
 ## More Information
 1. Changed the preprocess and postprocess in tensorrtx, give a different way to convert NHWC to NCHW in preprocess and just show the reslut using opencv rather than saving the result in postprocess.