diff --git a/README.md b/README.md
index 0e08607..e884f57 100755
--- a/README.md
+++ b/README.md
@@ -10,38 +10,38 @@ Understanding interactions between humans and objects is one of the fundamental
 ## Installation
 - Clone this repository. This repository is mainly based on [CenterNet](https://github.com/xingyizhou/CenterNet) and [iCAN](https://github.com/vt-vl-lab/iCAN).
 
-```Shell
-    IPNet_ROOT=/path/to/clone/IPNet
-    git clone https://github.com/vaesl/IP-Net $IPNet_ROOT
-```
+  ```Shell
+  IPNet_ROOT=/path/to/clone/IPNet
+  git clone https://github.com/vaesl/IP-Net "$IPNet_ROOT"
+  ```
 - The code was tested on Ubuntu 18.04, with [Anaconda](https://www.anaconda.com/download) Python 3.6 and [PyTorch]((http://pytorch.org/)) v1.0.1. 
 NVIDIA GPUs are needed for testing. After install Anaconda, create a new conda environment, activate the environment and install pytorch1.0.1.
 
-```Shell
-    conda create -n IPNet python=3.6
-    source activate IPNet
-    conda install pytorch=1.0.1 torchvision -c pytorch
-```
+  ```Shell
+  conda create -n IPNet python=3.6
+  source activate IPNet
+  conda install pytorch=1.0.1 torchvision -c pytorch
+  ```
 
 - Install the requirements. 
-```Shell
-    pip3 install -r requirements.txt
-```
+  ```Shell
+  pip3 install -r requirements.txt
+  ```
 - Compiling Center Pooling Layers.
-```Shell
-    cd IPNet_ROOT/src/lib/models/networks/py_utils/_cpools/
-    python setup.py install --user
-```
+  ```Shell
+  cd "$IPNet_ROOT"/src/lib/models/networks/py_utils/_cpools/
+  python setup.py install --user
+  ```
 
 - Install [COCOAPI](https://github.com/cocodataset/cocoapi):
 
-    ~~~
-    # COCOAPI=/path/to/clone/cocoapi
-    git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
-    cd $COCOAPI/PythonAPI
-    make
-    python setup.py install --user
-    ~~~
+  ```Shell
+  COCOAPI=/path/to/clone/cocoapi
+  git clone https://github.com/cocodataset/cocoapi.git "$COCOAPI"
+  cd "$COCOAPI"/PythonAPI
+  make
+  python setup.py install --user
+  ```
 
 ## Download
 To evaluate the performance reported in the paper, V-COCO and HICO-DET dataset as well as our trained models need to be downloaded.