update readme

README.md

@@ -1,5 +1,5 @@
 # Spatial Temporal Graph Convolutional Networks (ST-GCN)
-A graph convolutional network for skeleton based action recognition.
+A graph convolutional network for skeleton-based action recognition.
 
 <div align="center">
     <img src="resource/info/pipeline.png">
@@ -18,7 +18,7 @@ This repository holds the codebase, dataset and models for the paper>
 - June. 1, 2018 - We update our code base and complete the PyTorch 0.4.0 migration.
 
 ## Visulization of ST-GCN in Action
-Our demo for skeleton based action recognition:
+Our demo for skeleton-based action recognition:
 <p align="center">
     <img src="resource/info/demo_video.gif", width="1200">
 </p>
@@ -103,18 +103,18 @@ and extract files with
 cd st-gcn
 unzip <path to st-gcn-processed-data.zip>
 ```
-Otherwise, for processing raw data by yourself,
-please refer to below guidances.
+**Otherwise, for processing raw data by yourself,
+please refer to below guidances.**
 
-### Kinetics-skeleton
+#### Kinetics-skeleton
 [Kinetics](https://deepmind.com/research/open-source/open-source-datasets/kinetics/) is a video-based dataset for action recognition which only provide raw video clips without skeleton data. Kinetics dataset include To obatin the joint locations, we first resized all videos to the resolution of 340x256 and converted the frame rate to 30 fps.  Then, we extracted skeletons from each frame in Kinetics by [Openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose). The extracted skeleton data we called **Kinetics-skeleton**(7.5GB) can be directly downloaded from [GoogleDrive](https://drive.google.com/open?id=1SPQ6FmFsjGg3f59uCWfdUWI-5HJM_YhZ) or [BaiduYun](https://pan.baidu.com/s/1dwKG2TLvG-R1qeIiE4MjeA#list/path=%2FShare%2FAAAI18%2Fkinetics-skeleton&parentPath=%2FShare).
 
 After uncompressing, rebuild the database by this command:
 ```
 python tools/kinetics_gendata.py --data_path <path to kinetics-skeleton>
 ```
 
-### NTU RGB+D
+#### NTU RGB+D
 NTU RGB+D can be downloaded from [their website](http://rose1.ntu.edu.sg/datasets/actionrecognition.asp).
 Only the **3D skeletons**(5.8GB) modality is required in our experiments. After that, this command should be used to build the database for training or evaluation:
 ```