5分鐘學會Markdown語法，在GitHub上寫出漂亮文字，提升別人閱讀體驗

阿新 • • 發佈：2018-11-14

今天研究下在GitHub中如何漂亮的書寫readme部分

markdown是一種純文字格式的標記語言。通過簡單的標記語法，它可以使普通文字內容具有一定的格式。

1、因為是純文字，所以只要支援markdown的地方都能獲得一樣的編輯效果，可以讓作者擺脫排版的困擾，專心寫作。
2、操作簡單。比如:WYSIWYG編輯時標記個標題，先選中內容，再點選導航欄的標題按鈕，選擇幾級標題。要三個步驟。而markdown只需要在標題內容前加#即可

Markdown書寫規則

GitHub readme部分帶格式的效果

GitHub readme部分的原始文稿

Markdown書寫規則：

標題部分

加粗，加大，爽啊；

方法：給標題前面新增一個星號，也可以加多個星號，代表不同級別標題

例如

# Semantic Soft Segmentation, ACM SIGGRAPH 2018

加黑部分

方法：左右兩邊新增兩個星號。

比如：**stand-alone**，

程式碼部分

黃色部分就是程式碼部分，可以左右拖動

方法：切換英語鍵盤，點選數字1左邊的那個鍵，按三下，把程式碼放進反引號就好看了

比如

```
pip3 install -r requirements.txt
```

超連結

綠色部分，比如project page是超連結，給project page新增英語方括號，後面用英語圓括號包圍網址

比如

[project page](http://people.inf.ethz.ch/aksoyy/sss/)

GitHub readme部分帶格式的效果

https://github.com/BTUJACK/SIGGRAPH18SSS

Semantic Soft Segmentation, ACM SIGGRAPH 2018

This repository includes the semantic feature (128-D) generation approach presented in
Yagiz Aksoy, Tae-Hyun Oh, Sylvain Paris, Marc Pollefeys and Wojciech Matusik, "Semantic Soft Segmentation", ACM Transactions on Graphics (Proc. SIGGRAPH), 2018 
Also, note that this repository is NOT stand-alone. The spectral segmentation implementation can be found [here]. The low-dimension projection to 3-dimension and its filtering code are available in the repository.

Please refer to the [project page] for more information.

Note that only the feature generator is presented in this repository and the training code is not included.

Requirements

Python 3.6, TensorFlow >= 1.4 and other common packages listed in requirements.txt.

The code has been tested on {Linux Ubuntu 16.04, TensorFlow-GPU 1.4} and {Windows 10, TensorFlow-GPU 1.8}.

Installation

Install dependencies
pip3 install -r requirements.txt
Clone or download this repository.

Download the pre-trained model.

Extract the model and put the extracted "model" folder into the folder where the repository is cloned.

e.g., If the repository is cloned at "/project/sss", then move the model to be "/project/sss/model")

Run "run_extract_feat.sh", which will process sample images in the "samples" folder. If you want to run your own images, notice that image files should be the PNG formats.

Notes

At this point, only PNG image format is supported in the image loader.

Citation

If you use this code, please cite our paper:
@ARTICLE{sss,
author={Ya\u{g}{\i}z Aksoy and Tae-Hyun Oh and Sylvain Paris and Marc Pollefeys and Wojciech Matusik},
title={Semantic Soft Segmentation},
journal={ACM Transactions on Graphics (Proc. SIGGRAPH)},
year={2018},
pages = {72:1-72:13},
volume = {37},
number = {4}
}
This code is for protyping research ideas; thus, please use this code only for non-commercial purpose only.

Credits

The part of the base codes (the tools in the "deeplab_resnet" directory) are borrowed from (Re-)implementation of DeepLab-ResNet-TensorFlow Likewise, our code (the tools in "kaffe" directory) is benefited from Caffe to TensorFlow

Also, our architecture is implemented on top of the base architecture, DeepLab-ResNet-101.
@article{CP2016Deeplab,
      title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
      author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
      journal={arXiv:1606.00915},
      year={2016}
    }

GitHub readme部分的原始文稿

下面是大佬使用Markdown格式寫

# Semantic Soft Segmentation, ACM SIGGRAPH 2018

This repository includes the semantic feature (128-D) generation approach presented in

    Yagiz Aksoy, Tae-Hyun Oh, Sylvain Paris, Marc Pollefeys and Wojciech Matusik, "Semantic Soft Segmentation", ACM Transactions on Graphics (Proc. SIGGRAPH), 2018 

Also, note that this repository is NOT **stand-alone**. 
The spectral segmentation implementation can be found [[here](https://github.com/yaksoy/SemanticSoftSegmentation)].
The low-dimension projection to 3-dimension and its filtering code are available in the repository.

Please refer to the [[project page](http://people.inf.ethz.ch/aksoyy/sss/)] for more information.

Note that only the feature generator is presented in this repository and the training code is not included.

# Requirements
Python 3.6, TensorFlow >= 1.4 and other common packages listed in requirements.txt.

The code has been tested on {Linux Ubuntu 16.04, TensorFlow-GPU 1.4} and {Windows 10, TensorFlow-GPU 1.8}.

# Installation
1. Install dependencies
```
pip3 install -r requirements.txt
```
2. Clone or download this repository.
3. Download the [pre-trained](http://cvg.ethz.ch/research/semantic-soft-segmentation/SSS_model.zip) model.
4. Extract the model and put the extracted "model" folder into the folder where the repository is cloned.
   - e.g., If the repository is cloned at "/project/sss", then move the model to be "/project/sss/model")
5. Run "run_extract_feat.sh", which will process sample images in the "samples" folder. If you want to run your own images, notice that image files should be the PNG formats.


# Notes
At this point, only PNG image format is supported in the image loader.

# Citation
If you use this code, please cite our paper:

```
@ARTICLE{sss,
author={Ya\u{g}{\i}z Aksoy and Tae-Hyun Oh and Sylvain Paris and Marc Pollefeys and Wojciech Matusik},
title={Semantic Soft Segmentation},
journal={ACM Transactions on Graphics (Proc. SIGGRAPH)},
year={2018},
pages = {72:1-72:13},
volume = {37},
number = {4}
}
```
This code is for protyping research ideas; thus, please use this code only for non-commercial purpose only.  

# Credits
The part of the base codes (the tools in the "deeplab_resnet" directory) are borrowed from [(Re-)implementation of DeepLab-ResNet-TensorFlow](https://github.com/DrSleep/tensorflow-deeplab-resnet#using-your-dataset)
Likewise, our code (the tools in "kaffe" directory) is benefited from [Caffe to TensorFlow](https://github.com/ethereon/caffe-tensorflow)

Also, our architecture is implemented on top of the base architecture, DeepLab-ResNet-101.

```
@article{CP2016Deeplab,
      title={DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs},
      author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
      journal={arXiv:1606.00915},
      year={2016}
    }
```

更多Markdown語法可以點選https://www.jianshu.com/p/191d1e21f7ed

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