5分钟学会Markdown语法，在GitHub上写出漂亮文本，提升别人阅读体验
今天研究下在GitHub中如何漂亮的书写readme部分
markdown是一种纯文本格式的标记语言。通过简单的标记语法，它可以使普通文本内容具有一定的格式。
1、因为是纯文本，所以只要支持markdown的地方都能获得一样的编辑效果，可以让作者摆脱排版的困扰，专心写作。
2、操作简单。比如:WYSIWYG编辑时标记个标题，先选中内容，再点击导航栏的标题按钮，选择几级标题。要三个步骤。而markdown只需要在标题内容前加#即可
目录：
Markdown书写规则
GitHub readme部分带格式的效果
GitHub readme部分的原始文稿
Markdown书写规则：
标题部分
加粗，加大，爽啊；
方法：给标题前面添加一个星号，也可以加多个星号，代表不同级别标题
例如
# Semantic Soft Segmentation, ACM SIGGRAPH 2018
加黑部分
方法：左右两边添加两个星号。
比如：**stand-alone**，
代码部分
黄色部分就是代码部分，可以左右拖动
方法：切换英语键盘，点击数字1左边的那个键，按三下，把代码放进反引号就好看了
比如
```
pip3 install -r requirements.txt
```
超链接
绿色部分，比如project page是超链接，给project page添加英语方括号，后面用英语圆括号包围网址
比如
[project page](http://people.inf.ethz.ch/aksoyy/sss/)
GitHub readme部分带格式的效果
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), 2018Also, 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格式写
# 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语法可以点击https://www.jianshu.com/p/191d1e21f7ed
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