model

本文介绍了一款基于Markdown的编辑器,支持丰富的扩展功能如代码高亮、LaTeX公式、UML图表等,并具备离线写作及自动保存功能。

欢迎使用Markdown编辑器写博客

本Markdown编辑器使用StackEdit修改而来,用它写博客,将会带来全新的体验哦:

  • Markdown和扩展Markdown简洁的语法
  • 代码块高亮
  • 图片链接和图片上传
  • LaTex数学公式
  • UML序列图和流程图
  • 离线写博客
  • 导入导出Markdown文件
  • 丰富的快捷键

快捷键

  • 加粗 Ctrl + B
  • 斜体 Ctrl + I
  • 引用 Ctrl + Q
  • 插入链接 Ctrl + L
  • 插入代码 Ctrl + K
  • 插入图片 Ctrl + G
  • 提升标题 Ctrl + H
  • 有序列表 Ctrl + O
  • 无序列表 Ctrl + U
  • 横线 Ctrl + R
  • 撤销 Ctrl + Z
  • 重做 Ctrl + Y

Markdown及扩展

Markdown 是一种轻量级标记语言,它允许人们使用易读易写的纯文本格式编写文档,然后转换成格式丰富的HTML页面。 —— [ 维基百科 ]

使用简单的符号标识不同的标题,将某些文字标记为粗体或者斜体,创建一个链接等,详细语法参考帮助?。

本编辑器支持 Markdown Extra ,  扩展了很多好用的功能。具体请参考Github.

表格

Markdown Extra 表格语法:

项目价格
Computer$1600
Phone$12
Pipe$1

可以使用冒号来定义对齐方式:

项目价格数量
Computer1600 元5
Phone12 元12
Pipe1 元234

定义列表

Markdown Extra 定义列表语法: 项目1 项目2
定义 A
定义 B
项目3
定义 C

定义 D

定义D内容

代码块

代码块语法遵循标准markdown代码,例如:

@requires_authorization
def somefunc(param1='', param2=0):
    '''A docstring'''
    if param1 > param2: # interesting
        print 'Greater'
    return (param2 - param1 + 1) or None
class SomeClass:
    pass
>>> message = '''interpreter
... prompt'''

脚注

生成一个脚注1.

目录

[TOC]来生成目录:

数学公式

使用MathJax渲染LaTex 数学公式,详见math.stackexchange.com.

  • 行内公式,数学公式为: Γ(n)=(n1)!nN
  • 块级公式:

x=b±b24ac2a

更多LaTex语法请参考 这儿.

UML 图:

可以渲染序列图:

Created with Raphaël 2.1.0 张三 张三 李四 李四 嘿,小四儿, 写博客了没? 李四愣了一下,说: 忙得吐血,哪有时间写。

或者流程图:

Created with Raphaël 2.1.0 开始 我的操作 确认? 结束 yes no
  • 关于 序列图 语法,参考 这儿,
  • 关于 流程图 语法,参考 这儿.

离线写博客

即使用户在没有网络的情况下,也可以通过本编辑器离线写博客(直接在曾经使用过的浏览器中输入write.blog.youkuaiyun.com/mdeditor即可。Markdown编辑器使用浏览器离线存储将内容保存在本地。

用户写博客的过程中,内容实时保存在浏览器缓存中,在用户关闭浏览器或者其它异常情况下,内容不会丢失。用户再次打开浏览器时,会显示上次用户正在编辑的没有发表的内容。

博客发表后,本地缓存将被删除。 

用户可以选择 把正在写的博客保存到服务器草稿箱,即使换浏览器或者清除缓存,内容也不会丢失。

注意:虽然浏览器存储大部分时候都比较可靠,但为了您的数据安全,在联网后,请务必及时发表或者保存到服务器草稿箱

浏览器兼容

  1. 目前,本编辑器对Chrome浏览器支持最为完整。建议大家使用较新版本的Chrome。
  2. IE9以下不支持
  3. IE9,10,11存在以下问题
    1. 不支持离线功能
    2. IE9不支持文件导入导出
    3. IE10不支持拖拽文件导入


  1. 这里是 脚注内容.
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for YOLO: Missing key(s) in state_dict: "model.model.0.conv.weight", "model.model.0.bn.weight", "model.model.0.bn.bias", "model.model.0.bn.running_mean", "model.model.0.bn.running_var", "model.model.1.conv.weight", "model.model.1.bn.weight", "model.model.1.bn.bias", "model.model.1.bn.running_mean", "model.model.1.bn.running_var", "model.model.2.cv1.conv.weight", "model.model.2.cv1.bn.weight", "model.model.2.cv1.bn.bias", "model.model.2.cv1.bn.running_mean", "model.model.2.cv1.bn.running_var", "model.model.2.cv2.conv.weight", "model.model.2.cv2.bn.weight", "model.model.2.cv2.bn.bias", "model.model.2.cv2.bn.running_mean", "model.model.2.cv2.bn.running_var", "model.model.2.m.0.cv1.conv.weight", "model.model.2.m.0.cv1.bn.weight", "model.model.2.m.0.cv1.bn.bias", "model.model.2.m.0.cv1.bn.running_mean", "model.model.2.m.0.cv1.bn.running_var", "model.model.2.m.0.cv2.conv.weight", "model.model.2.m.0.cv2.bn.weight", "model.model.2.m.0.cv2.bn.bias", "model.model.2.m.0.cv2.bn.running_mean", "model.model.2.m.0.cv2.bn.running_var", "model.model.3.conv.weight", "model.model.3.bn.weight", "model.model.3.bn.bias", "model.model.3.bn.running_mean", "model.model.3.bn.running_var", "model.model.4.cv1.conv.weight", "model.model.4.cv1.bn.weight", "model.model.4.cv1.bn.bias", "model.model.4.cv1.bn.running_mean", "model.model.4.cv1.bn.running_var", "model.model.4.cv2.conv.weight", "model.model.4.cv2.bn.weight", "model.model.4.cv2.bn.bias", "model.model.4.cv2.bn.running_mean", "model.model.4.cv2.bn.running_var", "model.model.4.m.0.cv1.conv.weight", "model.model.4.m.0.cv1.bn.weight", "model.model.4.m.0.cv1.bn.bias", "model.model.4.m.0.cv1.bn.running_mean", "model.model.4.m.0.cv1.bn.running_var", "model.model.4.m.0.cv2.conv.weight", "model.model.4.m.0.cv2.bn.weight", "model.model.4.m.0.cv2.bn.bias", "model.model.4.m.0.cv2.bn.running_mean", "model.model.4.m.0.cv2.bn.running_var", "model.model.5.conv.weight", "model.model.5.bn.weight", "model.model.5.bn.bias", "model.model.5.bn.running_mean", "model.model.5.bn.running_var", "model.model.6.cv1.conv.weight", "model.model.6.cv1.bn.weight", "model.model.6.cv1.bn.bias", "model.model.6.cv1.bn.running_mean", "model.model.6.cv1.bn.running_var", "model.model.6.cv2.conv.weight", "model.model.6.cv2.bn.weight", "model.model.6.cv2.bn.bias", "model.model.6.cv2.bn.running_mean", "model.model.6.cv2.bn.running_var", "model.model.6.m.0.cv1.conv.weight", "model.model.6.m.0.cv1.bn.weight", "model.model.6.m.0.cv1.bn.bias", "model.model.6.m.0.cv1.bn.running_mean", "model.model.6.m.0.cv1.bn.running_var", "model.model.6.m.0.cv2.conv.weight", "model.model.6.m.0.cv2.bn.weight", "model.model.6.m.0.cv2.bn.bias", "model.model.6.m.0.cv2.bn.running_mean", "model.model.6.m.0.cv2.bn.running_var", "model.model.6.m.0.cv3.conv.weight", "model.model.6.m.0.cv3.bn.weight", "model.model.6.m.0.cv3.bn.bias", "model.model.6.m.0.cv3.bn.running_mean", "model.model.6.m.0.cv3.bn.running_var", "model.model.6.m.0.m.0.cv1.conv.weight", "model.model.6.m.0.m.0.cv1.bn.weight", "model.model.6.m.0.m.0.cv1.bn.bias", "model.model.6.m.0.m.0.cv1.bn.running_mean", "model.model.6.m.0.m.0.cv1.bn.running_var", "model.model.6.m.0.m.0.cv2.conv.weight", "model.model.6.m.0.m.0.cv2.bn.weight", "model.model.6.m.0.m.0.cv2.bn.bias", "model.model.6.m.0.m.0.cv2.bn.running_mean", "model.model.6.m.0.m.0.cv2.bn.running_var", "model.model.6.m.0.m.1.cv1.conv.weight", "model.model.6.m.0.m.1.cv1.bn.weight", "model.model.6.m.0.m.1.cv1.bn.bias", "model.model.6.m.0.m.1.cv1.bn.running_mean", "model.model.6.m.0.m.1.cv1.bn.running_var", "model.model.6.m.0.m.1.cv2.conv.weight", "model.model.6.m.0.m.1.cv2.bn.weight", "model.model.6.m.0.m.1.cv2.bn.bias", "model.model.6.m.0.m.1.cv2.bn.running_mean", "model.model.6.m.0.m.1.cv2.bn.running_var", "model.model.7.conv.weight", "model.model.7.bn.weight", "model.model.7.bn.bias", "model.model.7.bn.running_mean", "model.model.7.bn.running_var", "model.model.8.cv1.conv.weight", "model.model.8.cv1.bn.weight", "model.model.8.cv1.bn.bias", "model.model.8.cv1.bn.running_mean", "model.model.8.cv1.bn.running_var", "model.model.8.cv2.conv.weight", "model.model.8.cv2.bn.weight", "model.model.8.cv2.bn.bias", "model.model.8.cv2.bn.running_mean", "model.model.8.cv2.bn.running_var", "model.model.8.m.0.cv1.conv.weight", "model.model.8.m.0.cv1.bn.weight", "model.model.8.m.0.cv1.bn.bias", "model.model.8.m.0.cv1.bn.running_mean", "model.model.8.m.0.cv1.bn.running_var", "model.model.8.m.0.cv2.conv.weight", "model.model.8.m.0.cv2.bn.weight", "model.model.8.m.0.cv2.bn.bias", "model.model.8.m.0.cv2.bn.running_mean", "model.model.8.m.0.cv2.bn.running_var", "model.model.8.m.0.cv3.conv.weight", "model.model.8.m.0.cv3.bn.weight", "model.model.8.m.0.cv3.bn.bias", "model.model.8.m.0.cv3.bn.running_mean", "model.model.8.m.0.cv3.bn.running_var", "model.model.8.m.0.m.0.cv1.conv.weight", "model.model.8.m.0.m.0.cv1.bn.weight", "model.model.8.m.0.m.0.cv1.bn.bias", "model.model.8.m.0.m.0.cv1.bn.running_mean", "model.model.8.m.0.m.0.cv1.bn.running_var", "model.model.8.m.0.m.0.cv2.conv.weight", "model.model.8.m.0.m.0.cv2.bn.weight", "model.model.8.m.0.m.0.cv2.bn.bias", "model.model.8.m.0.m.0.cv2.bn.running_mean", "model.model.8.m.0.m.0.cv2.bn.running_var", "model.model.8.m.0.m.1.cv1.conv.weight", "model.model.8.m.0.m.1.cv1.bn.weight", "model.model.8.m.0.m.1.cv1.bn.bias", "model.model.8.m.0.m.1.cv1.bn.running_mean", "model.model.8.m.0.m.1.cv1.bn.running_var", "model.model.8.m.0.m.1.cv2.conv.weight", "model.model.8.m.0.m.1.cv2.bn.weight", "model.model.8.m.0.m.1.cv2.bn.bias", "model.model.8.m.0.m.1.cv2.bn.running_mean", "model.model.8.m.0.m.1.cv2.bn.running_var", "model.model.9.cv1.conv.weight", "model.model.9.cv1.bn.weight", "model.model.9.cv1.bn.bias", "model.model.9.cv1.bn.running_mean", "model.model.9.cv1.bn.running_var", "model.model.9.cv2.conv.weight", "model.model.9.cv2.bn.weight", "model.model.9.cv2.bn.bias", "model.model.9.cv2.bn.running_mean", "model.model.9.cv2.bn.running_var", "model.model.10.cv1.conv.weight", "model.model.10.cv1.bn.weight", "model.model.10.cv1.bn.bias", "model.model.10.cv1.bn.running_mean", "model.model.10.cv1.bn.running_var", "model.model.10.cv2.conv.weight", "model.model.10.cv2.bn.weight", "model.model.10.cv2.bn.bias", "model.model.10.cv2.bn.running_mean", "model.model.10.cv2.bn.running_var", "model.model.10.m.0.attn.qkv.conv.weight", "model.model.10.m.0.attn.qkv.bn.weight", "model.model.10.m.0.attn.qkv.bn.bias", "model.model.10.m.0.attn.qkv.bn.running_mean", "model.model.10.m.0.attn.qkv.bn.running_var", "model.model.10.m.0.attn.proj.conv.weight", "model.model.10.m.0.attn.proj.bn.weight", "model.model.10.m.0.attn.proj.bn.bias", "model.model.10.m.0.attn.proj.bn.running_mean", "model.model.10.m.0.attn.proj.bn.running_var", "model.model.10.m.0.attn.pe.conv.weight", "model.model.10.m.0.attn.pe.bn.weight", "model.model.10.m.0.attn.pe.bn.bias", "model.model.10.m.0.attn.pe.bn.running_mean", "model.model.10.m.0.attn.pe.bn.running_var", "model.model.10.m.0.ffn.0.conv.weight", "model.model.10.m.0.ffn.0.bn.weight", "model.model.10.m.0.ffn.0.bn.bias", "model.model.10.m.0.ffn.0.bn.running_mean", "model.model.10.m.0.ffn.0.bn.running_var", "model.model.10.m.0.ffn.1.conv.weight", "model.model.10.m.0.ffn.1.bn.weight", "model.model.10.m.0.ffn.1.bn.bias", "model.model.10.m.0.ffn.1.bn.running_mean", "model.model.10.m.0.ffn.1.bn.running_var", "model.model.13.cv1.conv.weight", "model.model.13.cv1.bn.weight", "model.model.13.cv1.bn.bias", "model.model.13.cv1.bn.running_mean", "model.model.13.cv1.bn.running_var", "model.model.13.cv2.conv.weight", "model.model.13.cv2.bn.weight", "model.model.13.cv2.bn.bias", "model.model.13.cv2.bn.running_mean", "model.model.13.cv2.bn.running_var", "model.model.13.m.0.cv1.conv.weight", "model.model.13.m.0.cv1.bn.weight", "model.model.13.m.0.cv1.bn.bias", "model.model.13.m.0.cv1.bn.running_mean", "model.model.13.m.0.cv1.bn.running_var", "model.model.13.m.0.cv2.conv.weight", "model.model.13.m.0.cv2.bn.weight", "model.model.13.m.0.cv2.bn.bias", "model.model.13.m.0.cv2.bn.running_mean", "model.model.13.m.0.cv2.bn.running_var", "model.model.16.cv1.conv.weight", "model.model.16.cv1.bn.weight", "model.model.16.cv1.bn.bias", "model.model.16.cv1.bn.running_mean", "model.model.16.cv1.bn.running_var", "model.model.16.cv2.conv.weight", "model.model.16.cv2.bn.weight", "model.model.16.cv2.bn.bias", "model.model.16.cv2.bn.running_mean", "model.model.16.cv2.bn.running_var", "model.model.16.m.0.cv1.conv.weight", "model.model.16.m.0.cv1.bn.weight", "model.model.16.m.0.cv1.bn.bias", "model.model.16.m.0.cv1.bn.running_mean", "model.model.16.m.0.cv1.bn.running_var", "model.model.16.m.0.cv2.conv.weight", "model.model.16.m.0.cv2.bn.weight", "model.model.16.m.0.cv2.bn.bias", "model.model.16.m.0.cv2.bn.running_mean", "model.model.16.m.0.cv2.bn.running_var", "model.model.17.conv.weight", "model.model.17.bn.weight", "model.model.17.bn.bias", "model.model.17.bn.running_mean", "model.model.17.bn.running_var", "model.model.19.cv1.conv.weight", "model.model.19.cv1.bn.weight", "model.model.19.cv1.bn.bias", "model.model.19.cv1.bn.running_mean", "model.model.19.cv1.bn.running_var", "model.model.19.cv2.conv.weight", "model.model.19.cv2.bn.weight", "model.model.19.cv2.bn.bias", "model.model.19.cv2.bn.running_mean", "model.model.19.cv2.bn.running_var", "model.model.19.m.0.cv1.conv.weight", "model.model.19.m.0.cv1.bn.weight", "model.model.19.m.0.cv1.bn.bias", "model.model.19.m.0.cv1.bn.running_mean", "model.model.19.m.0.cv1.bn.running_var", "model.model.19.m.0.cv2.conv.weight", "model.model.19.m.0.cv2.bn.weight", "model.model.19.m.0.cv2.bn.bias", "model.model.19.m.0.cv2.bn.running_mean", "model.model.19.m.0.cv2.bn.running_var", "model.model.20.conv.weight", "model.model.20.bn.weight", "model.model.20.bn.bias", "model.model.20.bn.running_mean", "model.model.20.bn.running_var", "model.model.22.cv1.conv.weight", "model.model.22.cv1.bn.weight", "model.model.22.cv1.bn.bias", "model.model.22.cv1.bn.running_mean", "model.model.22.cv1.bn.running_var", "model.model.22.cv2.conv.weight", "model.model.22.cv2.bn.weight", "model.model.22.cv2.bn.bias", "model.model.22.cv2.bn.running_mean", "model.model.22.cv2.bn.running_var", "model.model.22.m.0.cv1.conv.weight", "model.model.22.m.0.cv1.bn.weight", "model.model.22.m.0.cv1.bn.bias", "model.model.22.m.0.cv1.bn.running_mean", "model.model.22.m.0.cv1.bn.running_var", "model.model.22.m.0.cv2.conv.weight", "model.model.22.m.0.cv2.bn.weight", "model.model.22.m.0.cv2.bn.bias", "model.model.22.m.0.cv2.bn.running_mean", "model.model.22.m.0.cv2.bn.running_var", "model.model.22.m.0.cv3.conv.weight", "model.model.22.m.0.cv3.bn.weight", "model.model.22.m.0.cv3.bn.bias", "model.model.22.m.0.cv3.bn.running_mean", "model.model.22.m.0.cv3.bn.running_var", "model.model.22.m.0.m.0.cv1.conv.weight", 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"model.model.23.cv3.2.0.0.bn.bias", "model.model.23.cv3.2.0.0.bn.running_mean", "model.model.23.cv3.2.0.0.bn.running_var", "model.model.23.cv3.2.0.1.conv.weight", "model.model.23.cv3.2.0.1.bn.weight", "model.model.23.cv3.2.0.1.bn.bias", "model.model.23.cv3.2.0.1.bn.running_mean", "model.model.23.cv3.2.0.1.bn.running_var", "model.model.23.cv3.2.1.0.conv.weight", "model.model.23.cv3.2.1.0.bn.weight", "model.model.23.cv3.2.1.0.bn.bias", "model.model.23.cv3.2.1.0.bn.running_mean", "model.model.23.cv3.2.1.0.bn.running_var", "model.model.23.cv3.2.1.1.conv.weight", "model.model.23.cv3.2.1.1.bn.weight", "model.model.23.cv3.2.1.1.bn.bias", "model.model.23.cv3.2.1.1.bn.running_mean", "model.model.23.cv3.2.1.1.bn.running_var", "model.model.23.cv3.2.2.weight", "model.model.23.cv3.2.2.bias", "model.model.23.dfl.conv.weight". Unexpected key(s) in state_dict: "nc", "scales", "backbone", "head".
08-26
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