Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
疯人忠
Cvat
提交
11e398d3
C
Cvat
项目概览
疯人忠
/
Cvat
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
DevOps
流水线
流水线任务
计划
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
C
Cvat
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
DevOps
DevOps
流水线
流水线任务
计划
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
流水线任务
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
11e398d3
编写于
1月 10, 2023
作者:
M
Michael Selasi Dzamesi
提交者:
GitHub
1月 10, 2023
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Update serverless-tutorial.md (#5360)
上级
3d9c5add
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
18 addition
and
22 deletion
+18
-22
site/content/en/docs/manual/advanced/serverless-tutorial.md
site/content/en/docs/manual/advanced/serverless-tutorial.md
+18
-22
未找到文件。
site/content/en/docs/manual/advanced/serverless-tutorial.md
浏览文件 @
11e398d3
...
...
@@ -6,28 +6,24 @@ weight: 32
## Introduction
Computers have now become our partners. They help us to solve routine problems,
fix mistakes, find information, etc. It is a natural idea to use their
compute power to annotate datasets. There are multiple DL models for
classification, object detection, semantic segmentation which can do
data annotation for us. And it is relatively simple to integrate your
own ML/DL solution into CVAT.
But the world is not perfect and we don't have a silver bullet which can
solve all our problems. Usually, available DL models are trained on public
datasets which cannot cover all specific cases. Very often you want to
detect objects which cannot be recognized by these models. Our annotation
requirements can be so strict that automatically
annotated objects cannot be accepted as is, and it is easier to annotate them
from scratch. You always need to keep in mind all these mentioned limitations.
Even if you have a DL solution which can
_perfectly_
annotate 50% of your data, it means that manual work will only be
reduced in half.
When we know that DL models can help us to annotate data faster, the next
question is how to use them? In CVAT all such DL models are implemented
as serverless functions for the
[
Nuclio
][
nuclio-homepage
]
serverless platform.
And there are multiple implemented functions which can be
Leveraging the power of computers to solve daily routine problems,
fix mistakes, and find information has become second nature. It is therefore
natural to use computing power in annotating datasets. There are multiple
publicly available DL models for classification, object detection, and semantic
segmentation which can be used for data annotation. Whilst some of these publicly
available DL models can be found on CVAT, it is relatively simple to integrate your
privately trained ML/DL model into CVAT.
With the imperfection of the world, alongside the unavailability of a silver bullet
that can solve all our problems; publicly available DL models cannot be used when we
want to detect niche or specific objects on which these publicly available models were not trained.
As annotation requirements can be sometimes strict, automatically annotated objects cannot be accepted
as it is, and it is easier to annotate them from scratch. With these limitations in mind, a DL solution
that can _perfectly_ annotate 50% of your data equates to reducing manual annotation by half.
Since we know DL models can help us to annotate faster, how then do we use them?
In CVAT all such DL models are implemented as serverless functions using the
[
Nuclio
][
nuclio-homepage
]
serverless platform. There are multiple implemented functions that can be
found in the
[
serverless
][
cvat-builtin-serverless
]
directory such as _Mask RCNN,
Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut_, etc.
Follow
[
the installation guide
][
cvat-auto-annotation-guide
]
to build and deploy
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录