blocks|key|700128|text|对于语义分割，图像的每个像素都应该被标记。完成这项任务有三种方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|700129|基于矢量的多边形，多边形|ordered-list-item|700130|基于像素的刷、橡皮擦|700131|人工智能工具|700132|在督学中，可以使用执行1,2,3的工具。|offset|length|700133|​|700134|📷|atomic|700135|700136|下面是两个比较多边形和人工智能工具的视频：汽车分割和食物分割。|700137|关于Supervisely的注释特性的更多细节可以找到这里。|700138|entityMap|0|LINK|mutability|MUTABLE|url|https://supervise.ly/|1|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/7817ba628bfa7f028fd3b89fececa148.gif|imageAlt|2|https://www.youtube.com/watch?v=VyUwHbqJ26g|3|https://www.youtube.com/watch?v=8VF9F6TSRVw|4|https://hackernoon.com/%25EF%25B8%258F-advanced-annotation-tools-in-deep-learning-training-data-for-computer-vision-with-supervisely-847f8699a9cb^0|0|0|0|0|1|2|0|0|0|0|1|1|0|0|L|4|2|Q|4|3|0|R|2|4|0^^$0|@$1|2|3|4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1H|8|@]|9|@]|A|$]]|$1|E|3|F|5|D|7|1I|8|@]|9|@]|A|$]]|$1|G|3|H|5|D|7|1J|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1K|8|@]|9|@$K|1L|L|1M|1|1N]]|A|$]]|$1|M|3|N|5|6|7|1O|8|@]|9|@]|A|$]]|$1|O|3|P|5|Q|7|1P|8|@]|9|@$K|1Q|L|1R|1|1S]]|A|$]]|$1|R|3|N|5|6|7|1T|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1U|8|@]|9|@$K|1V|L|1W|1|1X]|$K|1Y|L|1Z|1|20]]|A|$]]|$1|U|3|V|5|6|7|21|8|@]|9|@$K|22|L|23|1|24]]|A|$]]|$1|W|3|-4|5|6|7|25|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13]]|14|$5|15|10|16|A|$17|18|19|-4]]|1A|$5|Z|10|11|A|$12|1B]]|1C|$5|Z|10|11|A|$12|1D]]|1E|$5|Z|10|11|A|$12|1F]]]]

For semantic segmentation every pixel of an image should be labeled. There are three following ways to address the task:

<ol>
<li>Vector based - polygons, polylines </li>
<li>Pixel based - brush, eraser </li>
<li>AI-powered tools</li>
</ol>

In <a href="https://supervise.ly/" rel="nofollow noreferrer">Supervisely</a>, tools to perform 1,2,3 are available.

<a href="https://i.stack.imgur.com/gNZf3.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/gNZf3.gif" alt="SmartTool usage example"></a>

Below are two videos that compare polygon vs AI-powered tools: <a href="https://www.youtube.com/watch?v=VyUwHbqJ26g" rel="nofollow noreferrer">cars segmentation</a> and <a href="https://www.youtube.com/watch?v=8VF9F6TSRVw" rel="nofollow noreferrer">food segmentation</a>.

More details about annotation features of Supervisely can be found <a href="https://hackernoon.com/%EF%B8%8F-advanced-annotation-tools-in-deep-learning-training-data-for-computer-vision-with-supervisely-847f8699a9cb" rel="nofollow noreferrer">here</a>.

blocks|key|110891|text|脑海中闪现的一个工具是麻省理工学院的LabelMe工具箱：这个工具箱主要用于浏览数据集的现有标记图像，但它也可以选择注释新图像。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|110892|对于这个github存储库+UI，您可能会发现它很有用。|110893|entityMap|0|LINK|mutability|MUTABLE|url|http://labelme2.csail.mit.edu/Release3.0/browserTools/php/matlab_toolbox.php|1|https://github.com/tylin/coco-ui^0|B|H|0|0|2|B|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

One tool that pops to mind is <a href="http://labelme2.csail.mit.edu/Release3.0/browserTools/php/matlab_toolbox.php" rel="nofollow noreferrer">MIT's LabelMe toolbox</a>: this toolbox is mainly for browsing the existing labeled images of the dataset, but it has an option to annotated new images as well.

There's alos <a href="https://github.com/tylin/coco-ui" rel="nofollow noreferrer">this github repository</a> for <a href="http://mscoco.org/" rel="nofollow noreferrer">COCO</a> UI you might find useful.

blocks|key|699960|text|试试https://www.labelbox.io/。这是他们的图像分割模板的样子..。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|699961|​|699962|📷|atomic|699963|699964|很多代码都是开源，它们有一个托管服务来管理端到端的标签。|699965|entityMap|0|LINK|mutability|MUTABLE|url|https://www.labelbox.io/|1|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/b4cd32f72a98a93d1881866d8d613a0b.gif|imageAlt|2|https://github.com/labelbox/labelbox^0|2|O|0|0|0|0|1|1|0|0|6|2|2|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@$A|12|B|13|1|14]]|C|$]]|$1|D|3|E|5|6|7|15|8|@]|9|@]|C|$]]|$1|F|3|G|5|H|7|16|8|@]|9|@$A|17|B|18|1|19]]|C|$]]|$1|I|3|E|5|6|7|1A|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1B|8|@]|9|@$A|1C|B|1D|1|1E]]|C|$]]|$1|L|3|-4|5|6|7|1F|8|@]|9|@]|C|$]]]|M|$N|$5|O|P|Q|C|$R|S]]|T|$5|U|P|V|C|$W|X|Y|-4]]|Z|$5|O|P|Q|C|$R|10]]]]

Try out <a href="https://www.labelbox.io/" rel="nofollow noreferrer">https://www.labelbox.io/</a>. Here is what their image segmentation template looks like...

<a href="https://i.stack.imgur.com/llpgb.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/llpgb.gif" alt="enter image description here"></a>

A lot of the code is <a href="https://github.com/labelbox/labelbox" rel="nofollow noreferrer">open source</a> and they have a hosted service to manage labeling end to end.

blocks|key|700148|text|我创建了一个名为COCO注释器的开源工具:它提供了其他工具不足的任何特性：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|700149|直接导出为COCO格式|unordered-list-item|700150|对象分割|700151|用于分析数据的有用API端点|700152|导入已注释为COCO格式的数据集|700153|注释断开连接的对象为单个实例|700154|用任意数量的标签同时标记图像片段|700155|允许每个实例或对象的自定义元数据|700156|魔杖/选择工具|700157|使用google图像生成数据集|700158|用户认证系统|700159|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/jsbroks/coco-annotator/^0|8|7|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@$A|17|B|18|1|19]]|C|$]]|$1|D|3|E|5|F|7|1A|8|@]|9|@]|C|$]]|$1|G|3|H|5|F|7|1B|8|@]|9|@]|C|$]]|$1|I|3|J|5|F|7|1C|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|1D|8|@]|9|@]|C|$]]|$1|M|3|N|5|F|7|1E|8|@]|9|@]|C|$]]|$1|O|3|P|5|F|7|1F|8|@]|9|@]|C|$]]|$1|Q|3|R|5|F|7|1G|8|@]|9|@]|C|$]]|$1|S|3|T|5|F|7|1H|8|@]|9|@]|C|$]]|$1|U|3|V|5|F|7|1I|8|@]|9|@]|C|$]]|$1|W|3|X|5|F|7|1J|8|@]|9|@]|C|$]]|$1|Y|3|-4|5|6|7|1K|8|@]|9|@]|C|$]]]|Z|$10|$5|11|12|13|C|$14|15]]]]

I created an open source tool called <a href="https://github.com/jsbroks/coco-annotator/" rel="nofollow noreferrer">COCO Annotator</a>: It provide any features where other tools fall short:

<ul>
<li>Directly export to COCO format</li>
<li>Segmentation of objects</li>
<li>Useful API endpoints to analyze data</li>
<li>Import datasets already annotated in COCO format</li>
<li>Annotated disconnected objects as a single instance</li>
<li>Labeling image segments with any number of labels simultaneously</li>
<li>Allow custom metadata for each instance or object</li>
<li>Magic wand/select tool</li>
<li>Generate datasets using google images</li>
<li>User authentication system</li>
</ul>

blocks|key|111015|text|SageMaker地面真相有数据集管理和UI支持语义分割。https://aws.amazon.com/sagemaker/groundtruth/|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|111016|我们最近发布了对UI的增强，通过自动查找区域边界，大大加快了注释的速度。在这里阅读：https://aws.amazon.com/blogs/machine-learning/auto-segmenting-objects-when-performing-semantic-segmentation-labeling-with-amazon-sagemaker-ground-truth/|111017|​|111018|📷|atomic|111019|111020|entityMap|0|LINK|mutability|MUTABLE|url|https://aws.amazon.com/sagemaker/groundtruth/|1|https://aws.amazon.com/blogs/machine-learning/auto-segmenting-objects-when-performing-semantic-segmentation-labeling-with-amazon-sagemaker-ground-truth/|2|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/674c62a85c6978842a430c09528c65c7.gif|imageAlt^0|T|19|0|0|16|48|1|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@$A|12|B|13|1|14]]|C|$]]|$1|D|3|E|5|6|7|15|8|@]|9|@$A|16|B|17|1|18]]|C|$]]|$1|F|3|G|5|6|7|19|8|@]|9|@]|C|$]]|$1|H|3|I|5|J|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]]|C|$]]|$1|K|3|G|5|6|7|1E|8|@]|9|@]|C|$]]|$1|L|3|-4|5|6|7|1F|8|@]|9|@]|C|$]]]|M|$N|$5|O|P|Q|C|$R|S]]|T|$5|O|P|Q|C|$R|U]]|V|$5|W|P|X|C|$Y|Z|10|-4]]]]

SageMaker Ground Truth has dataset management and UIs to enable semantic segmentation. <a href="https://aws.amazon.com/sagemaker/groundtruth/" rel="nofollow noreferrer">https://aws.amazon.com/sagemaker/groundtruth/</a>

We recently released an enhancement to the UI which speeds up annotations considerably, by automatically finding region boundaries. Read about it here: <a href="https://aws.amazon.com/blogs/machine-learning/auto-segmenting-objects-when-performing-semantic-segmentation-labeling-with-amazon-sagemaker-ground-truth/" rel="nofollow noreferrer">https://aws.amazon.com/blogs/machine-learning/auto-segmenting-objects-when-performing-semantic-segmentation-labeling-with-amazon-sagemaker-ground-truth/</a>

<a href="https://i.stack.imgur.com/bv2KZ.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/bv2KZ.gif" alt="enter image description here"></a>

blocks|key|111157|text|整个过程是：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|111158|将数据加载到工具中|ordered-list-item|111159|画个形状。例如，使用多边形工具或其他形状工具。|111160|导出这些点并使用它们直接进行培训，或者将它们转换成一个密集的像素掩码。|111161|注解加工细节|111162|一般来说，基于矢量的工具是首选的，因为它们更精确。例如，左边是4个多边形点，右边是一个画笔。|111163|📷|atomic|111164|​|111165|反直觉，它甚至可以更快地跟踪点。例如，我简单地将鼠标移动到大纲上，该大纲记录了以下内容：|111166|111167|111168|但它实际上是以点的形式存储的：|111169|111170|111171|其他形状使用|111172|例如，可以使用椭圆或圆圈创建分段掩码。不需要实际手动创建多边形。|111173|111174|111175|111176|111177|111178|111179|Autobordering|111180|想要达到100%25像素覆盖率的主要概念之一是一个叫做自动边界的概念。基本上，它只是意味着物体的边缘很好地相交。例如，在Diffgram中，您可以选择两个点，它将创建一个匹配的边界。|111181|下面是一个例子：|111182|111183|111184|最后，还有许多其他的概念，如预标记，复制到下一个，跟踪等.|111185|工具|111186|Diffgram的分割标记软件是图像和视频注释的一个很好的工具选择。它是开放的，你可以在你的库伯内特斯群上运行它.|111187|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/34128b96abacb177309cc1effe67f734.png|imageAlt|1|https://developer.qcloudimg.com/http-save/yehe-900000/063f38dc4ac03dc9fda86ab4f2202e1b.png|2|https://developer.qcloudimg.com/http-save/yehe-900000/87740801bc84e91afab7de08bbb97d01.png|3|https://developer.qcloudimg.com/http-save/yehe-900000/6601518967dbcc52c133d10acfa53392.png|4|https://developer.qcloudimg.com/http-save/yehe-900000/b666ea384fb224bf41dc4d4cf945fe61.png|5|https://web.archive.org/web/20210506132248/https://d9hhrg4mnvzow.cloudfront.net/get.diffgram.com/image-segmentation-software/semantic-segmentation-labelling-tool/polygon/segmentation/85f8efdb-unnamed.gif|6|LINK|MUTABLE|url|https://get.diffgram.com/image-segmentation-software/semantic-segmentation-labelling-tool/polygon/segmentation/|7|https://diffgram.com/secure^0|0|6|0|0|0|0|0|6|0|0|0|1|0|0|0|0|0|1|1|0|0|0|0|1|2|0|0|0|6|0|0|0|0|1|3|0|0|0|0|1|4|0|0|0|D|0|0|0|0|1|5|0|0|0|0|2|0|9|6|6|18|8|7|0^^$0|@$1|2|3|4|5|6|7|2H|8|@$9|2I|A|2J|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|2K|8|@]|D|@]|E|$]]|$1|I|3|J|5|H|7|2L|8|@]|D|@]|E|$]]|$1|K|3|L|5|H|7|2M|8|@]|D|@]|E|$]]|$1|M|3|N|5|6|7|2N|8|@$9|2O|A|2P|B|C]]|D|@]|E|$]]|$1|O|3|P|5|6|7|2Q|8|@]|D|@]|E|$]]|$1|Q|3|R|5|S|7|2R|8|@]|D|@$9|2S|A|2T|1|2U]]|E|$]]|$1|T|3|U|5|6|7|2V|8|@]|D|@]|E|$]]|$1|V|3|W|5|6|7|2W|8|@]|D|@]|E|$]]|$1|X|3|R|5|S|7|2X|8|@]|D|@$9|2Y|A|2Z|1|30]]|E|$]]|$1|Y|3|U|5|6|7|31|8|@]|D|@]|E|$]]|$1|Z|3|10|5|6|7|32|8|@]|D|@]|E|$]]|$1|11|3|R|5|S|7|33|8|@]|D|@$9|34|A|35|1|36]]|E|$]]|$1|12|3|U|5|6|7|37|8|@]|D|@]|E|$]]|$1|13|3|14|5|6|7|38|8|@$9|39|A|3A|B|C]]|D|@]|E|$]]|$1|15|3|16|5|6|7|3B|8|@]|D|@]|E|$]]|$1|17|3|U|5|6|7|3C|8|@]|D|@]|E|$]]|$1|18|3|R|5|S|7|3D|8|@]|D|@$9|3E|A|3F|1|3G]]|E|$]]|$1|19|3|U|5|6|7|3H|8|@]|D|@]|E|$]]|$1|1A|3|U|5|6|7|3I|8|@]|D|@]|E|$]]|$1|1B|3|R|5|S|7|3J|8|@]|D|@$9|3K|A|3L|1|3M]]|E|$]]|$1|1C|3|U|5|6|7|3N|8|@]|D|@]|E|$]]|$1|1D|3|1E|5|6|7|3O|8|@$9|3P|A|3Q|B|C]]|D|@]|E|$]]|$1|1F|3|1G|5|6|7|3R|8|@]|D|@]|E|$]]|$1|1H|3|1I|5|6|7|3S|8|@]|D|@]|E|$]]|$1|1J|3|R|5|S|7|3T|8|@]|D|@$9|3U|A|3V|1|3W]]|E|$]]|$1|1K|3|U|5|6|7|3X|8|@]|D|@]|E|$]]|$1|1L|3|1M|5|6|7|3Y|8|@]|D|@]|E|$]]|$1|1N|3|1O|5|6|7|3Z|8|@$9|40|A|41|B|C]]|D|@]|E|$]]|$1|1P|3|1Q|5|6|7|42|8|@]|D|@$9|43|A|44|1|45]|$9|46|A|47|1|48]]|E|$]]|$1|1R|3|-4|5|6|7|49|8|@]|D|@]|E|$]]]|1S|$1T|$5|1U|1V|1W|E|$1X|1Y|1Z|-4]]|20|$5|1U|1V|1W|E|$1X|21|1Z|-4]]|22|$5|1U|1V|1W|E|$1X|23|1Z|-4]]|24|$5|1U|1V|1W|E|$1X|25|1Z|-4]]|26|$5|1U|1V|1W|E|$1X|27|1Z|-4]]|28|$5|1U|1V|1W|E|$1X|29|1Z|-4]]|2A|$5|2B|1V|2C|E|$2D|2E]]|2F|$5|2B|1V|2C|E|$2D|2G]]]]

The overall process is:
<ol>
<li>Load the data into a tool</li>
<li>Draw a shape. E.G. Using a polygon tool or other shape tools</li>
<li>Export the points and use them for training directly, or convert them into a dense pixel mask.</li>
</ol>
Annotation Process Detail
Generally speaking, vector based tools are preferred because they are more accurate.
For example on the left is 4 polygon points and on the right is a brush.
<a href="https://i.stack.imgur.com/3b0B8.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/3b0B8.png" alt="enter image description here" /></a>
Counter intuitively it can even be faster to trace out the points. For example here, I simply moved my mouse over the outline which records this:
<a href="https://i.stack.imgur.com/p29DR.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/p29DR.png" alt="enter image description here" /></a>
But it's actually stored as points:
<a href="https://i.stack.imgur.com/Oidwb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Oidwb.png" alt="enter image description here" /></a>
Other shapes are used
For example, you can use an ellipse or a circle to create segmentation masks.
There's no requirement to actually manually create a polygon.
<a href="https://i.stack.imgur.com/BfVFy.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/BfVFy.png" alt="enter image description here" /></a>
<a href="https://i.stack.imgur.com/tJCwr.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tJCwr.png" alt="enter image description here" /></a>
Autobordering
One of the main concepts behind wanting to get to 100% coverage of pixels is a concept called autobordering. Basically it just means that the edges of objects intersect well. In Diffgram for example you can select two points and it will create a matching border.
Here's an example:
<a href="https://web.archive.org/web/20210506132248/https://d9hhrg4mnvzow.cloudfront.net/get.diffgram.com/image-segmentation-software/semantic-segmentation-labelling-tool/polygon/segmentation/85f8efdb-unnamed.gif" rel="nofollow noreferrer"><img src="https://web.archive.org/web/20210506132248/https://d9hhrg4mnvzow.cloudfront.net/get.diffgram.com/image-segmentation-software/semantic-segmentation-labelling-tool/polygon/segmentation/85f8efdb-unnamed.gif" alt="enter image description here" /></a>
Finally there are many other concepts like pre-labeling, copying to next, tracking etc.
Tooling
Diffgram's <a href="https://get.diffgram.com/image-segmentation-software/semantic-segmentation-labelling-tool/polygon/segmentation/" rel="nofollow noreferrer">Segmentation Labeling Software</a> is a great tool option for Image and Video Annotation. It's open-core, you can run it on <a href="https://diffgram.com/secure" rel="nofollow noreferrer">your kubernetes cluster</a>.

blocks|key|111189|text|ITK是为医学图像分割而设计的。这是容易学习和强大的仍然。你可以试试。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|111190|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

ITK-SNAP is designed for medical image segmentation. It is easy to learn and powerful still. You can try it.

blocks|key|699995|text|您可以在数据集中使用平均和规范化技术，这样就可以创建基本事实，然后标记不同的结构。为此目的，您可以考虑创建一个所谓的“\##.确保您的所有数据最初是共同注册的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|699996|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

you can use averaging and normalization techniques all over your dataset so create your ground truth and then label different structures. for this purpose, you can think about creating a so called |template|. make sure all your data are co registered initially.

I'm trying to train a CNN model that perform image segmentation,
but I'm confused how to create the ground truth if I have several
image samples?

Image segmentation can classify each pixel in input image to 
a pre-defined class, such as cars, buildings, people, or any else.

Is there any tools or some good idea to create the ground
truth for image segmentation?

Thanks!

How to annotate the ground truth for image segmentation?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

教程

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云智能顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

EdgeOne AI 安全实战专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云AI代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

功能1上新10个字符

功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符功能2描述100个字符。

功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符功能2上新100个字符。

功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符功能5描述100个字符

功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符功能5上新100个字符

功能4上新

文章&问答评论现已支持表情

全新交互，全新视觉，新增快捷键、悬浮工具栏、高亮块等功能并同时优化现有功能，全面提升创作效率和体验

社区富文本编辑器全新改版！诚邀体验～ 

精选全网热门MCP server，让你的AI更好用 🚀

💥开发者 MCP广场重磅上线！

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

聚焦“写作效率、视觉美观与运行性能”三方面进行全面升级，为您提供更高效、稳定的创作环境

社区富文本&Markdown编辑器全新改版上线，欢迎大家体验!

诚挚邀请您参与本次调研，分享您的真实使用感受与建议。您的反馈至关重要，感谢您的支持与参与！

社区新版编辑器体验调研

我试着训练一个CNN模型来进行图像分割，但是如果我有几个图像样本，我很困惑如何创建真实的图像呢？图像分割可以将输入图像中的每个像素分类为预定义的类，如汽车、建筑物、人员或任何其他。有什么工具或什么好主意来创造真实的地面图像分割吗？谢谢!

问如何在图像分割中标注真实情况？
EN

回答 8

Stack Overflow用户

Stack Overflow用户

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何在图像分割中标注真实情况？EN

回答 8

Stack Overflow用户

Stack Overflow用户

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何在图像分割中标注真实情况？
EN