如何注释图像分割的基本事实? [英] How to annotate the ground truth for image segmentation?

问题描述

我正在尝试训练可进行图像分割的CNN模型， 但是如果我有几个人，我很困惑如何创建基本事实 图片样本?

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?

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，例如汽车，建筑物，人或其他任何人.

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

是否有任何工具或一些好主意来奠定基础 图像分割的真相吗?

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

谢谢！

推荐答案

尝试 https://www.labelbox. io/.这是他们的图像分割模板的样子……

Try out https://www.labelbox.io/. Here is what their image segmentation template looks like...

很多代码是开源，并且它们有一个托管服务来管理标签的结尾.结束.

A lot of the code is open source and they have a hosted service to manage labeling end to end.

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