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

问题描述

我正在尝试训练一个执行图像分割的 CNN 模型，但如果我有几个，我很困惑如何创建基本事实图像样本?

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别，例如汽车、建筑物、人或任何其他类别.

是否有任何工具或一些好主意来创建地面图像分割的真相?

谢谢！

解决方案

对于语义分割，应标记图像的每个像素.有以下三种方式来解决这个任务:

1. 基于矢量 - 多边形、折线

2. 基于像素 - 画笔、橡皮擦

3. 人工智能驱动的工具

在

以下是比较多边形工具与 AI 驱动工具的两个视频:汽车分割和食物细分.

更多关于 Supervisely 注释特性的细节可以在 此处.

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!

解决方案

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

1. Vector based - polygons, polylines

2. Pixel based - brush, eraser

3. AI-powered tools

In Supervisely, tools to perform 1,2,3 are available.

Below are two videos that compare polygon vs AI-powered tools: cars segmentation and food segmentation.

More details about annotation features of Supervisely can be found here.

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