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annotationsneural-networkdeep-learningimage-segmentation

How to annotate the ground truth for image segmentation?


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!


Solution

  • 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.

    SmartTool usage example

    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.