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!
For semantic segmentation every pixel of an image should be labeled. There are three following ways to address the task:
Vector based - polygons, polylines
Pixel based - brush, eraser
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.