How to annotate the ground truth for image segmentation?

Question

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!

Answer 1

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.

Answer 2

One tool that pops to mind is MIT's LabelMe toolbox: 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 this github repository for COCO UI you might find useful.