In this course, you'll learn how to create your own COCO dataset with images containing custom object categories. You'll learn how to use the GIMP image editor and Python code to automatically generate thousands of realistic, synthetic images with minimal manual effort. I'll walk you through all of the code, which is available on GitHub, so that you can understand it at a fundamental level and modify it for your own needs.

(Important: If you only want to do manual image annotation, this course is not for you. Google "coco annotator" for a great tool you can use. This course teaches how to generate datasets automatically.)

By the end of this course, you will:

• Have a full understanding of how COCO datasets work

• Know how to use GIMP to create the components that go into a synthetic image dataset

• Understand how to use code to generate COCO Instances Annotations in JSON format

• Create your own custom training dataset with thousands of images, automatically

• Train a Mask R-CNN to spot and mark the exact pixels of custom object categories

• Be able to apply this knowledge to real world problems

I've saved weeks of my precious time using this method because I'm not doing the tedious task of manual image labeling, which can easily take a full 40 hour work week to create 1000 images. You should value your time too. After all, how are you going to solve the world's problems if you're busy clicking outlines on images for the next couple weeks?

Soundtrack by Silk Music
Track name: Shingo Nakamura - Hakodate