Complete Guide to Creating COCO Datasets Build your own image datasets automatically with Python

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h264, yuv420p, 1280x720 | English, aac, 48000 Hz, 2 channels, s16 | 3h 55mn | 2.9 GB
Instructor: Immersive Limit
Build your own image datasets automatically with Python

What you'll learn

How COCO annotations work and how to parse them with Python
How to go beyond the original 90 categories of the COCO dataset
How to automatically generate a huge synthetic COCO dataset with instance annotations
How to train a Mask R-CNN to detect your own custom object categories in real photos

Requirements

Take an Intro to Deep Learning course first (perhaps from another Udemy course!)
Have basic to intermediate Python programming skills
Have a physical or cloud computer with GPU/CUDA compute
Recommended: Prior experience with Anaconda & Jupyter notebooks

Description

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
Who this course is for:

Developers who have completed a Deep Learning course and want to solve real-world image recognition problems
Developers looking for a deep walkthrough of creating a COCO dataset and training a Mask R-CNN
Screenshots

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