Complete Guide to Creating COCO Datasets by Adam Kelly Immersive Limit

Udemy course Complete Guide to Creating COCO Datasets by Adam Kelly Immersive Limit

Complete Guide to Creating COCO Datasets is the best Udemy course on the market. With this offer they will be able to greatly improve their knowledge and become more competitive within the Development category. Therefore, if you are looking to improve your Development skills we recommend that you download Complete Guide to Creating COCO Datasets udemy course.

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Course data:

  • Author: Adam Kelly Immersive Limit
  • Course rating: 4.5
  • Category: Development
  • Modality: Online
  • Status: Available
  • Idiom: English

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Abouth Adam Kelly Immersive Limit

Hey! I’m Adam Kelly. As I see it, artificial intelligence, 3d simulations, virtual reality, and augmented reality are going to change the world in ways we can only imagine. I’m committed to learning all about them by doing practical projects that solve real world problems and sharing what I learn along the way. I have a degree in Computer Science from the University of Michigan and almost a decade of professional development experience, including as a Software Engineer at Microsoft and General Motors. Let’s learn together! 🙂

Complete Guide to Creating COCO Datasets

What the udemy Complete Guide to Creating COCO Datasets course teaches?

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

Build your own image datasets automatically with Python

More information about the course Complete Guide to Creating COCO Datasets

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

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