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The Complete Guide to Creating your own AI Object Detection Apps.

Learn how we implemented YOLO V3 Deep Learning Object Detection Models From Training to Inference – Step-by-Step

When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is that if you are just getting started learning about AI Object Detection, you may encounter some of the following common obstacles along the way:

Labeling dataset is quite tedious and cumbersome,
Annotation formats between various object detection models are quite different.
Labels may get corrupt with free annotation tools,
Unclear instructions on how to train models – causes a lot of wasted time during trial and error.
Duplicate images are a headache to manage.

This got us searching for a better way to manage the object detection workflow, that will not only help us better manage the object detection process but will also improve our time to market.

Course Information

Amongst the possible solutions we arrived at using Supervisely which is free Object Detection Workflow Tool, that can help you with the following:

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So as you can see, that the features mentioned above can save you a tremendous amount of time. In this course, we show you how to use this workflow by training your own custom YOLO V3 as well as how to deploy your models using PyTorch. So essentially, we’ve structured this training to reduce debugging, speed up your time to market and get you results sooner.

In this course, here’s some of the things that you will learn:

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Bonuses:

Get a Career Boost with a Certificate of Completion

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Upon completing 100% of this course, you will be emailed a certificate of completion. You can show it as proof of your expertise and that you have completed a certain number of hours of instruction.

Money-Back Guarantee

***Important Notes***

This is a practical-focused course. While we do provide an overview of YOLO V3 theory, we focus mostly on helping you getting YOLO V3 working step-by-step.

The Course on Udemy will be shutting down and will only be available on this platform.

Your Instructor

Ritesh Kanjee

Ritesh Kanjee (Masters Degree in Electronic Engineering) is the CEO and founder of Augmented Startups, with over 42’000+ students on Augmented AI Bootcamp, and over 50’000 subscribers on YouTube. He teaches the latest topics on Artificial Intelligence and Augmented Reality.

Course Curriculum

YOLO v3 – Robust Deep Learning Object Detection in 1 hour
Section 1 – Introduction to Yolo V3 Object Detection

    Lectrure 1 – Introduction (1:28)

    How to Join the Private FB Group

    Lecture 2 – How to take this course

Section 2 – The Quickest Way to get YoloV3 up and Running!

    Lecture 3 – Yolo v3 Intuition (12:10)

    Lecture 4 – Execute Yolo V3 (8:50)

Section 3 – How to Train Yolo V3 – Training & Workflow

    Lecture 5 – 4 Steps to Setting up a Supervisely Deep Learning Cluster (9:32)

    Lecture 6 – How to Web Scrape Images for your Dataset like a PRO! (6:21)

    Lecture 7 – The Best Way to Annotate your Dataset (4:22)

    Lecture 8 – How to let the AI Annotate your Dataset for you – Human in the Loop Annotation (2:49)

    Lecture 9 – Got Little Data? No Problem! Data Augmentation to the Rescue ? (4:28)

    Lecture 10 – How to Train a Yolo V3 Network (5:04)

    Lecture 11 – A Quick and Easy Method Deploying your Custom Object Detector after Training (6:37)

Section 4 – Post-Processing and Maintenance

    Lecture 12 – How to Record video, change bounding box color and add confidence percentage (5:32)

    Lecture 13 – Cleaning up you Supervisely Cluster and Cluster Maintenance (1:37)

Section 5 – Bonus Lecture

    Artificial Neural Networks (18:30)

    Convolutional Neural Networks (11:17)

Access download Ritesh Kanjee – YOLO v3 – Robust Deep Learning Object Detection in 1 hour at Forimc.com right now!

Salepage: https://augmentedstartups.teachable.com/p/yolo-v3-robust-deep-learning-object-detection-in-1-hour

Archive: https://archive.ph/wip/LlHWH

Delivery Method

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