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.
Amongst the possible solutions we arrived at using Supervisely which is free Object Detection Workflow Tool, that can help you with the following:
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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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.
The course comes with an unconditional, 30-day money-back guarantee. This is not just a guarantee, it's my personal promise to you that I will go out of my way to help you succeed just like I've done for thousands of my other students.
Let me help you get fast results. Enroll now, by clicking the button and let us show you how to Develop Object Segmentation Using YOLO V3.
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.
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.
StartLecture 5 - 4 Steps to Setting up a Supervisely Deep Learning Cluster (9:32)
StartLecture 6 - How to Web Scrape Images for your Dataset like a PRO! (6:21)
StartLecture 7 - The Best Way to Annotate your Dataset (4:22)
StartLecture 8 - How to let the AI Annotate your Dataset for you - Human in the Loop Annotation (2:49)
StartLecture 9 - Got Little Data? No Problem! Data Augmentation to the Rescue ;) (4:28)
StartLecture 10 - How to Train a Yolo V3 Network (5:04)
StartLecture 11 - A Quick and Easy Method Deploying your Custom Object Detector after Training (6:37)
Get started now!