YOLO v3 – Robust Deep Learning Object Detection in 1 hour

YoloV3
02
Dec
$200.00 $19.00

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:

  • Use AI to annotate your dataset,
  • Annotation for one dataset can be used for other models (No need for any conversion) – Yolo, SSD, FR-CNN, Inception etc,
  • Robust and Fast Annotation and Data Augmentation,
  • Supervisely handles duplicate images.
  • You can Train your AI Models Online (for free) from anywhere in the world, once you’ve set up your Deep Learning Cluster.

So as you can see, that the features mentioned above can save you a tremendous amount of time. In this course, I show you how to use this workflow by training your own custom YoloV3 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:

  • Learn the State of the Art in Object Detection using Yolo V3 pre-trained model,
  • Discover the Object Detection Workflow that saves you time and money,
  • The quickest way to gather images and annotate your dataset while avoiding duplicates,
  • Secret tip to multiply your data using Data Augmentation,
  • How to use AI to label your dataset for you,
  • Find out how to train your own custom YoloV3 from scratch,
  • Step-by-step instructions on how to Execute,Collect Images, Annotate, Train and Deploy Custom Yolo V3 models,
  • and much more…

You also get helpful bonuses:

  • Neural Network Fundamentals

Personal help within the course

I donate my time to regularly hold office hours with students. During the office hours you can ask me any business question you want, and I will do my best to help you. The office hours are free. I don’t try to sell anything.

Students can start discussions and message me with private questions. I answer 99% of questions within 24 hours. I love helping students who take my courses and I look forward to helping you.

I regularly update this course to reflect the current marketing landscape.

Get a Career Boost with a Certificate of Completion  

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.

If you want to get a marketing job or freelancing clients, a certificate from this course can help you appear as a stronger candidate for Artificial Intelligence jobs.

Money-Back Guarantee

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 Detection Using Yolo V3.

Course Content

Time: 1 hour

Curriculum is empty

Instructor

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Ritesh Kanjee has over 7 years in Printed Circuit Board (PCB) design as well in image processing and embedded control. He completed his Masters Degree in Electronic engineering and published a paper for IEEE called Vision-based adaptive Cruise control using Pattern matching (on Google Scholar). His work was implemented in LabVIEW. He works as an Embedded Electronic Engineer in defence research. He has experience in FPGA design with programming in both VHDL and Verilog.

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$200.00 $19.00