Learn how we implemented Mask R-CNN Deep Learning Object Segmentation 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 Segmentation, 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 Segmentation 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 Mask RCNN as well as how to deploy your models using Keras. So essentially, we’ve structured this training to reduce debugging, speed up your time to market and get you results sooner. We have partnered up with Geeky Bee AI to bring the State-of-the-Art in AI.

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

Bonuses:

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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

 

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 Mask R-CNN.

***Important Notes***

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

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

Course Curriculum

Mask R-CNN – Robust Deep Learning Segmentation in 1 hour
Section 1: Introduction

Lecture 1: Introduction to Mask R-CNN (2:38)

How to Join the Private FB Group

Lecture 2: How to take this course. (4:27)

Lecture 3: Mask R-CNN Intuition (10:07)

Section 2: Setup of Mask RCNN

Lecture 4: Anaconda Install and Setup for Mask RCNN (1:57)

Lecture 5: Installing the requirements, dependencies (10:56)

Section 3: Mask RCNN

Lecture 6: Real-time Mask RCNN – How to execute like a boss. (5:32)

Section 4: Training Mask RCNN

Lecture 7: Set up Supervisely Cluster (9:32)

Lecture 9: Annotating Images (8:10)

Lecture 10: Data Augmentation (4:38)

Lecture 11: How to Train a Mask RCNN model (5:50)

Section 5: Deploying Mask-RCNN

Lecture 12: How to Deploy a Custom Mask RCNN after Training (4:27)

Lecture 13: Segmentation Area Analysis – How to Count Potholes and its Area Size (2:55)

Section 6: Conclusion to the Course

Lecture 15: Artificial Neural Networks Intuition – [Bonus] (18:30)

Lecture 16: Convolutional Neural Networks Intuition – [BONUS] (11:17)