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Purchase this course you will earn 25 Points worth of $2.50The Complete Guide to Creating your own AI Semantic Segmentation Apps
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.
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***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)
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