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.
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:
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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.
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.
Ritesh Kanjee (Masters Degree in Electronic Engineering) is the CEO and founder of Augmented Startups, with over 40'000+ students, and over 44'000 subscribers on YouTube. He teaches the latest topics on Artificial Intelligence and Augmented Reality.