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Mask R-CNN - Practical Deep Learning Segmentation in 1 hour

 Mask R-CNN - Practical Deep Learning Segmentation in 1 hour
Udemy Course Mask R-CNN - Practical Deep Learning Segmentation in 1 hour | NED
Mask R-CNN – Practical Deep Learning Segmentation in 1 hour free download also includes 6 hours on-demand video, 5 articles, 80 downloadable resources, Full lifetime access, Access on mobile and TV, Assignments, Certificate
by Augmented Startups, Geeky Bee AI Private Limited

What you'll learn
  • What is Instance Segmentation
  • How to take object segmentation further using Mask RCNN
  • 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 Mask R-CNN from scratch.
  • Pothole Detection using Mask R-CNN
  • Step-by-step instructions on how to Execute, Annotate, Train and Deploy Custom Mask R-CNN models.
 Description

***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 get Mask R-CNN working step-by-step.

Learn how we implemented Mask R-CNN 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 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.

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