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

***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.

Amongst the possible solutions we arrived at using Supervisely which is free Object Segmentation Workflow Tool, that can help you: