***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 DeepLearning 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:
Use AI to annotate your dataset for Mask segmentation,
Annotation for one dataset can be used for other models (No need for any conversion) - Mask-RCNN, Yolo, SSD, FR-CNN, Inception etc,
Robust and Fast Annotation and Data Augmentation,
Supervisely handles duplicate images.
You can Train your AI Models Online (for free) from anywhere in the world, once you've set up your Deep Learning Cluster.
So as you can see, that the features mentioned above can save you a tremendous amount of time. In this course, I show you how to use this workflow by training your own custom Mask RCNN as well as how to deploy your models using PyTorch. So essentially, we've structured this training to reduce debugging, speed up your time to market and get you results sooner.
In this course, here's some of the things that you will learn:
Learn the State of the Art in Object Detection using Mask R-CNN pre-trained model,
Discover the Object Segmentation Workflow that saves you time and money,
The quickest way to gather images and annotate your dataset while avoiding duplicates,
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 for Road Pothole Detection, Segmentation & Pixel Analysis,
Step-by-step instructions on how to Execute,Collect Images, Annotate, Train and Deploy Custom Mask R-CNN models,
and much more...
You also get helpful bonuses:
Personal help within the course
We donate my time to regularly hold office hours with students. During the office hours you can ask me any business question you want, and wewill do my best to help you. Students can start discussions and message us with private questions. Weregularly update this course to reflect the current marketing landscape.
Geta Career Boost with a CertificateofCompletion
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.
If you want to get a marketing job or freelancing clients, a certificate from this course can help you appear as a stronger candidate for Artificial Intelligence jobs.
Money-Back Guarantee
The course comes with an unconditional, Udemy-backed, 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'vedone 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.
