Vote For Us !
Topliste | volno.org - Topliste | LinkR.top - Dein Linkverzeichnis für den Underground! | | | Cyonix! | |


Latest Topics: --- The Croods A New Age (2020) 2160p UHD BluRay x265-B0MBARDiERS --- vMix Pro 23.0.0.68 (x64) Multilingual --- PDF Shaper Professional / Premium 10.8 Multilingual --- Data Engineering - ETL, Web Scraping ,Big Data,SQL,Power BI --- Remove unprotect Excel VBA & Sheet password using VBA tool --- AquaSoft SlideShow Premium 12.2.01 (x64) Multilingual --- AquaSoft Stages 12.2.01 (x64) Multilingual --- AquaSoft SlideShow Ultimate 12.2.01 (x64) Multilingual --- Implement JIT and JEA Administration in Windows Server 2019 --- Chinese Characters You Must Know for HSK 3-4 Volume 20 ---

Categories
Applications & Games
Ebooks ,Tutorials & Scripts
Music & Music Video
Movies & Documentaries
TV Series , Anime and Sport

Friends
WarezForums.com
DirtyWarez.com
Best of Links!
8ebooks.net

Latest Threads
The Croods A New Age (2020) 2160p UHD BluRay x265-B0MBARDiERS
Last Post: NoFearFCP
Today 03:09 PM
» Replies: 0
» Views: 12
vMix Pro 23.0.0.68 (x64) Multilingual
Last Post: kalpatru
Today 02:34 PM
» Replies: 0
» Views: 13
PDF Shaper Professional / Premium 10.8 Multilingual
Last Post: kalpatru
Today 01:54 PM
» Replies: 0
» Views: 16
Data Engineering - ETL, Web Scraping ,Big Data,SQL,Power BI
Last Post: salah21
Today 01:49 PM
» Replies: 0
» Views: 18
Remove unprotect Excel VBA & Sheet password using VBA tool
Last Post: salah21
Today 01:46 PM
» Replies: 0
» Views: 16
AquaSoft SlideShow Premium 12.2.01 (x64) Multilingual
Last Post: kalpatru
Today 01:46 PM
» Replies: 0
» Views: 13
AquaSoft Stages 12.2.01 (x64) Multilingual
Last Post: kalpatru
Today 01:44 PM
» Replies: 0
» Views: 17
AquaSoft SlideShow Ultimate 12.2.01 (x64) Multilingual
Last Post: kalpatru
Today 01:44 PM
» Replies: 0
» Views: 11
Implement JIT and JEA Administration in Windows Server 2019
Last Post: salah21
Today 01:43 PM
» Replies: 0
» Views: 12
Chinese Characters You Must Know for HSK 3-4 Volume 20
Last Post: salah21
Today 01:39 PM
» Replies: 0
» Views: 17

Mask R-CNN - Practical Deep Learning Segmentation in 1 hour
#1
[Image: 2102201751290093.jpg]

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

MP4 | h264, 1280x720 | Lang: English | Audio: aac, 48000 Hz | 2h 10m | 2.84 GB
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.

Requirements
Background in OpenCV & Computer Vision
Have prior experience in Python using Anaconda.
A PC/Laptop with CUDA-enabled Nvidia graphics Card for training - We use Ubuntu for training.
Create a Free Account with Supervisely.
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.

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:

Neural Network Fundamentals

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 we will do my best to help you. Students can start discussions and message us with private questions. We regularly update this course to reflect the current marketing landscape.

Get a Career Boost with a Certificate of Completion

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've done 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.

Who this course is for:
Students who want to learn how to take object detection further with Mask RCNN
Students who are curious to learn practical approach to Instance segmentation
This course is for students with Python, OpenCV or AI experience who want to learn how to do Object Segmentation with Mask RCNN


Code:
https://rapidgator.net/file/0d4e1be1b665f33c2a7efcebe6be15dc/Mask_R-CNN_-_Practical_Deep_Learning_Segmentation_in_1_hour.part1.rar.html
https://rapidgator.net/file/69e9fde6df34ee04c0e82a48e5e3b7b2/Mask_R-CNN_-_Practical_Deep_Learning_Segmentation_in_1_hour.part2.rar.html
https://rapidgator.net/file/3ceaefd13718818a71f3d7ea55d90508/Mask_R-CNN_-_Practical_Deep_Learning_Segmentation_in_1_hour.part3.rar.html

https://dropapk.to/aeozjr2n5avi
https://dropapk.to/ay2v1vo1vz24
https://dropapk.to/55i8lf3w5h9m
Reply

Create an account or sign in to comment
You need to be a member in order to leave a comment
Create an account
Sign up for a new account in our community. It's easy!
or
Sign in
Already have an account? Sign in here.


crawli download suchmaschine
WarezOmen

LEGAL NOTICE
This is an indexing website for external links only. No file(s)/image(s) is/are uploaded on the website server. All take-down requests should be directed to the external websites hosting the file(s)/image(s).