Deep learning for image segmentation using Tensorflow 2

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Deep learning for image segmentation using Tensorflow 2 [TutsNode.com] - Deep learning for image segmentation using Tensorflow 2 07 Train and evaluate Mask RCNN model using google AI platform
  • 016 Running the exported model on new examples locally.mp4 (227.3 MB)
  • 016 Running the exported model on new examples locally.en.srt (26.0 KB)
  • 013 Analyzing the results of the second training.en.srt (10.0 KB)
  • 012 Analyzing the results after the training of Mask RCNN model is finished.en.srt (9.8 KB)
  • 015 Downloading the trained model and exporting the SavedModel from checkpoints.en.srt (9.5 KB)
  • 011 Running the evaluation for Mask RCNN model during the training.en.srt (7.7 KB)
  • 005 Creating a google bucket and uploading data to it.en.srt (7.5 KB)
  • 008 Exploring the training command.en.srt (6.8 KB)
  • 003 Downloading Google Cloud SDK.en.srt (6.1 KB)
  • 002 Creating a Google Cloud account.en.srt (6.0 KB)
  • 001 What is cloud computing and what is AI Platform_ (optional).en.srt (5.9 KB)
  • 006 Preparing our config file for training on google cloud.en.srt (5.7 KB)
  • 009 Running the training for Mask RCNN model.en.srt (4.2 KB)
  • 010 Checking the progress of the training job on google ai platform.en.srt (3.9 KB)
  • 007 Checking connection to google cloud from within our local machine.en.srt (3.8 KB)
  • 004 Setting up a new project on google cloud platform.en.srt (3.1 KB)
  • 014 Further explanation of when to run your evaluation jobs.en.srt (2.6 KB)
  • external-assets-links.txt (0.2 KB)
  • 001 What is cloud computing and what is AI Platform_ (optional).mp4 (115.9 MB)
  • 012 Analyzing the results after the training of Mask RCNN model is finished.mp4 (87.4 MB)
  • 013 Analyzing the results of the second training.mp4 (83.9 MB)
  • 015 Downloading the trained model and exporting the SavedModel from checkpoints.mp4 (73.9 MB)
  • 006 Preparing our config file for training on google cloud.mp4 (50.7 MB)
  • 003 Downloading Google Cloud SDK.mp4 (50.6 MB)
  • 011 Running the evaluation for Mask RCNN model during the training.mp4 (46.4 MB)
  • 005 Creating a google bucket and uploading data to it.mp4 (40.9 MB)
  • 008 Exploring the training command.mp4 (36.9 MB)
  • 002 Creating a Google Cloud account.mp4 (36.3 MB)
  • 010 Checking the progress of the training job on google ai platform.mp4 (28.9 MB)
  • 009 Running the training for Mask RCNN model.mp4 (27.9 MB)
  • 007 Checking connection to google cloud from within our local machine.mp4 (21.0 MB)
  • 004 Setting up a new project on google cloud platform.mp4 (18.4 MB)
  • 014 Further explanation of when to run your evaluation jobs.mp4 (14.8 MB)
04 Software setup
  • 008 Windows installation _ Installing tensorflow with GPU support.en.srt (25.3 KB)
  • 007 Windows installation _ Installing tensorflow 2 object detection API.en.srt (15.6 KB)
  • 003 Linux installation _ How to install tensorflow 2 with GPU support (part 2).en.srt (13.2 KB)
  • 002 Linux installation _ How to install tensorflow 2 with GPU support (part 1).en.srt (10.6 KB)
  • 006 Windows installation _ Create virtual environment.en.srt (2.2 KB)
  • 001 Brief intro to Tensorflow 2 object detection API.en.srt (1.9 KB)
  • 004 Linux installation _ How to install tensorflow 2 object detection API.en.srt (7.4 KB)
  • 005 Windows installation _ Installing miniconda.en.srt (2.6 KB)
  • 008 Windows installation _ Installing tensorflow with GPU support.mp4 (205.3 MB)
  • 007 Windows installation _ Installing tensorflow 2 object detection API.mp4 (107.3 MB)
  • 003 Linux installation _ How to install tensorflow 2 with GPU support (part 2).mp4 (102.7 MB)
  • 002 Linux installation _ How to install tensorflow 2 with GPU support (part 1).mp4 (83.7 MB)
  • 004 Linux installation _ How to install tensorflow 2 object detection API.mp4 (54.4 MB)
  • 005 Windows installation _ Installing miniconda.mp4 (15.0 MB)
  • 006 Windows installation _ Create virtual environment.mp4 (11.4 MB)
  • 001 Brief intro to Tensorflow 2 object detection API.mp4 (7.9 MB)
01 Introduction and course content
  • 002 Code for this course.en.srt (1.0 KB)
  • 001 Course outline.en.srt (4.9 KB)
  • 001 Course outline.mp4 (22.9 MB)
  • 002 Code for this course.mp4 (6.0 MB)
05 Custom data preparation
  • 005 Annotating a custom dataset.en.srt (19.0 KB)
  • 005 Annotating a custom dataset.mp4 (218.5 MB)
  • 006 From multiple annotation files to one annotation file.en.srt (8.2 KB)
  • 007 Transforming our dataset to tfrecord format.en.srt (8.1 KB)
  • 003 Linux - Exploring the dataset - Part 2.en.srt (6.4 KB)
  • 004 Windows - Exploring the dataset.en.srt (6.2 KB)
  • 001 Choosing a dataset.en.srt (0.7 KB)
  • 002 Linux - Exploring the dataset - Part 1.en.srt (4.7 KB)
  • external-assets-links.txt (0.2 KB)
  • 007 Transforming our dataset to tfrecord format.mp4 (56.0 MB)
  • 006 From multiple annotation files to one annotation file.mp4 (55.6 MB)
  • 003 Linux - Exploring the dataset - Part 2.mp4 (44.3 MB)
  • 004 Windows - Exploring the dataset.mp4 (39.2 MB)
  • 002 Linux - Exploring the dataset - Part 1.mp4 (38.7 MB)
  • 019 damaged_cars_dataset.zip (14.4 MB)
  • 001 Choosing a dataset.mp4 (5.5 MB)
06 Train Mask RCNN model on your local machine
  • 005 Modifying the configuration file - Part 1.en.srt (14.5 KB)
  • 007 Running the training locally.en.srt (9.6 KB)
  • 004 Exploring the configuration file.en.srt (5.2 KB)
  • 030 mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.config (3.9 KB)
  • 002 Downloading Mask RCNN pretrained model.en.srt (3.5 KB)
  • 006 Modifying the configuration file - Part 2.en.srt (3.4 KB)
  • 001 Training on premise VS training on the cloud.en.srt (1.8 KB)
  • 003 Finding the right configuration file.en.srt (1.7 KB)
  • external-assets-links.txt (0.3 KB)
  • 005 Modifying the configuration file - Part 1.mp4 (84.8 MB)
  • 007 Running the training locally.mp4 (58.9 MB)
  • 004 Exploring the configuration file.mp4 (29.8 MB)
  • 002 Downloading Mask RCNN pretrained model.mp4 (24.2 MB)
  • 006 Modifying the configuration file - Part 2.mp4 (21.5 MB)
  • 003 Finding the right configuration file.mp4 (13.2 MB)
  • 001 Training on premise VS training on the cloud.mp4 (5.2 MB)
02 Image segmentation in computer vision
  • 002 Why deep learning for image segmentation_.en.srt (1.2 KB)

Description


Description

This course is about using deep learning to perform image segmentation with Tensorflow 2. It will show you a step by step guide on how to build powerful deep learning driven image segmentation tasks in computer vision.

The course will show you how to use Mask RCNN deep learning model in order to perform image segmentation. Mask RCNN is one of the widely used neural networks for image segmentation tasks.

The course will help you answer these questions:

1/ What is image segmentation?

2/ What are the different types of segmentation in computer vision?

3/ How do you prepare a custom dataset for training Mask RCNN model?

4/ What tools are used for annotating a dataset for image segmentation?

5/ How do you transform your images and annotations to tfrecords format?

6/ How do you use Tensorflow 2 object detection API for training Mask RCNN model?

7/ How do you use Tensorflow 2 object detection API for evaluating Mask RCNN model?

8/ How to run the training of Mask RCNN model on your local machine?

9/ How to create an account on google cloud platform (GCP)

10/ How to setup a project on google cloud platform (GCP)

11/ How to run the training of Mask RCNN model on google ai platform?

12/ How do you export a SavedModel from your training checkpoints?

13/ How do you use your SavedModel to perform image segmentation on new images?

14/ How do you use Mask RCNN to build a powerful image segmentation model for segmenting different parts of a damaged car (door, hood, lamps, …). Which is by the way the course project!

And a lot more!

My strategy with this course is to enable you to build powerful AI solutions for image segmentation in computer vision.
Who this course is for:

Students
DIY makers
AI Hobbyists
Machine learning enthusiats
Machine learning engineers
Computer vision enthusiasts
Computer vision engineers
Data scientists

Requirements

Basic understanding of Python (you should know what functions are and how to use them in Python)
Basic understanding of deep learning (you should know what a neural network is and what training is)

Last Updated 5/2021



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Deep learning for image segmentation using Tensorflow 2


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2.4 GB
seeders:16
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Deep learning for image segmentation using Tensorflow 2


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