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Deep learning for image segmentation using Tensorflow 2

Video Introducing this tutorial

Introduction and course content :
Course outline
Code for this course

Image segmentation in computer vision :
What is image segmentation?
What is image segmentation?
Why deep learning for image segmentation?
Why are so many researchers using deep learning to solve segmentation tasks?
Types of image segmentation
What is the difference between instance segmentation and semantic segmentation?

Instance segmentation using Mask RCNN :
Quick introduction to Mask RCNN
Is Mask RCNN an instance segmentation model or a semantic segmentation model?
Mask RCNN as an extension of Faster RCNN
High level overview of Faster RCNN (optional)
Can we use Faster RCNN model to perform image segmentation tasks?
High level overview of Mask RCNN
What are the similarities between Mask RCNN and Faster RCNN models?
Steps to build an image segmentation model with Mask RCNN for a custom dataset

Software setup :
Brief intro to Tensorflow 2 object detection API
Linux installation : How to install tensorflow 2 with GPU support (part 1)
Linux installation : How to install tensorflow 2 with GPU support (part 2)
Linux installation : How to install tensorflow 2 object detection API
Did you face any difficulties when installing tensorflow 2 object detection api?
Windows installation : Installing miniconda
Windows installation : Create virtual environment
Windows installation : Installing tensorflow 2 object detection API
Windows installation : Installing tensorflow with GPU support
Did you face any difficulties when installing tensorflow 2 object detection api?

Custom data preparation :
Choosing a dataset
Linux - Exploring the dataset - Part 1
Linux - Exploring the dataset - Part 2
Windows - Exploring the dataset
Annotating a custom dataset
What does it mean to annotate a dataset for image segmentation?
From multiple annotation files to one annotation file
Transforming our dataset to tfrecord format
For which task are we annotating our dataset?

Train Mask RCNN model on your local machine :
Training on premise VS training on the cloud
Downloading Mask RCNN pretrained model
Finding the right configuration file
Exploring the configuration file
Modifying the configuration file - Part 1
Modifying the configuration file - Part 2
Running the training locally
Were you able to run the training on your local machine?

Train and evaluate Mask RCNN model using google AI platform :
What is cloud computing and what is AI Platform? (optional)
Creating a Google Cloud account
Downloading Google Cloud SDK
Did you face any difficulties when installing google cloud SDK?
Setting up a new project on google cloud platform
Creating a google bucket and uploading data to it
Preparing our config file for training on google cloud
What's the main difference between the two configuration files?
Checking connection to google cloud from within our local machine
Exploring the training command
Running the training for Mask RCNN model
Did you face any troubles when using the previous command to run your training?
Checking the progress of the training job on google ai platform
Running the evaluation for Mask RCNN model during the training
Analyzing the results after the training of Mask RCNN model is finished
What is tensorboard used for?
Analyzing the results of the second training
Further explanation of when to run your evaluation jobs
Should you run your evaluation job during or after the training is done?
Downloading the trained model and exporting the SavedModel from checkpoints
Running the exported model on new examples locally
What is the naming convention for exported (production) models in Tensorflow 2?

Conclusion :
Conclusion and next steps for you
Can you tell me what you liked the most about this course?
What is something that you think could be improved about this course?
What topic would you like me to cover in my next course