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Deep learning for image segmentation using Tensorflow 2
Introduction and course content
Course outline (4:32)
Code for this course (0:46)
Image segmentation in computer vision
What is image segmentation? (4:08)
Why deep learning for image segmentation? (0:57)
Types of image segmentation (3:17)
Instance segmentation using Mask RCNN
Quick introduction to Mask RCNN (2:21)
Mask RCNN as an extension of Faster RCNN (2:37)
High level overview of Faster RCNN (optional) (3:21)
High level overview of Mask RCNN (1:36)
Steps to build an image segmentation model with Mask RCNN for a custom dataset (2:23)
Software setup
Brief intro to Tensorflow 2 object detection API (1:45)
Linux installation : How to install tensorflow 2 with GPU support (part 1) (9:09)
Linux installation : How to install tensorflow 2 with GPU support (part 2) (11:14)
Linux installation : How to install tensorflow 2 object detection API (7:07)
Windows installation : Installing miniconda (2:15)
Windows installation : Create virtual environment (1:58)
Windows installation : Installing tensorflow 2 object detection API (13:23)
Windows installation : Installing tensorflow with GPU support (23:01)
Custom data preparation
Choosing a dataset (0:36)
Linux - Exploring the dataset - Part 1 (4:24)
Linux - Exploring the dataset - Part 2 (5:33)
Windows - Exploring the dataset (5:14)
Annotating a custom dataset (17:04)
From multiple annotation files to one annotation file (7:14)
Transforming our dataset to tfrecord format (7:09)
Train Mask RCNN model on your local machine
Training on premise VS training on the cloud (1:42)
Downloading Mask RCNN pretrained model (3:15)
Finding the right configuration file (1:31)
Exploring the configuration file (4:23)
Modifying the configuration file - Part 1 (12:35)
Modifying the configuration file - Part 2 (3:23)
Running the training locally (8:34)
Train and evaluate Mask RCNN model using google AI platform
What is cloud computing and what is AI-Platform? (optional) (5:48)
Creating a Google Cloud account (5:28)
Downloading Google Cloud SDK (5:50)
Setting up a new project on google cloud platform (2:43)
Creating a google bucket and uploading data to it (6:20)
Preparing our config file for training on google cloud (5:42)
Checking connection to google cloud from within our local machine (3:13)
Exploring the training command (5:36)
Running the training for Mask RCNN model (3:35)
Checking the progress of the training job on google ai platform (3:24)
Running the evaluation for Mask RCNN model during the training (6:32)
Analyzing the results after the training of Mask RCNN model is finished (8:24)
Analyzing the results of the second training (8:34)
Further explanation of when to run your evaluation jobs (2:14)
Downloading the trained model and exporting the SavedModel from checkpoints (8:21)
Running the exported model on new examples locally (22:06)
Summary
Conclusion (1:41)
Teach online with
Checking the progress of the training job on google ai platform
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