Deep Learning-Based Image Segmentation for Computer Vision with Keras and TensorFlow in Google Colab Platform : Hands-on
What you will learn
Understand what multi-class image segmentation is in computer vision
Understand the fundamentals of DeepLabv3+ (CNN)
Build and train a the multi-class image segmentation model using Keras with Tensorflow as a backend using Google Colab
Learn to use the trained model to predict the segmented mask of a new set of image data
Description
Welcome to the “Multi-Class Semantic Image Segmentation with Keras in Python” course. In this project, you will learn to build a multi-class image segmentation deep-learning model in Keras with a TensorFlow backend from scratch. You will learn to train the model using the image dataset and perform multi-class image segmentation. By the end of this course, you could build and train the deep learning multi-class image segmentation model. After that, you will also be able to use the trained model to predict segmented masks on new images and visualise them. Please note that you don’t need a high-powered workstation to learn this exciting course. We will carry out the entire project in the Google Colab environment and Google Drive, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. The multi-class image segmentation course applies to many industries, especially the autonomous industry, healthcare, aerial imagery, geo-sensing, precision agriculture, etc. You can add this project to your portfolio, which is essential for your following job interview. This course is designed most straightforwardly to utilise your time wisely.
Happy learning.
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