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PROJECT


Implementation of U-Net for Image Segmentation

In this project, we’ll delve into the task of image segmentation in computer vision. We’ll be introduced to the robust deep learning architecture U-Net and guided through the implementation of this state-of-the-art model using TensorFlow and Keras.

Implementation of U-Net for Image Segmentation

You will learn to:

Perform an image segmentation task.

Use TensorFlow/Keras in a deep learning project.

Create and understand U-Net architecture.

Implement the state-of-the-art U-Net model.

Skills

Deep Learning

Computer Vision

Image Segmentation

Prerequisites

Intermediate knowledge of Python programming

Intermediate knowledge of neural networks

Basic understanding of CNNs

Technologies

keras logo

Keras

Numpy

Python

Tensorflow

Matplotlib

Project Description

Image segmentation, a crucial task in computer vision, entails breaking down an image into distinct, meaningful segments. These segments represent different objects, boundaries, or areas of interest within the image. The primary aim of image segmentation is to simplify image representation, making it easier to analyze and extract valuable information.

In this project, we’ll unravel the intricacies of image segmentation tasks and explore the state-of-the-art U-Net deep learning architecture. We’ll take a quick look at various types of image segmentation tasks and dive deep into understanding U-Net’s architecture. We’ll also learn to implement this cutting-edge segmentation method from scratch, using the powerful combination of TensorFlow and Keras. The skills learned in this project can be seamlessly adapted for future deep learning projects.

Project Tasks

1

Getting Started

Task 0: Get Started

Task 1: Understand the Image Segmentation Task

Task 2: Set Up the Project

2

Working with the Dataset

Task 3: Understand the Dataset

Task 4: Load the Training Data

Task 5: Load the Validation Data

Task 6: Combine Images and Masks

Task 7: Visualize the Images and Masks

3

Data Preprocessing

Task 8: Preprocess the Data

Task 9: Perform Image Normalizing

Task 10: Apply Preprocessing

Task 11: Perform Dataset Preprocessing Optimizations

4

Understanding the U-NET Architecture and Building the Model

Task 12: Understand the U-Net Architecture

Task 13: Create the Double Convolution Block

Task 14: Create the Downsample Block

Task 15: Create the Upsample Block

Task 16: Build the U-Net Model

Task 17: Compile the Model

Task 18: Train the Model

5

Making Predictions

Task 19: Make Predictions

Task 20: Visualize the Predictions

Congratulations!