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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.
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
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!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.