Packt
Mastering Image Segmentation with PyTorch
Packt

Mastering Image Segmentation with PyTorch

Packt

Instructor: Packt

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply multi-class semantic segmentation using PyTorch to real-world datasets.

  • Analyze the architecture and functionality of UNet and FPN models for effective image segmentation.

  • Evaluate and select appropriate loss functions and evaluation metrics for optimizing deep learning models.

Details to know

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Recently updated!

September 2024

Assessments

1 assignment

Taught in English

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There are 4 modules in this course

In this module, we will establish the foundational setup required for the course. We will define image segmentation, outline the course scope, and walk through the system setup. Additionally, we will cover how to access the necessary materials and configure the Conda environment for working with PyTorch.

What's included

5 videos1 reading

In this module, we will explore the basics of PyTorch, a powerful deep learning framework. We will delve into tensor operations, computational graphs, and the construction of neural network models. This section will equip you with essential skills for developing and training models in PyTorch.

What's included

19 videos

In this module, we will delve into Convolutional Neural Networks (CNNs) and their applications in computer vision. We will cover the basics of CNN architecture, image preprocessing techniques, and the debugging of neural networks. This section provides a comprehensive introduction to CNNs and their practical implementations.

What's included

6 videos

In this module, we will focus on semantic segmentation, a critical task in image analysis. We will explore various neural network architectures, upsampling techniques, and loss functions. Additionally, we will cover data preparation, model training, and evaluation metrics to ensure accurate and effective segmentation results.

What's included

15 videos1 assignment

Instructor

Packt
Packt
231 Courses3,858 learners

Offered by

Packt

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