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Mastering Image Segmentation with PyTorch
Rating: 4.2 out of 5(81 ratings)
521 students

Mastering Image Segmentation with PyTorch

Master the art of image segmentation with PyTorch with hands-on training and real-world projects
Created byBert Gollnick
Last updated 5/2024
English

What you'll learn

  • implement multi-class semantic segmentation with PyTorch on a real-world dataset
  • get familiar with architectures like UNet, FPN
  • understand theoretical background, e.g. on upsampling, loss functions, evaluation metrics
  • perform data preparation to reshape inputs to appropriate format

Course content

5 sections • 45 lectures • 5h 3m total length

  • Image Segmentation (101)7:41
  • Course Scope3:46
  • System Setup4:22

    Install these programs:

    1. Anaconda (https://www.anaconda.com/products/distribution)

    2. Visual Studio Code (https://code.visualstudio.com/)

  • How to Get The Material2:16

    This is the link to my homepage:

    https://gollnickdata.de/


    Here is the link to the GitHub repository: https://github.com/DataScienceHamburg/ImageSegmentationCourseMaterial

  • Conda Environment Setup7:02

    1. Install PyTorch

    • go to Pytorch.org and select the option appropriate to your system

    2. Install further packages

    • patchify

    • ipykernel

    • seaborn

    • segmentation-models-pytorch

    • opencv-python

    • torchmetrics

Requirements

  • Basic Python knowledge

Description

Welcome to "Mastering Image Segmentation with PyTorch"! In this course, you will learn everything you need to know to get started with image segmentation using PyTorch.

Image segmentation is a key technology in the field of computer vision, which enables computers to understand the content of an image at a pixel level. It has numerous applications, including autonomous vehicles, medical imaging, and augmented reality.

This course is designed for both beginners and experts in the field of computer vision. If you are a beginner, we will start with the basics of PyTorch and how to use it for simple modeling. Then, you will learn how to implement popular semantic segmentation models such as FPN or U-Net.

By the end of this course, you will have the skills and knowledge to tackle real-world semantic segmentation projects using PyTorch.

So why wait? Join me today and take the first step towards mastering image segmentation with PyTorch!


In my course I will teach you:

  • Tensor handling

    • creation and specific features of tensors

    • automatic gradient calculation (autograd)

  • Modeling introduction, incl.

    • Linear Regression from scratch

    • understanding PyTorch model training

    • Batches

    • Datasets and Dataloaders

    • Hyperparameter Tuning

    • saving and loading models

  • Convolutional Neural Networks

    • CNN theory

    • layer dimension calculation

    • image transformations

  • Semantic Segmentation

    • Architecture

    • Upsampling

    • Loss Functions

    • Evaluation Metrics

    • Train a Semantic Segmentation Model on a custom Dataset


Enroll right now to learn some of the coolest techniques and boost your career with your new skills.


Best regards,

Bert

Who this course is for:

  • Developers who want to understand and implement Image Segmentation
  • Data Scientists who want to broaden their scope of Deep Learning techniques

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