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Mastering Image Segmentation with PyTorch

Mastering Image Segmentation with PyTorch
Mastering Image Segmentation with PyTorch

Mastering Image Segmentation with PyTorch

Master the art of image segmentation with PyTorch with hands-on training and real-world projects

What you’ll learn

Mastering Image Segmentation with PyTorch

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


  • Basic Python knowledge


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 critical technology in computer vision, enabling computers to understand an image’s content 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 toward mastering image segmentation with PyTorch!

In my course, I will teach you:

  • Tensor handling
    • creation and specific features of tensors
    • automatic gradient calculation (autographed)
  • 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 Semantic Segmentation Model on a custom Dataset.

Enrol now to learn some excellent techniques and boost your career with new skills.

Best regards,


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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