Mastering Image Segmentation with PyTorch using Real-World Projects [Video]

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.
You will start by exploring tensor handling, automatic gradient calculation with autograd, and the fundamentals of PyTorch model training. As you progress, you will build a strong foundation, covering critical topics such as working with datasets, optimizing hyperparameters, and the art of saving and deploying your models.
With a robust understanding of PyTorch, you will dive into the heart of the course—semantic segmentation. You will explore the architecture of popular models such as UNet and FPN, understand the intricacies of upsampling, grasp the nuances of various loss functions, and become fluent in essential evaluation metrics.
Moreover, you will apply this knowledge in real-world scenarios, learning how to train a semantic segmentation model on a custom dataset. This practical experience ensures that you are not just learning theory but gaining the skills to tackle actual projects with confidence.
By course end, you will wield the power to perform multi-class semantic segmentation on real-world datasets.
What You Will Learn
Implement multi-class semantic segmentation with PyTorch
Explore UNet and FPN architectures for image segmentation
Understand upsampling techniques and their importance in deep learning
Learn the theory behind loss functions and evaluation metrics
Perform efficient data preparation to reshape inputs to the appropriate format
Create a custom dataset class for image segmentation in PyTorch