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.
publication date
2023-09-29
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
key features
Understand key concepts from tensors to advanced segmentation models * Implement real-world image segmentation projects with confidence * Ideal for both beginners and experienced computer vision enthusiasts
approach
This course employs a hands-on, practical approach to ensure effective learning. You will start from the basics, gradually advancing through theoretical foundations and model implementation. Real-world projects and step-by-step guidance will empower you to confidently apply your knowledge. Get ready to code, experiment, and conquer image segmentation with PyTorch.
audience
This course is tailored to a diverse audience, making it accessible to both newcomers and experienced individuals in the field of computer vision. If you are an aspiring developer eager to delve into image segmentation or a data scientist aiming to expand your deep learning repertoire, this course is for you.
While no prior image segmentation knowledge is required, a fundamental understanding of Python is essential. Familiarity with machine learning concepts will be beneficial.
short description
Dive into the world of image segmentation with PyTorch. From tensors to UNet and FPN architectures, grasp the theory behind convolutional neural networks, loss functions, and evaluation metrics. Learn to mold data and tackle real-world projects, equipping developers and data scientists with versatile deep-learning skills.
subtitle
Master the art of image segmentation with PyTorch through hands-on training and real-world projects
keywords
Image Segmentation, PyTorch, Deep Learning, Semantic Segmentation, Computer Vision, UNet Architecture, FPN (Feature Pyramid Network), Data Preparation, Model Training, Evaluation Metrics, Real-world Projects, Autonomous Vehicles, Medical Imaging, Augmented Reality, Deep Learning Techniques, Tensors, Autograd, Convolutional Neural Networks (CNNs), Upsampling Techniques, Loss Functions, Hyperparameter Tuning, Data Loaders, Custom Dataset Class, Model Deployment, Model Saving and Loading, Batches, Pixel Accuracy, Intersection Over Union (IoU), Semantic Segmentation Model, Training Loop, Regenerate
Product ISBN
9781801817356
