PyTorch Image Segmentation Tutorial with U-NET: everything from scratch baby
This tutorial provides a comprehensive guide to image segmentation using PyTorch and U-NET. It covers the entire process from scratch, including creating a dataset, building a model, training, and evaluation. It also provides useful utilities to help with the process. This tutorial is a great resource for anyone looking to get started with image segmentation. ▼
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Course Feature
Cost:
Free
Provider:
Youtube
Certificate:
Paid Certification
Language:
English
Start Date:
On-Demand
Course Overview
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Updated in [February 21st, 2023]
In this course, you will embark on a journey to learn PyTorch image segmentation using the U-Net architecture. From scratch, you will understand how to build a powerful model capable of segmenting images with precision. Starting with the basics, you will learn how to construct the U-Net model and create a dataset specifically designed for image segmentation tasks. Next, you will delve into the training process, exploring techniques to optimize model performance. Additionally, you will master the development of utility functions, enabling seamless integration of your model into practical applications. By the end of this course, you will have the knowledge and skills to perform image segmentation using PyTorch and the U-Net architecture.
Possible Development Paths:
Computer Vision Engineer: This course lays a solid foundation for a career as a computer vision engineer. Building upon the knowledge gained in image segmentation, learners can further their understanding of computer vision techniques, such as object detection, image classification, and image synthesis. Pursuing advanced courses or certifications in computer vision and deep learning can enhance career prospects in industries like autonomous vehicles, medical imaging, or augmented reality.
Research and Development: For learners interested in pushing the boundaries of image segmentation, further research and development offer exciting prospects. Exploring advanced topics like semantic segmentation, instance segmentation, or video segmentation can contribute to the advancement of computer vision. Pursuing higher degrees or joining research institutions focused on computer vision and machine learning can open doors to opportunities in R&D.
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