Enroll Course: https://www.udemy.com/course/yolov8-seg/
In the rapidly evolving field of computer vision, instance segmentation stands out as a powerful technique, allowing us to not only detect objects but also delineate their precise boundaries. The latest iteration, YOLOv8, has taken this capability to new heights, building upon the legacy of its predecessors with enhanced performance and flexibility. This Udemy course, “YOLOv8實例分割實戰：訓練自己的資料集” (YOLOv8 Instance Segmentation Practical: Training Your Own Dataset), offers an in-depth, hands-on approach to mastering this technology.
The course promises to guide students through the entire process of training a custom YOLOv8 instance segmentation model. It covers everything from setting up the necessary software environment – including Nvidia drivers, CUDA, and cuDNN – to installing PyTorch and YOLOv8 itself. A significant portion of the curriculum is dedicated to practical application, focusing on using the ‘labelme’ tool for data annotation. Students will learn how to meticulously label their own datasets, convert them into the required format, and prepare them for training.
The core of the course revolves around a real-world project: segmenting objects in automotive driving scenarios. Specifically, it targets the identification and segmentation of potholes, vehicles, and lane lines within images and videos. This practical focus provides invaluable experience in applying YOLOv8 to a complex and relevant problem. The course thoughtfully demonstrates the entire workflow on both Windows and Ubuntu operating systems, ensuring accessibility for a wider audience.
Key modules include modifying configuration files, the crucial step of training the custom dataset, and finally, testing the trained model with performance statistics. This structured approach ensures that learners gain a thorough understanding of not just the ‘what’ but also the ‘how’ and ‘why’ behind each step.
For anyone looking to dive into instance segmentation and build their own specialized models, this course is a highly recommended resource. Its practical, project-based learning approach, coupled with comprehensive coverage of essential tools and techniques, makes it an excellent investment for aspiring computer vision engineers and researchers.
Enroll Course: https://www.udemy.com/course/yolov8-seg/