Withdrawal method: Baidu.com
Total [39] sections
Availability of courseware: Yes

You will gain

Mastering the YOLOv8 Example Segmentation Training Your Own Dataset methodology

Master image segmentation annotation methods

Learning YOLOv8 PySide6 GUI Visualization Interface

population (esp. of a group of people)

Students and practitioners who wish to learn YOLOv8 instance segmentation techniques
Courses

Ultralytics YOLOv8 builds on the success of previous YOLO releases, introducing new features and improvements to further enhance performance and flexibility.YOLOv8 supports target detection and tracking, instance segmentation, image classification and pose estimation tasks.

This course will teach you to use labelme labeling and use YOLOv8 to train your own dataset to complete a multi-objective instance segmentation project. This course uses pictures and videos of car driving scenes to carry out the project: object annotation and instance segmentation of potholes, cars and lane lines in the car driving scene.

This course is a project demonstration on Windows and Ubuntu systems respectively. Including: installing software environment (Nvidia graphics driver, cuda and cudnn), installing PyTorch, installing YOLOv8, labeling your own dataset using labelme, dataset format conversion, preparing your own dataset, modifying configuration files, training your own dataset, testing the trained network model and performance statistics, YOLOv8 PySide6 GUI visualization interface.

New to this course is a hands-on project demo flow for using free GPU arithmetic on AliCloud.

Course Catalog

Chapter 1: Introduction to the course
Course Description 10:29

Chapter 2: Image Segmentation Basics
Image Segmentation - Task Description and Common Datasets 15:07
Image Segmentation - Performance Metrics 15:09

Chapter 3: YOLOv8 network principle chapter
History of YOLO Target Detection Series Technology 16:33
YOLOv8 Network Architecture 18:32
YOLACT Example Segmentation Principle 30:02
YOLOv8 Example Split Network Output 10:22

Chapter 4: YOLOv8 Example Split Project Practice (Windows)
Installing software environment (Nvidia drivers, CUDA and cuDNN) 07:50
Installing PyTorch 03:36
Installing YOLOv8 07:00
Labeling your own dataset with labelme 09:15
Data set format conversion 12:19
Preparing your own dataset 02:43
Modifying configuration files 02:19
Modifying configuration file update 00:48
Training your own dataset 08:14
Testing trained network models and performance statistics 06:46
YOLOv8 PySide6 GUI visualization interface 08:40
YOLOv8 PySide6 GUI visual interface manipulation 04:53
YOLOv8 Example of Splitting Each Target Saved to a Separate File 01:24

Chapter 5: YOLOv8 Example Segmentation Project in Action (Ubuntu)
Installing software environment (Nvidia drivers, CUDA and cuDNN) 08:57
Installing PyTorch 08:49
Installing YOLOv8 10:42
Labeling your own dataset with labelme 11:11
Data set format conversion 10:17
Preparing your own dataset 02:05
Modifying configuration files 03:07
Modifying configuration file update 00:48
Training your own dataset 09:29
Testing trained network models and performance statistics 06:09
YOLOv8 PySide6 GUI visualization interface 07:15
YOLOv8 PySide6 GUI visual interface manipulation 04:53
YOLOv8 Example of Splitting Each Target Saved to a Separate File 01:24

Chapter 6: YOLOv8 example segmentation project practice (AliCloud free GPU arithmetic)
AliCloud Creating Instances 04:42
Project cloning and installation 05:53
Preparing the dataset 01:53
Modifying configuration files 02:50
Training your own dataset 06:28
Performance Evaluation 03:44

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