UNet（TensorFlow2）图像语义分割实战：训练自己的数据集

Name: UNet（TensorFlow2）图像语义分割实战：训练自己的数据集
Price: 24.99 USD
Availability: InStock

Develop essential data science & ai skills with expert instruction and practical examples.

Online Course

Self-paced learning

Flexible Schedule

Learn at your pace

Expert Instructor

Industry professional

Certificate

Upon completion

What You'll Learn

Master the fundamentals of data science & ai

Apply best practices and industry standards

Build practical projects to demonstrate your skills

Understand advanced concepts and techniques

Skills you'll gain:

Professional SkillsBest PracticesIndustry Standards

Prerequisites & Target Audience

Skill Level

IntermediateSome prior knowledge recommended

Requirements

Basic understanding of data science & ai

Enthusiasm to learn

Access to necessary software/tools

Commitment to practice

Who This Course Is For

•Professionals working in data science & ai

•Students and career changers

•Freelancers and consultants

•Anyone looking to improve their skills

Course Information

About This Course

U-Net是一种基于深度学习的图像语义分割方法，尤其在医学图像分割中表现优异。本课程将手把手地教大家使用labelme图像标注工具制作自己的数据集，生成Mask图像，并使用U-Net训练自己的数据集，从而能开展自己的图像分割应用。本课程首先讲述图像分割的任务说明、常用数据集，然后介绍UNet网络的原理。本课程有三个项目实践：(1) Kaggle盐体识别比赛：利用U-Net进行Kaggle盐体识别(2) Pothole语义分割：对汽车行驶场景中的路坑进行标注和语义分割(3) Kaggle细胞核分割比赛：利用U-Net进行Kaggle细胞核分割本课程使用TensorFlow2版本的U-Net，在Ubuntu系统上用Jupyter Notebook做项目演示。包括：数据集标注、数据集格式转换和Mask图像生成、编写U-Net程序文件、训练自己的数据集、测试训练出的网络模型、性能评估。项目代码也可在Windows上运行，课程提供Windows环境搭建方法。本课程提供项目的数据集和Python程序代码。.

Provider

Udemy

Estimated Duration

10-20 hours

Language

English