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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-for-semantic-segmentation-with-python-pytorh/
课程评论：没有评论
课程名称：用Python和PyTorch进行图像分割的深度学习 课程概述：本课程旨在为学员提供全面的实践经验，应用深度学习技术解决语义图像分割问题。课程适合想要提升对深度学习理解的学员，学习如何将深度学习应用于实际问题。在课程中，你将学习如何利用深度学习对图像进行分割，并从视觉数据中提取有意义的信息。课程内容包括语义分割基础知识的介绍，接着将实现并训练自己的语义分割模型，使用Python和PyTorch。 适合对象：本课程面向广泛的学员和专业人士，包括但不限于： - 想将深度学习应用于图像分割任务的机器学习工程师、深度学习工程师和数据科学家 - 希望学习如何使用PyTorch构建和训练语义分割深度学习模型的计算机视觉工程师和研究人员 - 希望将语义分割功能整合到项目中的开发者 - 希望了解深度学习最新进展的计算机科学、电气工程及相关领域的研究生和研究人员 课程内容涵盖了语义分割的完整流程，具有丰富的实践经验，主要包括： - 语义图像分割及其在自动驾驶汽车等现实世界应用中的重要性 - 适用于语义分割的深度学习架构，例如金字塔场景解析网络（PSPNet）、UNet、UNet++、金字塔注意力网络（PAN）、多任务上下文网络（MTCNet）、DeepLabV3等 - 语义分割所需的数据集和数据标注工具 - 使用Google Colab编写Python代码 - PyTorch中的数据增强和数据加载 - 分割模型评估的性能指标（IOU） - 迁移学习及预训练深度Resnet架构 - 使用不同的编码器和解码器架构在PyTorch中实现分割模型 - 超参数优化和模型训练 - 测试分割模型并计算IOU、分类IOU、像素准确率、精确率、召回率和F-score - 可视化分割结果并生成RGB预测分割图 课程结束时，你将获得将深度学习应用于语义分割问题的知识和技能，能够在自己的工作或研究中加以应用。无论你是计算机视觉工程师、数据科学家，还是开发者，该课程都是提升深度学习理解的完美途径。让我们一起开始这段与Python和PyTorch的深度学习语义分割的激动人心的旅程吧。
This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems. Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? In this course, you'll learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You'll start with an introduction to the basics of Semantic Segmentation using Deep Learning, then move on to implementing and training your own models for Semantic Segmentation with Python and PyTorch.This course is designed for a wide range of students and professionals, including but not limited to:Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Image Segmentation tasks Computer Vision Engineers and Researchers who want to learn how to use PyTorch to build and train Deep Learning models for Semantic SegmentationDevelopers who want to incorporate Semantic Segmentation capabilities into their projectsGraduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic SegmentationIn general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorchThe course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch as follows:Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.Deep Learning Architectures for Semantic Segmentation including Pyramid Scene Parsing Network (PSPNet), UNet, UNet++, Pyramid Attention Network (PAN), Multi-Task Contextual Network (MTCNet), DeepLabV3, etc.Datasets and Data annotations Tool for Semantic SegmentationGoogle Colab for Writing Python CodeData Augmentation and Data Loading in PyTorchPerformance Metrics (IOU) for Segmentation Models EvaluationTransfer Learning and Pretrained Deep Resnet ArchitectureSegmentation Models Implementation in PyTorch using different Encoder and Decoder ArchitecturesHyperparameters Optimization and Training of Segmentation ModelsTest Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-scoreVisualize Segmentation Results and Generate RGB Predicted Segmentation MapBy the end of this course, you'll have the knowledge and skills you need to start applying Deep Learning to Semantic Segmentation problems in your own work or research. Whether you're a Computer Vision Engineer, Data Scientist, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let's get started on this exciting journey of Deep Learning for Semantic Segmentation with Python and PyTorch.
