Deep Learning for Image Segmentation with Python & Pytorch

这是一门面向实际应用的深度学习实践课程，专注于使用Python和PyTorch框架实现图像语义分割技术。课程将带您从零开始掌握语义分割完整流程：从理解UNet、DeepLabV3、SAM等主流分割架构，到实现数据增强与数据加载，再到使用预训练ResNet进行迁移学习，最终完成自定义数据集的模型训练、性能评估（包括IOU、准确率等指标）和结果可视化。通过本课程，您将具备将深度学习应用于自动驾驶等现实场景的能力，无需前置分割知识，仅需Gmail账号即可通过Google Colab开始实践。

由 Mazhar Hussain 博士、人工智能与计算机科学学院
MP4 创建 | 视频：h264、1920×1080 | 音频：AAC、44.1 KHz、2 声道
级别：全部 | 类型：电子学习 | 语言：英语 + 英文字幕 | 时长：40 讲（3 小时 37 分钟）| 大小：2 GB

Image Semantic Segmentation for Computer Vision with PyTorch & Python to Train & Deploy YOUR own Models (UNet, SAM)

What you’ll learn
Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch
Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet etc.)
Segmentatin Anything Model (SAM) produces high quality object masks from input prompts.
Perform Image Segmentation with Deep Learning Models on Custom Datasets
Datasets and Data Annotations Tool for Semantic Segmentation
Data Augmentation and Data Loaders Implementation in PyTorch
Learn Performance Metrics (IOU, etc.) for Segmentation Models Evaluation
Transfer Learning and Pretrained Deep Resnet Architecture
Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) in PyTorch using different Encoder and Decoder Architectures
Learn to Optimize Hyperparameters for Segmentation Models to Improve the Performance during Training on Custom Dataset
Test Segmentation Trained Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score
Visualize Segmentation Results and Generate RGB Predicted Output Segmentation Map

Requirements
Deep Learning for Semantic Segmentation with Python and Pytorch is taught in this course by following a complete pipeline from Zero to Hero
No prior knowledge of Semantic Segmentation is assumed. Everything will be covered with hands-on training
A Google Gmail account is required to get started with Google Colab to write Python Code

Description
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.Segmentatin Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes.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.