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Deep Learning for Image Segmentation with Python & Pytorch
Section 1 - Introduction To Course
Introduction to the Course (4:00)
Section 2 - Semantic Segmentation And Its Real-World Applications
What Is Semantic Image Segmentation (5:31)
Semantic Segmentation Real-World Applications (11:02)
Section 3 - Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN, MTCNet)
Pyramid Scene Parsing Network (PSPNet) (4:30)
UNet Architecture For Segmentation (3:37)
Pyramid Attention Network (PAN) (3:34)
Multi-Task Contextual Network (MTCNet) (4:37)
Section 4 - Datasets And Data Annotations Tool For Semantic Segmentation
Datasets For Semantic Segmentation (5:52)
Data Annotations Tool For Semantic Segmentation (5:30)
Section 5 - Google Colab Setting-Up For Writing Python Code
Set-Up Google Colab For Writing Python And PyTorch Code (5:11)
Connect Google Colab With Google Drive To Read And Write Data (2:43)
Python Code
Section 6 - Data Augmentation and Data Loading
Data Loading With PyTorch Customized Dataset Class (18:01)
Data Loading for Segmentation with Python and PyTorch Code
Performe Data Augmentation Using Albumentations With Different Transformations (9:49)
Augmentation Python Code
Learn To Implement Data Loaders In Pytorch (5:01)
Section 7 -Performance Metrics (IOU) For Segmentation Models Evaluation
Performance Metrics (IOU, Pixel Accuracy, Fscore) (9:48)
Code (Python And PyTorch)
Section 8 - Transfer Learning And Pretrained Deep Resnet Architecture
Transfer Learning And Pretrained Deep Resnet Architecture (8:49)
Section 9 - Encoders and Decoders For Segmentation In PyTorch
Encoders for Segmentation with PyTorch Liberary (9:38)
Dencoders for Segmentation in PyTorch Liberary (10:04)
Section 10 - Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) Using PyTorch
Implement Segmentation Models (17:00)
Segmentation Models Code with Python
Section 11 - Optimization and Training Of Segmentation Models
Learn To Optimize Hyperparameters For Segmentation Models (8:31)
Model Optimaztion Code (Python And PyTorch)
Training of Segmentation Models (9:00)
Training Code (Python And PyTorch)
Section 12 - Test Models and Visualize Segmentation Results
Test Model and Calculate IOU,Pixel Accuracy,Fscore (12:01)
Test Model and Calculate Performance Scores (Python Code)
Visualize Segmentation Results and Generate RGB Segmented Map (12:00)
Segmentation Results Visualization (Python Code)
Section 13 - Bonus Lecture Resources - Code And Dataset Of Semantic Segmentation
Final Code Review (3:19)
TrayDataset for Segmentation
Final Code
Learn To Implement Data Loaders In Pytorch
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