Performance Metrics (IOU, Pixel Accuracy, Fscore)

Deep Learning for Image Segmentation with Python & Pytorch

Section 1 - Introduction To Course

Section 2 - Semantic Segmentation And Its Real-World Applications

Section 3 - Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN, MTCNet)

Section 4 - Datasets And Data Annotations Tool For Semantic Segmentation

Section 5 - Google Colab Setting-Up For Writing 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

Section 8 - Transfer Learning And Pretrained Deep Resnet Architecture

Section 9 - Encoders and Decoders For Segmentation In PyTorch

Section 10 - Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) Using PyTorch

Section 11 - Optimization and Training Of Segmentation Models

Section 12 - Test Models and Visualize Segmentation Results

Section 13 - Bonus Lecture Resources - Code And Dataset Of Semantic Segmentation

Lesson content locked

If you're already enrolled, you'll need to login.