This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic 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 Segmentation
Developers who want to incorporate Semantic Segmentation capabilities into their projects
Graduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic Segmentation
In 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 PyTorch
The course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch as follows:
Semantic Segmentation and its Real-World Applications
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 Segmentation
Google Colab for Writing Python Code
Data Augmentation and Data Loading in PyTorch
Performance Metrics (IOU) for Segmentation Models Evaluation
Transfer Learning and Pretrained Deep Resnet Architecture
Segmentation Models Implementation in PyTorch using different Encoder and Decoder Architectures
Hyperparameters Optimization and Training of Segmentation Models
Test Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score
Visualize Segmentation Results and Generate RGB Predicted Segmentation Map
By 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.