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 will learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You will 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 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 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 will have the knowledge and skills you need to start applying Deep Learning to Semantic Segmentation problems in your own work or research. Whether you are 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. Lets get started on this exciting journey of Deep Learning for Semantic Segmentation with Python and PyTorch.
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SKILLS YOU WILL GAIN
Image Segmentation with Python and PyTorch
Semantic Segmentation with PyTorch and Python
Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) Implementation in PyTorch
Test, Train and Deploy Segmentation Models
WHAT YOU WILL LEARN
Learn Image Semantic Segmentation Complete Pipeline and its Real world Applications with Python and PyTorch using Google Colab
Segmentation Datasets, Data annotations Tool for Semantic Segmentation, Data Augmentation and Data Loaders Implementation in PyTorch and Segmentation Performance Metrics (IOU, etc.)
Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) Implementation in PyTorch using different Encoder and Decoder Architectures
Learn to Optimize Hyperparameters for Segmentation Models, Test Segmentation Trained Model, Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall, and Visualize Segmentation Results