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The deep learning image segmentation course aims to help students quickly grasp the principles of classic algorithms in the field of segmentation and their practical applications. Explain the current mainstream segmentation algorithm and its improved version of the network architecture in general, and demonstrate the network modeling process and its application methods in detail through the source code.
All cases are based on real data sets and actual tasks, and all project contents are completed based on the PyTorch framework. The overall style is easy to understand, and the whole process of practical interpretation of the major segmentation algorithms and their application examples.
Chapter 1 Overview of Image Segmentation and Loss Functions
Chapter 2 Convolutional Neural Network Principles and Interpretation of Parameters
Chapter 3 Explanation of Unet Series Algorithms
Chapter 4 Practice of Unet Medical Cell Segmentation
Chapter 5 Interpretation of U2NET Network Architecture Ideas
Chapter 6 DeepLab Series Algorithms
Chapter 7 VOC segmentation practice based on deeplabV3+ version
Chapter 8 Medical cardiac video dataset segmentation modeling practice
Chapter 9 Object detection framework-MaskRcnn project introduction and configuration
Chapter 10 MaskRcnn network framework source code detailed
Chapter 11 Based on MASK-RCNN framework Training Your Data and Tasks
Chapter 12 Updates and Supplements-PyTorch Framework Basic Processing Operations
Chapter 13 Updates and Supplements-PyTorch Framework Essential Core Module Interpretation
Chapter 14 Updates and Supplements-Resnet Model and Its Application Examples