Dive deep into the intricacies of InceptionV3, a pivotal iteration in the Inception network series for CNN classifiers. Explore the key improvements and architectural modifications that set InceptionV3 apart from its predecessors. Learn about factorized convolutions, including symmetric and asymmetric approaches, and understand how these optimizations enhance both speed and accuracy. Examine the detailed layer architecture of InceptionV3, including Reduction A, Inception B, Inception C, and Reduction Blocks. Gain insights into the implementation of RMSProp optimizer, factorized 7x7 convolutions, BatchNorm in auxiliary classifiers, and label smoothing techniques. Discover how these enhancements contribute to preventing overfitting and improving overall network performance.
Overview
Syllabus
Introduction
Factorization
Factorization into symmetric convolution
Factorization into asymmetric convolution
InceptionV3 Layer Architecture
InceptionV3 Code
Reduction A
Inception B
Inception C
Reduction Blocks
Conclusion
Taught by
Code With Aarohi
Related Courses
-
Computer Vision Fundamentals with Google Cloud
-
Computer Vision Fundamentals with Google Cloud
-
Inception V2 Neural Network Architecture Tutorial
-
Alexnet Architecture In-Depth Discussion Along With Code - Deep Learning Advanced CNN
-
Explained Identity Block and Convolution Block in ResNet - Residual Networks
