ResNet and ResNeXt

Understanding and Implementing Architectures of ResNet and ResNeXt for state-of-the-art Image Classification: From Microsoft to Facebook [Part 1]

In this two part blog post we will explore Residual networks. More specifically we will discuss three papers released by Microsoft Research and Facebook AI research, state of the art Image classification networks- ResNet and ResNeXt Architectures and try to implement them on Pytorch.

About the series:

Signup for my AI newsletter

Was ResNet Successful?

What problem does ResNet solve?

Problem:

Seeing Degrading in Action:

Shallow network and its deeper variant both giving the same output

How to solve?

The author’s hypothesis is that it is easy to optimize the residual mapping function F(x) than to optimize the original, unreferenced mapping H(x).

Intuition behind Residual blocks:

Identity mapping in Residual blocks

Test cases:

Plain VGG and VGG with Residual Blocks
Residual block
Residual block function when input and output dimensions are same
Residual block function when the input and output dimensions are not same.
ResNet Model comparison with their counter plain nets

Deeper Studies

ResNet Architectures
ResNet 2 layer and 3 layer Block

Observations:

ResNet ImageNet Results-2015

Implementation using Pytorch

Senior Data Scientist @FractalAI

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store