ResNet | Paper Explained & PyTorch Implementation

Показать описание

In this video I go through famous "Deep Residual Learning for Image Recognition" paper and implement it in PyTorch.

* Values above blocks are not number of parameters

Paper:
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

GitHub Repo:

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

Connect with me on:

Timestamps:
0:00 Paper Overview
1:03 Degradation Problem / Identity Mapping
3:02 Residual Block
4:10 Architecture
5:47 Implementation Details
6:46 Bottleneck Representation
8:18 PyTorch implementation
9:48 Bottleneck Residual Block
16:10 ResNet Architecture
21:03 Testing & Fixing

Рекомендации по теме

Комментарии

Thanks a 1000 brother for this awesome implementation. I saw many other vids but none gave me such a clarity that this vid. gave. You earned a sub :- )

codedByAyush

amazing! really helpful, thank you :)

gabriellafernandes

I think there is slight mistake in the ResidualBlock- you have fed x as input for self.c2 and self.c3 convlayers it should be f=self.relu(self.c2(f)); f=self.c3(f)

rahulnakka

Thank you for perfect explanation. It works very good. Maybe you could do a similar video about GANs?

mwont

ResNet | Paper Explained & PyTorch Implementation

[Classic] Deep Residual Learning for Image Recognition (Paper Explained)

ResNet (actually) explained in under 10 minutes

Residual Networks and Skip Connections (DL 15)

ResNet | Paper Explained & PyTorch Implementation

ResNet Explained Step by Step( Residual Networks)

ResNet Strikes Back! | Patches Are All You Need? | Papers Explained

What is ResNet? (with 3D Visualizations)

2015 ResNet paper summary

C4W2L03 Resnets

ResNeXt | Paper Explained & PyTorch Implementation

ResNet Explained!

[Classic] ImageNet Classification with Deep Convolutional Neural Networks (Paper Explained)

Vision Transformer Quick Guide - Theory and Code in (almost) 15 min

OpenAI CLIP: ConnectingText and Images (Paper Explained)

Transformers, explained: Understand the model behind GPT, BERT, and T5

[ENG] ResNet paper detailed explanation

ResNet Deep Neural Network Architecture Explained

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)

The U-Net (actually) explained in 10 minutes

NVAE: A Deep Hierarchical Variational Autoencoder (Paper Explained)

ResNet Explained (Residual Network for Object Recognition) | ResNet Architecture

Faster Neural Network Training with Data Echoing (Paper Explained)

Convolutional Block Attention Module (CBAM) Paper Explained

Illustrated Guide to Transformers Neural Network: A step by step explanation