Deep Learning Image Classification In Pytorch 2.0

Deep Learning Image Classification In Pytorch 2.0
Published 11/2023
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Deep Learning | Computer Vision | Image Classification Model Training and Testing | PyTorch 2.0 | Python3

What you’ll learn

Learn to prepare an image classification dataset.

Learn to process the dataset by using image_folder and by extending the dataset class from torchvision.

Learn to prepare and test the data pipeline.

Learning about Data augmentation such as resize, cropping, ColorJitter, RandomHorizontalflip, RandomVerticalFlip, RandomRotation.

Understanding the detail architecture of LeNet, VGG16, Inception v3, and ResNet50 with complete block diagram.

Learn to train the model on less data through transfer learning.

Learning about training pipeline to train any image classification model.

Learning about inference pipeline to display the result.

Learning about evalution process of image classification model through Precision, Recall, F1 Score, and Accuracy.

Requirements

Basic knowledge of Python

Access to internet connection

Basic understanding of CNNs

Description

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 what is PyTorch ?

Lecture 3 Installing Python and PyCharm

Lecture 4 Installing Cuda Libraries

Lecture 5 Installing PyTorch Package

Lecture 6 Introduction to Colab

Section 2: Data Preparation and Processing in PyTorch

Lecture 7 About Dataset Preparation

Lecture 8 Data Processing with ImageFolder

Lecture 9 Implementation of Data Processing with ImageFolder

Lecture 10 Data Processing with Custom Class

Lecture 11 Implementation of Data Processing with Custom Class

Lecture 12 Testing Data Preparation Pipeline

Section 3: LeNet

Lecture 13 LeNet-5 Architecture

Lecture 14 Implementation of LeNet Model

Lecture 15 Issue while Training Model on Colab

Lecture 18 Inferencing LeNet Model in PyTorch 2.0

Section 4: VGG 16 Model

Lecture 19 VGG 16 Model Architecture

Lecture 20 Implementation of VGG 16 Model

Lecture 21 Training VGG 16 Model in PyTorch 2.0

Lecture 22 Inferencing VGG 16 Model in PyTorch 2.0

Section 5: Inception Model

Lecture 23 Inception Model Architecture

Lecture 24 Implementation of Inception Model

Lecture 25 Training Inception Model in PyTorch 2.0

Lecture 26 Inferencing Inception Model in PyTorch 2.0

Section 6: ResNet Model

Lecture 27 ResNet 50 Model Architecture

Lecture 28 Implementation of ResNet 50 Model

Lecture 29 Training ResNet Model in PyTorch 2.0

Lecture 30 Inferencing ResNet Model in PyTorch 2.0

Section 7: Conclusion

Lecture 31 Thank You