Deep Learning Image Classification in PyTorch 2.0

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

Created by: Pooja Dhouchak
Founder and CEO of FatheVision

Created by: FatheVision AI
Now Computer Vision is easy to learn

What Will I 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

Target audience

• Python developer who is interested in Deep Learning
• Deep Learning enthusiasts who wants to understand Architecture of Image Classification Models such ResNet, VGG, LeNet, Inception
• Deep Learning enthusiasts who wants to learn new features of PyTorch 2.0.
• Deep Learning enthusiasts who is learning Computer Vision and wants to train and evaluate various image classification models
• Deep Learning enthusiasts who wants to learn how to build an custom image classification data