Getting Started with Image Classification with PyTorch

Gain insights into image classification with PyTorch. Learn about data preprocessing, model training, fine-tuning, and deploying models using ONNX for real-world applications.

Beginner

50 Lessons

Certificate of Completion

Gain insights into image classification with PyTorch. Learn about data preprocessing, model training, fine-tuning, and deploying models using ONNX for real-world applications.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

110 Playgrounds

6 Quizzes

This course includes

110 Playgrounds

6 Quizzes

Course Overview

PyTorch is a machine learning framework used in a wide array of popular applications, including Tesla’s Autopilot and Pyro, Uber’s probabilistic modeling engine. This course is an introduction to image classification using PyTorch’s computer vision models for training and tuning your own model. You’ll start with the fundamental concepts of applying machine learning and its applications to image classification before exploring the process of training your AI model. You’ll prepare data for intake by the comp...Show More

TAKEAWAY SKILLS

Database

Python

Sql

What You'll Learn

A basic overview of the PyTorch Image Model

The ability to fine-tune custom image classification models

A working knowledge of deploying models as REST API

A familiarity with converting PyTorch models into ONNX format

What You'll Learn

A basic overview of the PyTorch Image Model

Course Content

Introduction

Get familiar with image classification, techniques, metrics, and PyTorch Image Model framework.

Overview of Image Classification

Image Classification Techniques

Image Classification Metrics PyTorch Image Model Framework

Basic Concepts

Look at essential PyTorch image classification, including models, datasets, preprocessing, and inference.

Create Model List and Search Supported Models Dataset Format Load and Preprocess Images Prediction Using Pre-trained Resnet50 Prediction Using Pre-trained EfficientNet Quiz (Basic)Exercise (Basic)

Augmentation

Examine augmentation techniques to diversify datasets, improve model performance, and mitigate overfitting.

Training a Neural Network with Augmentation Mixup and Cutmix RandAugment Random Erase AutoAugment Random Resized Crop and Interpolation Quiz (Augmentation)

Loss

Grasp the fundamentals of loss functions to improve model accuracy in PyTorch.

Loss Functions Asymmetric Loss Jensen-Shannon Divergence and Cross-Entropy Loss Quiz (Loss)

Training

Solve problems in image classification training using PyTorch, models, and techniques.

Training Workflow ResNet50 EfficientNet Fine-tuning Custom Model Training with an EMA (Exponential Moving Average)Quiz (Training)Exercise (Training)

Model Conversion

9 Lessons

Follow the process of converting and serving models across PyTorch, ONNX, TensorFlow, and TFLite.

Anomaly Detection in Medical Images: Python, TF, and PyTorch

Project

Deployment

5 Lessons

Practice using FastAPI to deploy image classification models with HTTP methods and REST API integration.

Appendix

5 Lessons

Learn how to use virtual environments, Python packages, training arguments, and deployment dependencies.

Conclusion

1 Lesson

Solve challenges with essential PyTorch image classification concepts, model deployment, and performance improvement.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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