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Getting Started with Image Classification with PyTorch

Name: Getting Started with Image Classification with PyTorch
Price: 59 USD
Author: Ng Wai Foong

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

4.6

50 lessons

1 project

Created by Ng Wai Foong

Getting Started with Image Classification with PyTorch

What's included

50 lessons across 9 modules
6 quizzes with instant feedback
1 project in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

50 Lessons6 Quizzes

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

7 Lessons

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

Loss

4 Lessons

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

Training

7 Lessons

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

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

Premium

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

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

Ending Note

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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 computer vision model with image pre-processing, set up pipelines for training your model, and fine-tune the variables to improve predictive performance. You’ll finish by deploying the image classification model by converting to ONNX format and serving it via REST API. By the end of this course, you’ll be able to build and deploy your own image classification models from scratch.

ABOUT THE AUTHOR

Ng Wai Foong

Content writer and AI specialist in speech synthesis, object detection and neural machine translation.

Learn more about Ng

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