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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.

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Explanations

AI-POWERED

Explanations

This course includes

110 Playgrounds

6 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

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

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

A basic overview of the PyTorch Image Model

Course Content

Introduction

4 Lessons

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

8 Lessons

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.

Training a Neural Network with Augmentation

Random Resized Crop and Interpolation

Quiz (Augmentation)

Loss

4 Lessons

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

7 Lessons

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.

Serving a Pytorch Model

Convert PyTorch Model into an ONNX Model

Serving an ONNX Model

Convert ONNX into a Tensorflow Model

Serving a TensorFlow Model

Convert a TensorFlow Model into a TFLite Model

Serving a TFLite Model

Quiz (Model Conversion)

Exercise (Model Conversion)

Deployment

5 Lessons

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

Exercise (Deployment)

Appendix

5 Lessons

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

Deployment Dependencies

Conclusion

1 Lessons

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

Ending Note

Course Author

Ng Wai Foong

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