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

Introduction to PyTorch image classification-01.png

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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.

Getting Started with Image Classification with PyTorch

## Introduction to image preprocessing
Image processing is an important part of a computer vision project. This lesson will cover two important tools that help load and preprocess images.

First, we’ll review loading images with the **Python Imaging Library (PIL)**. Second, we’ll review image transformations with the **torchvision** framework.


## Python Imaging Library (PIL)
The Python Imaging Library (PIL)  is a useful library for image processing because it comes with the following features:
* It has extensive file format support.
* It has efficient internal representation. 
* It has powerful image-processing functions.

It’s widely considered to be a must-have image-processing tool for Python developers.


### Load an image
We can easily load an image via PIL using the `open` function:



# Introduction to image preprocessing
Image processing is an important part of a computer vision project. This lesson will cover two important tools that help load and preprocess images.

First, we’ll review loading images with the **Python Imaging Library (PIL)**. Second, we’ll review image transformations with the **torchvision** framework.


# Python Imaging Library (PIL)
The Python Imaging Library (PIL)  is a useful library for image processing because it comes with the following features:
* It has extensive file format support.
* It has efficient internal representation. 
* It has powerful image-processing functions.

It’s widely considered to be a must-have image-processing tool for Python developers.


## Load an image
We can easily load an image via PIL using the `open` function:



Learn to load images using the PIL package and perform transformations on images with torchvision. 

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Deployment

Appendix

Conclusion

Load and Preprocess Images

Introduction to image preprocessing

Python Imaging Library (PIL)

Load an image