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 model creation
Model creation is the first step in building an image classification model. In this lesson, we’ll learn how to create a new model based on our desired architecture.

## Import
Place the following import statement at the top of our file to import the PyTorch Image Model (timm).

```
import timm
```

## Instantiate a new model
Model instantiation is via the built-in `create_model(modelName)` function. The model name t is a required parameter, so we need to pass it in. We can now use it normally. To understand how the PyTorch model actually works, study the code in the following widget:

# Introduction to model creation
Model creation is the first step in building an image classification model. In this lesson, we’ll learn how to create a new model based on our desired architecture.

# Import
Place the following import statement at the top of our file to import the PyTorch Image Model (timm).

```
import timm
```

# Instantiate a new model
Model instantiation is via the built-in `create_model(modelName)` function. The model name t is a required parameter, so we need to pass it in. We can now use it normally. To understand how the PyTorch model actually works, study the code in the following widget:

Define and load pre-trained or local weights based on our use cases.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

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

Deployment

Appendix

Conclusion

Create Model

Introduction to model creation

Import

Instantiate a new model