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

Data augmentation is an important aspect of training machine learning models. This lesson will cover all the data augmentation classes in the PyTorch Image Model framework.

**Data augmentation** is a common technique used in machine learning to increase datasets. Typically, we modify the raw samples in multiple ways to create altered copies.

Let’s look at the following illustrations to understand more about data augmentation:


# Benefits of data augmentation
Training a machine learning model requires a lot of labeled data, and it takes time to collect and label the datasets. The entire process can be tedious and costly.

Therefore, most developers apply data augmentation before training their models. In general, data augmentation provides the following benefits:

* It artificially increases the amount of training data for small datasets.
* It adds a variety of noise to the training data to prevent overfitting.
* It improves the real-world input performance of our model.


> **Note**: Overfitting means that a machine learning model performs exceptionally well on its training data but not on the test data. Hence, it will not do well on unseen data.

# Image transformation
We can create synthetic data for an image classification model by applying a simple transformation. The most common image transformations are:

* Flipping (vertical and horizontal)
* Axis translation (x or y)
* Padding
* Rotation
* Crop
* Brightness and contrast
* Grayscale
* Random noise
* Blur

Let’s review some of the more common image transformations in greater detail.


## Grayscale
**Grayscale** turns color images into shades of gray. Black has the weakest contrast intensity, while white has the strongest.
The following drawing illustrates the differences between a color image and a grayscale image:


Learn to augment datasets when training neural networks.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

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

Deployment

Appendix

Conclusion

Training a Neural Network with Augmentation

Benefits of data augmentation