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

**Random Erase** is a special image augmentation technique that works by:
Selecting a region from an image randomly.
Removing the pixels from the region.
Filling the region with random pixels.
It generates training images with various levels of occlusion. As a result, it makes our image classification models robust to occlusions, which improves performance and reduces overfitting.


## The RandomErasing class
The PyTorch Image Model implements this technique under the `RandomErasing` class. We can easily test it with the `timm.data.random_erasing` module.

Let’s look at the following interactive playground:


**Random Erase** is a special image augmentation technique that works by:
Selecting a region from an image randomly.
Removing the pixels from the region.
Filling the region with random pixels.
It generates training images with various levels of occlusion. As a result, it makes our image classification models robust to occlusions, which improves performance and reduces overfitting.


# The RandomErasing class
The PyTorch Image Model implements this technique under the `RandomErasing` class. We can easily test it with the `timm.data.random_erasing` module.

Let’s look at the following interactive playground:


Learn to perform data augmentation with the Random Erase method.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

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

Deployment

Appendix

Conclusion

Random Erase

The RandomErasing class