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

wfngenv.tar.gz

wfngenv

terminalenv

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

By default, the PyTorch Image Model framework will apply the `RandomResizedCropAndInterpolation` data augmentation during training.

As the name implies, it will randomly crop or resize the input image. It also changes the aspect ratio of the image randomly.

The `RandomResizedCropAndInterpolation` data augmentation works very well with most datasets. It helps to boost the performance of our image classification model.

## Random resized crop and interpolation
The `RandomResizedCropAndInterpolation` class is part of the `timm.data.transforms` module. Since it accepts a `PIL.Image` as input, we can load our image as usual and implement the remaining code like this:


By default, the PyTorch Image Model framework will apply the `RandomResizedCropAndInterpolation` data augmentation during training.

As the name implies, it will randomly crop or resize the input image. It also changes the aspect ratio of the image randomly.

The `RandomResizedCropAndInterpolation` data augmentation works very well with most datasets. It helps to boost the performance of our image classification model.

# Random resized crop and interpolation
The `RandomResizedCropAndInterpolation` class is part of the `timm.data.transforms` module. Since it accepts a `PIL.Image` as input, we can load our image as usual and implement the remaining code like this:


Learn to perform data augmentation with the random resized crop and interpolation techniques.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Deployment

Appendix

Conclusion

Random Resized Crop and Interpolation

Random resized crop and interpolation