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

What is the default image augmentation in the PyTorch Image Model framework?

What is the default image augmentation in the PyTorch Image Model framework?

Which argument enables training with both Mixup and Cutmix data augmentation?

Which argument enables training with both Mixup and Cutmix data augmentation?

Which is the correct flag for training with `RandAugment` data augmentation?

Which is the correct flag for training with <code>RandAugment</code> data augmentation?

**(True or False)** The `RandomErasing` instances accept either a batch or single image tensor instead of `PIL.Image`.

(True or False) The <code>RandomErasing</code> instances accept either a batch or single image tensor instead of <code>PIL.Image</code>.

Which is the correct flag for training without data augmentation?

Which is the correct flag for training without data augmentation?

Augmentation

Augmentation

Test your knowledge of the data augmentations in the PyTorch Image Model.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Deployment

Appendix

Conclusion

Quiz (Augmentation)