Ending Note

Explore a comprehensive review of this PyTorch image classification course, covering key learning points such as model creation, augmentation techniques, loss functions, training methods, model conversion to ONNX, and deployment using FastAPI to build APIs for image classification.

We'll cover the following...

Introduction
Basic concepts
Augmentation
Loss
Training
Model conversion
Deployment
Ending note

Here’s a recap of all the lessons in this course:

Introduction

The course started with the fundamental concepts behind image classification. It also highlighted the techniques and metrics for image classification.

Then, it explored the PyTorch Image Model framework, an open source Python library to train image classification models.

Basic concepts

Subsequently, we have learned more about the common built-in functions in the PyTorch Image Model framework. We can use these functions to perform the following ...

1.Introduction

2.Basic Concepts

3.Augmentation

4.Loss

5.Training

6.Model Conversion

Project

7.Deployment

8.Appendix

9.Conclusion

Ending Note

Introduction

Basic concepts