Gain insights into using PyTorch for image classification and detection, delve into model implementation, and explore deployment on edge devices with ONNX and OpenVINO.

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Image classification and object detection have gained widespread use in recent years. Content categorization and monitoring, disease diagnosis from medical images, identifying terrain in satellite images, and detecting road elements for self-driving cars are classification problems at their core. PyTorch is a popular framework for these tasks—offering a useful mix of user-friendliness, deep learning functionalities, customization, and optimization.

In this course, you will cover the fundamentals of classification and object detection models and apply them to actual datasets using PyTorch. You’ll learn popular architectures and how to implement and fine-tune them for better results. Finally, you’ll learn to convert models to ONNX and OpenVINO to deploy in edge devices.

By the end of this course, you will have acquired the necessary skills to be able to use PyTorch for image identification and object detection in real-world applications

Using PyTorch for Image Classification and Object Detection

# Chapter review

In this chapter, we will explore the meaning of image classification and learn how the most popular architectures classify images. 

1. First, we will check the differences and use cases between fully connected and convolutional neural networks.
2. Next, we will learn how to mix them to obtain image classification architectures.
3. Lastly, we will examine the most successful techniques from older to newer versions with the novelties that they brought us.


We will explain the main problems in deep neural network training, network size, and computational cost calculation to elaborate the intuition about the logic of these novelties.



Learn about image classification tasks and briefly compare fully connected and convolutional neural networks.

Introduction to Architectures for Image Classification

Before We Start

Basics of Convolutional Neural Networks

Popular Neural Network Architectures for Image Classification

Using PyTorch for Image Classification

Model Deployment

Basics of Object Detection

Two-Stage Object Detection Architectures

One-Stage Object Detection Architectures

YOLOv7 Model Train and Inference on Edge

Conclusion

Appendix

Introduction to Architectures for Image Classification

Chapter review