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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pytorch.tar.gz

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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

Image classification is one of the main tasks in the computer vision field. Most image classification architectures consist of convolutional layers besides fully connected ones. Therefore, it is essential to understand the logic behind convolution, how to apply it, and what to expect after this operation is applied. In addition to this operation, there are still some more terms and methodologies to use inside of convolutional layers, and all of these supplementary subjects will be covered during this chapter along with example Python codings to have better intuition. 

We will learn the following in this chapter:

* 2D convolution operations
* Applying padding and stride to 2D convolutions
* Convolutional neural networks
* Comparison between fully and convolutional neural networks
* Pooling types (max pooling, average pooling)
* The difference between 2D and 3D convolution
* Python coding to apply convolution and pooling on 2D matrices
* Using PyTorch built-in functions to create CNN layer
* Creating the first batch of images from scratch
* Using the Matplotlib library to visualize the feature maps coming from CNN layers


Introduction to the Basics of CNNs

Before We Start

Basics of Convolutional Neural Networks

Cats vs Dogs Classification with Convolutional Neural Networks

Popular Neural Network Architectures for Image Classification

Using PyTorch for Image Classification

Model Deployment

Using a PyTorch Model in JavaScript with ONNX

Basics of Object Detection

Two-Stage Object Detection Architectures

One-Stage Object Detection Architectures

YOLOv7 Model Train and Inference on Edge

Conclusion

Appendix

Building a System for Safety Helmet Detection Based on YOLOv5

Introduction to the Basics of CNNs

Chapter review