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

# Image classification vs. object detection

While the image classification problem focuses on classifying the images, in object detection, there might be more than one object we're searching for. Our task is to find all of them placed in the most appropriate boxes with their belonging classes. Therefore, our goal is relatively more complex than image classification. We have to first detect the objects by locating them with bounding boxes, then classify them to decide which class this box (thing) belongs to. 


To sum up:

* **Image classification:** It predicts the class of an image.

* **Object localization:** It locates the presence of objects in an image and indicates their location with a bounding box.


* **Object detection:** It locates the presence of objects with a bounding box and detects the classes of the located objects in these boxes.


Since object detection involves providing both the location and the class of an object instance, we can consider it a sum of these two tasks. Depending on the object detection architecture, both tasks can be solved simultaneously or sequentially. We need to know that both tasks should be solved in an object detection model.



The image classification task involves classifying images. However, the task seems more complex now, with multiple issues to solve. We need to locate objects with bounding boxes, which means we need to predict four coordinates of this box. We then need to apply classification to decide which class belongs to this object. The localization task is not a classification problem but a regression problem!


# Classification vs. regression

When working with classification and regression tasks, it is always good to know the difference between them. Let's take a look at the regression types and the relationship between regression and classification:

## Linear regression 

**Linear regression** makes predictions for continuous or numeric variables. For example, predicting a house's rent price according to its location, size, and how old it is. It’s not a classification but a continuous numerical answer that the model has to find. 

One of the equations for a simple linear regression task is $y=mx$, and depending on the range of $x$ (for example, $[-∞, +∞]$), we have a range of $[-∞, +∞]$ for $y$. We saw some linear functions used as activation in our hidden layers, right? ReLU was one of them, and since its wide range, linear regression was a good choice for carrying our signal through the layers.

## Logistic regression (sigmoid regression)

Considering we have two classes and want to make predictions to classify our $x$, whether in class $a$ or $b$. The range of linear regression is pretty broad, right? If we had a range of $[0,1]$, we could easily classify our prediction as $class \ a \ if \ y > 0.5$ and $class \ b \ if \ y < 0.5$. Using logistic regression, also called sigmoid regression, we could limit our prediction range between $0$ and $1$ and apply our binary classification task. 

The sigmoid function is an impaired activation function for backpropagation. Still, regarding the final layer, our task determines which part is better, and if it's a binary classification, sigmoid is an obvious choice.

## Classification (softmax regression)

As mentioned, the softmax function converts the final predictions to probabilities. Therefore, we obtain possibilities for each class, and the sum of all the probabilities equals $1$. Ultimately, we take the class with the maximum probability as the predicted class.  



Learn about the connection between image classification and object detection.


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

From Image Classification to Object Detection