A Gentle Introduction to Image Classification
In this article, we explore the complex subject of image recognition, from understanding the basics of CNNs to implementing your own image classification model.
Created on January 27|Last edited on March 9
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Introduction
In this article, we will explore the world of image classification. We'll start by providing an overview of what image classification is and its various types, such as binary and multi-class classification. We will then delve into the importance of image classification in various fields, such as computer vision, medical imaging, and self-driving cars.
We'll cover the steps involved in building an image classification model, from data preparation and feature extraction to model training and evaluation. We will also discuss some of the best models available for image classification, including CNNs, which have been widely used in recent years and have achieved state-of-the-art performance on several benchmarks.
We'll also touch on some of the latest developments in the field, such as vision transformer, which has shown great performance in image classification tasks.
Whether you are a beginner or an experienced practitioner, this article will provide a comprehensive understanding of image classification and its various aspects.
Here's what we'll be covering:
Table of Contents
IntroductionWhat Is Image Classification?Why Is Image Classification Important? What Are the Types of Image Classification?Is Image Classification Supervised or Unsupervised?What Are the Steps in Image Classification?What Is the Best Model for Image Classification?Why Are CNNs Used for Image Classification? Which CNNs Are Best for Image Classification? What Is SVM Image Classification?An Example of Image Classification Using PyTorchConclusion
What Is Image Classification?
Image classification is a technique that classifies an image into pre-defined categories. Suppose you're using image classification to classify pictures of cars and motorcycles. In this example, the model can be told to classify pictures of cars as one category and pictures of motorcycles as another.
And image classification works better than you might think. In recent years, computers have beaten humans at playing board games like chess, checkers, and Othello. Robots are exploring Mars with their own onboard software. While these feats may seem very different from processing images, they all rely on recognizing patterns.
In the case of image classification, the computer is looking for specific features (such as whether there are wheels or wings on an object) in the picture and using those features to categorize the object. By doing this over and over again, the computer can learn which features make up an object (such as wheels or wings) and then begin looking for those features in new images.
Why Is Image Classification Important?
The importance of image classification is shown in its many uses and practical applications in many fields, such as medicine, autonomous driving, security, education, and more. Some examples of such applications include:
- Object detection - Image classification is used to detect and classify objects in the vehicle's environment, such as other vehicles, pedestrians, traffic signs, and road markings. This information is used to make decisions about the vehicle's speed and trajectory.
- Medical image analysis - Image classification can be used in medical imaging to help detect and diagnose diseases like cancer and heart conditions.
- Web search engines - Google Image Search uses image classification methods to return relevant results when you search for images.
- Facial recognition - Software can be trained using image classification techniques to identify faces and provide information about them (e.g., name, age, gender).
- Improving accessibility for visually impaired individuals - By describing the contents of images in real-time.
What Are the Types of Image Classification?
There are several types of image classification, including:
Binary classification: This is the simplest form of image classification, where the goal is to classify an image as belonging to one of two classes (e.g., "dog" or "not dog").
Multi-class classification: This is a more complex form of image classification, where the goal is to classify an image into one of several classes. For example, an image of a dog could be classified as a Golden Retriever, Labrador Retriever, or Poodle.
Multi-label classification: It's similar to multi-class classification but more flexible, where an image could belong to more than one class. For example, An image of a person wearing a hat and glasses could be labeled as person, hat, and glasses.
Object detection: This is a more complex task than image classification, where the goal is to not only classify the object in an image but also locate where the object is located in the image.
Segmentation: This is also a more complex task than image classification, where the goal is not only to classify the object in an image but also to segment the object within the image. This is useful for tasks such as medical imaging, where the goal is to locate and segment-specific structures within an image.
These are some of the main types of image classification; depending on the specific task and the type of data available, different models may be more suitable.
Is Image Classification Supervised or Unsupervised?
There are many different methods for classifying images. One of those methods is supervised learning, which makes use of manually categorized and labeled images. The machine-learning system then uses these labels to identify images and determine which categories they fall under.
For example, you might have thousands of pictures of your friends' dogs labeled as "dogs" and an equal number of pictures labeled as "not dogs" (cats, birds, trees). But this method is extremely time-consuming and expensive because someone has to label every picture individually. It can only work for specific categories like "dog" and not for more general categories like "mammal."
The other approach to image classification is unsupervised learning, which teaches computers to recognize patterns on their own. Unsupervised methods don't rely on labeled images at all. Instead, they use large sets of unlabeled images.
One example of unsupervised image classification is clustering, where the task is to group similar images together. The algorithm could be trained to extract features from images, such as color histograms or edge detection, and then use these features to group similar images together. Once the images are grouped, an analyst could inspect the groups and assign labels based on the contents of the images.
Remember that image classification using unsupervised learning tends to be less accurate and less robust than supervised methods as it relies on discovering patterns in the data rather than using explicit labels.
What Are the Steps in Image Classification?
Data collection: Collect a dataset of labeled images in the case of supervised learning and unlabeled images in the case of unsupervised learning. For example, you might collect a dataset of thousands of images of animals, each labeled with the species of the animal or with no label at all.
Data preprocessing: This is a crucial step in the data analytics process. It includes operations such as cleaning data, transforming the data, extracting useful information from it, and making it available for downstream analytics. In the case of image classification, this may involve resizing the images, converting them to grayscale, and normalizing the pixel values.
Feature extraction: This involves extracting relevant information from a dataset. The information that is extracted is called a feature, and it is often used as the basis for classification, prediction, or clustering. This may involve techniques such as applying convolutions to the image or histograms of oriented gradients (HOGs).
Model training: This involves training a machine learning algorithm with a known list of examples (data points). It's an iterative process in which an algorithm learns from a set of labeled data and then uses that knowledge to predict the correct label for new, previously unseen data. This may involve using a supervised learning algorithm such as deep neural networks or support vector machines.
Model evaluation: This is the part of the machine learning pipeline where you measure how well your model is performing. This evaluates the performance of the model on a set of test images that were not used during training. This may involve metrics such as accuracy, precision, and recall.
Deployment: This is the final stage of the machine learning pipeline, where the trained model can be put into a production environment. In this environment, the model will be continuously trained on new live data and continuously running predictions on real-world data.
What Is the Best Model for Image Classification?
The "best" model for image classification depends on the specific task and the dataset you are working with. Different models have different strengths and weaknesses, so it's important to choose a model that is well-suited to the task.
In recent years, convolutional neural networks (CNNs) have become the go-to model for image classification tasks. Convolutional or Deep neural networks are layers of interconnected neurons inspired by the neurobiological pathways in our brains.
They're known for their ability to detect patterns, which makes them ideal for building classifiers—systems that learn to distinguish between input categories, like "cats," "dogs," or even "hamburgers."
They're also good at generalizing from previous experiences. They can be trained with just a few examples from each category and then be expected to recognize new instances of those categories (and even new categories) on their own.
With that said, Vision Transformer is yet another popular go-to model for image classification. The key innovation of the Vision Transformer architecture is the use of self-attention mechanisms to process image data.
In a traditional convolutional neural network (CNN), the image is processed through a series of convolutional and pooling layers, which extract features at different scales and locations. However, in a Vision Transformer, the image is first divided into non-overlapping patches. Then the self-attention mechanism is used to compute the relationships between these patches.
Ultimately there are tons of great and effective image classification models. With that said, the best model for image classification is one that gives the most accurate results on your dataset and task. It's essential to try out different models and compare their performance using appropriate evaluation metrics.
Why Are CNNs Used for Image Classification?
The applications for CNNs are vast, and the technology is used in so many ways that you might be surprised to learn that it was originally invented for image classification. Consider this example: if you show a CNN an image of a dog, it will be able to tell you that it's looking at an image of a dog.
You can imagine how this might be useful in some applications—for example, it could help a self-driving car recognize traffic signs as well as other vehicles on the road so that it can respond appropriately.
Why are CNNs called Convolutional Neural Networks? The layers are organized into processing "nodes," where each node applies some specific transformation to the information it receives from its predecessor. In order to know how each node should transform its input, it looks at its neighboring nodes' outputs with a process called convolution—essentially creating a filter that highlights whatever parts the model needs to focus on.
CNNs process images by applying a series of convolutional and pooling layers, which extract features at different scales and locations. The convolutional layers scan the image with a small kernel (also known as a "filter") and apply the same transformation to all parts of the image. The pooling layers reduce the spatial resolution of the image by selecting the maximum or average value of a group of adjacent pixels, known as downsampling. This allows CNNs to recognize patterns in images regardless of their location in the image and reduce the computational cost.
When it comes to CNNs, the more complicated the problem, the more layers of neurons are needed in order to match the number of features needed to solve that problem. Image formation starts with taking light from the real world and turning it into data by capturing it through either one or multiple lenses.
In this case, each pixel of an image file is like a neuron in a CNN. The first layer of neurons in an image recognition CNN would look at all the pixels of one small section of an image. Each pixel will have different values (values can get assigned randomly, or they can be based on how much red or blue there is, how far away an object is from the viewer, etc.).
Which CNNs Are Best for Image Classification?
When choosing a CNN for image classification, there are many things to consider, such as the size of your dataset, how fast you need your model to be, and what kind of accuracy you're looking for. In this article, I'll go over some popular CNN architectures and their characteristics.
Some examples of such architectures include AlexNet, GoogleNet, GoogLeNet, VGGNet, ResNet, and Inception v3. These models have been optimized for accuracy and speed.
AlexNet
AlexNet is one of the most popular CNN architectures out there and is considered by many to be the first Convolutional Neural Network (CNN) to achieve a 1% test error on the ImageNet benchmark. ImageNet is a database containing ~1 million labeled images belonging to ~1000 different classes, developed specifically for computer vision and image processing research. It has been used for many years as a baseline for new architectures and even led to the development of several different models that improve its accuracy.
ResNet
In essence, ResNet is a series of convolutional neural networks (CNNs) designed with the purpose of increasing classification accuracy on ImageNet. The ResNet architecture uses multiple layers to extract progressively more information from the input. It consists of two parts: the residual block and the convolutional block. Both parts are comprised of several layers; however, each part performs distinct functions that, when combined, produce better results than either part alone.
Each layer in the ResNet CNN translates a 2D image into a fixed-dimensional feature representation, called a feature map. Each feature map has roughly half as many dimensions as neurons in that layer. This is done by simply taking a 2D convolution and flattening it into 1D.
What Is SVM Image Classification?
SVM stands for Support Vector Machine, a machine learning algorithm for image classification. It can classify images into different categories and predict which category an image belongs to with high accuracy.
The algorithm works by mapping the data points onto a higher dimensional space, allowing it to make more complex predictions than possible in the lower dimension space in which the data was originally found. This mapping is done through an optimization process that minimizes the difference between the prediction made by the SVM algorithm and the actual class of each data point.
This optimization aims to choose a separating hyperplane that best separates or categorizes all examples into their respective classes. There can be multiple separating hyperplanes in this higher dimensional space, so one hyperplane may separate some examples from their class, and another hyperplane may separate other examples from their class.
If a single hyperplane cannot separate all of the points from their correct class, then no hyperplane will be able to correctly separate them all. The SVM algorithm chooses this separating hyperplane by finding where there is a maximum margin between two classes.
In an SVM model, the decision boundary is defined by the equation w.x + b = 0, where w is the weight vector, x is the input data, and b is the bias term. This boundary separates the data into two regions, one for each class, with the convention that class +1 falls on one side of the boundary, and class -1 falls on the other side.
An Example of Image Classification Using PyTorch
Below is a simple example of an image classification machine learning model using PyTorch. The below code can be modified and used to classify as many classes as possible, for example, to classify images of a dataset containing images of birds, cars, planes, and animals.
Step 1: Import the required libraries, including the torch and torch vision libraries
import torchimport torchvision
Step 2: Define the torch vision model
This step loads a pre-trained ResNet18(mentioned above) model from the torchvision model zoo.
model = torchvision.models.resnet18(pretrained=True)
Step 3: Define the loss function and optimizer for the model
criterion = torch.nn.CrossEntropyLoss()optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
Step 4: Load the data
Insert the path of the data set as the path of the data_dir variable.
Note you can also use the CIFAR-100 dataset, but it will take much more time to train.
data_dir = "path/to/your/data"train_dataset = torchvision.datasets.ImageFolder( root=data_dir, transform=torchvision.transforms.ToTensor())train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=64,shuffle=True,num_workers=4)
Step 5: Train the model for ten epochs
The script then enters a loop for a set number of epochs (10). In each epoch, it loops over the data in the train_loader, passing each batch of images through the model to generate an output.
for epoch in range(10):for images, labels in train_loader:optimizer.zero_grad()output = model(images)loss = criterion(output, labels)loss.backward()optimizer.step()print("Epoch: {} Loss: {:.4f}".format(epoch, loss.item()))
Conclusion
In summary, image classification and computer vision are key areas of research in artificial intelligence and computer science. They have a wide range of applications, including self-driving cars, medical imaging, and security systems.
The field has progressed significantly in recent years due to advancements in deep learning and the availability of large amounts of data. However, there are still challenges to be addressed, such as improving the robustness of models to changes in lighting and viewpoint and increasing the efficiency of algorithms for real-time applications.
The continued research and development in image classification and computer vision will significantly impact how we interact with the world around us in various fields.
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