Image classification in machine learning: A full guide (2024) | Viam

Image classification in machine learning: A full guide (2024)

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Sara Gharnit

Senior Content Marketer

Have you ever wondered how reverse image search works or how your smartphone can recognize you and your contacts' faces in a photo?

This is all due to image classification, a critical component of machine learning (ML) that’s shaping the way our technology views the world.

Reverse image search on Google.

In this guide, I’ll walk you through the fundamentals of image classification, how it's applied in various industries, the technology that powers it, and what you need to understand before diving in yourself.

By the end of this blog, you’ll gain:

Let’s dive in!

Machine learning drives image classification

To understand the basics of image classification, it's essential to recognize that it's rooted in ML.

You may already be using ML to streamline tasks and eliminate manual effort. ML empowers computers to learn patterns from data and make decisions autonomously.

Instead of explicitly coding each step, you feed data into an ML model, which then identifies patterns and predicts outcomes.

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Showing the evolution of search results in Netflix, as ML models were implemented.

ML is being used all around us—in personalized recommendations on Netflix, spam filtering in your email, and even in predicting traffic on your GPS. But when it comes to training models to understand and work with images, that's where computer vision (CV) comes into play.

At the core of CV is image classification, which serves as the starting point for many more complex tasks, like object detection and image segmentation, within the field.

What is image classification within machine learning?

Image classification uses ML algorithms to analyze the presence of items in an image and categorize them accordingly.

For instance, an image classification model might look at the below picture, recognize patterns (like the shape of pepperoni and pizza), and find that it belongs to the class “pepperoni_pizza,”—without indicating where the pizza is within the image.

A pepperoni pizza being classified as multiple objects using the Viam app.

Unsupervised learning

Unsupervised learning is used to train a dataset with minimal human interference. That means the model analyzes the images from the dataset and looks for patterns and similarities—colors, textures, shapes—without guidance.

The model then clusters the images into groups, without assigning what the common thread is.

A diagram displaying how unsupervised learning works for the below example.

In unsupervised learning, if images of a fish tank were presented, the algorithm might cluster similar images into groups based on visual similarities, such as grouping images of different fish types. However, it wouldn’t be able to label these clusters with specific names, like 'goldfish' or 'angelfish,' since it doesn't have predefined categories or labels.

Supervised learning

Supervised learning relies heavily on human input early on, requiring each image in the dataset to be manually labeled with the correct classification or category.

For instance, if you're working with images of a fish tank, a person would determine and assign the specific categories for labeling, such as “fish,” “plants,” or “tank accessories.”

Supervised learning models can categorize images in different ways, such as through single-label classification or multi-label classification.

Single-label classification

If you’re working with simple tasks that require only one label per image, this model type is ideal for you. In single-label classification, you’ll only choose one label for each image, even if the image could appropriately be grouped under multiple categories.

This could be especially useful for specific cases, like a smart pet feeder for cats, where the camera only needs to determine whether food is present in the image to dispense more food.

An image of the training data on Viam’s Data tab—showing images with bowls empty and with food.

Multi-label classification

If you’re working with more complex tasks that require multiple labels, multi-label classification is the way to go.

Imagine your smart pet feeder stores food for both dogs and cats, requiring it to distinguish between the two. A multi-label classification model would make this possible, allowing the feeder to dispense treats when either or both animals are detected in the frame.

A smart pet dispenser classifying the image as a dog with 98% accuracy.

Did you know that with Viam, you can train single-label classification models and multi-label classification models, in under an hour?

Image classification vs. object detection

While image classification determines the class of a specific object, object detection takes this a step further by locating the object within the frame. This uses bounding boxes—rectangular boxes defined by x and y coordinates—to determine the position and size of an object within an image in CV.

Showing the difference between image classification and object detection.

An image classification model might detect a cat or dog within an image, but object detection models would be able to find where they are too.

This is especially useful for use cases where it’s necessary to locate the object, such as:

The object detection model showing how it could be used with traffic flow management.

To learn more about object detection, head to our guide.

What are the steps involved in image classification?

While Viam handles much of the heavy lifting for you with its no-code user experience—removing many of the steps—you might still want to understand the process.

Here’s a quick overview of how it works:

1. Dataset curation

Before you can train your model, you’ll need to have a good amount of data that’s fit for training. This means the data will need to be carefully curated, diverse, and aligned to the categories or classes you want the model to learn.

This data sometimes has to be preprocessed manually, which means you’ll need to:

2. Model selection and training the machine learning model

Selecting the right model architecture

After preparing your dataset, choose an appropriate model architecture for image classification, such as a convolutional neural network (CNN).

CNNs are effective for analyzing images because they process them layer by layer, similar to how the human brain recognizes visual patterns. These networks start by identifying simple features like edges and textures, and as they progress through layers, they learn to recognize more complex patterns, ultimately distinguishing between different categories or classes in the images.

A diagram showing how convolutional neural networks (CNNs) operate.

Training the machine learning model

Once the model architecture is defined, it’s time to train the model.

When you train an image classification model, you’re teaching a computer to recognize and categorize different images. You begin by feeding the model a large collection of images, like showing it pictures of different fish species, and labeling each one.

3. Machine learning model deployment

Deploying an image classification model involves taking the trained model and integrating it into a real-world application, where it can start making predictions on new, unseen images.

Once deployed, the model can instantly classify images as they are uploaded or captured, providing real-time results.

Build an image classification model with Viam

With Viam, building an image classification model is straightforward. You don’t need to do any coding—just follow the intuitive steps below, and you can have your model up and running in less than an hour.

Step 1 - Create a well-rounded dataset to train on

To begin training your model, you’ll need a solid dataset with at least 10 images—though we recommend using more for better results. With Viam’s Data Management Service, you’re able to:

Tips from an ML expert on creating a curated dataset

Step 2 - Train the image classification model

How to label images for image classification

Once your images are all populated into Viam’s data tab, it’s time to label the images. All you have to do is click on the image you’re looking to tag, type in or select the image tag, and select the dataset name in the dropdown.

Tips from an ML expert on labeling images

How to train your model

After creating a dataset, all you have to do is:

Step 3 - Deploy your image classification model

Now comes the exciting part: watching your model in action on your machine. With Viam, the deployment process is quick and seamless, requiring as little as 5 minutes to complete.

Tips from an ML expert on deploying a model to your machine

What is an example of image classification?

Now that you know how to create an image classification model, take a look at the following use cases to spark ideas on how you can apply these models in your everyday projects.

Streaming platforms

Streaming platforms use image classification to enhance user experience and streamline content management. By automatically classifying and tagging visual elements in video thumbnails, movie posters, and other media assets, these platforms can efficiently organize and recommend content to users based on their preferences.

E-commerce

If you’ve ever used an image search feature—uploading a photo to find similar products—or browsed a “similar items” section, you’ve benefited from image classification technology.

Security and surveillance

By automatically identifying and categorizing objects, activities, or individuals within video feeds, image classification enables real-time threat detection and response.

Manufacturing

Image classification is a powerful tool for quality control, making sure that products meet the required standards before reaching the market.

Social media

Image classification is essential for managing and curating large amounts of user-generated content. Platforms use image classification to automatically identify and tag visual content, which helps fuel features like photo organization, personalized content recommendations, and targeted advertising.

Wildlife monitoring

Image classification contributes to reducing vessel strikes on wildlife. For example, Viam’s partnership with the Whale and Vessel Safety Taskforce (WAVs), supports the development and implementation of technology to mitigate these risks.

Get started with image classification today

Ready to train and deploy your own image classification model? Give Viam a try—it’s free to use and there’s no-code necessary.