What Is Image Classification? A Comprehensive Overview for Developers & Product Teams
Image classification is a vital technology in today’s data-driven world. But if the term is new to you, you may find yourself wondering, just what is image classification? In short, image classification is the automated categorization of images into predefined classes or labels, enabling computers to interpret visual data quickly.
Although it’s often confused with terms like object detection, it’s not the same thing. While object detection involves identifying and locating specific instances of objects within images, image classification assigns labels to entire images.
In this guide, we provide insights into how image classification works and discuss how you can leverage it to enhance your company’s competitive edge.
Table of Contents
- Importance of image classification for product teams and developers
- How does image classification work?
- Types of image classification
- Applications of image classification
- Image classification models, algorithms, and techniques
- Image classification tools: AutoML for image classification
- Efficient, accurate, and powerful image classification
Importance of image classification for product teams and developers
As companies generate and consume increasing amounts of visual data, the importance of image classification has become more evident. Manually organizing and extracting meaningful insights from a significant amount of visual data is impractical and time-consuming. As a result, businesses need to automate how they manage, interpret, and moderate visual data.
By harnessing advanced image classification techniques, companies can efficiently classify images, providing opportunities to improve their internal processes, create enhanced customer features, and provide improved content moderation services.
1. Improve internal processes
Image classification allows businesses to streamline their data management processes. Organizations can efficiently organize and retrieve visual content by automatically categorizing images into predefined classes or labels, saving time and resources. Image classification can also promote effective decision-making by allowing companies to quickly sort through and assess large quantities of visual data.
For example, retail businesses can enhance inventory management by automatically classifying products based on images. Inventory management driven by image classification enables them to efficiently maintain precise stock records, identify low-stock items, and track product variations. This approach helps optimize supply chain operations while decreasing the risk of overstocking or understocking. Similarly, environmental organizations can use image classification to evaluate ecosystem health, monitor landscape changes, and track wildlife populations. This process generates valuable information that policy-makers can use to formulate conversation strategies and make informed policy decisions.
2. Enhance customer features
Additionally, by harnessing advanced image classification techniques, your company can unlock opportunities to build features into your products that enhance your customer experience. If done well, these features can create a competitive edge for your business.
Take, for instance, Gardyn, a company that sells smart indoor gardening systems. Gardyn utilizes image classification to analyze its customers’ plant health, helping them maintain thriving gardens. When the image classifier detects signs of stress or disease in their customers’ plants, it alerts the owner, suggesting specific actions for care. This not only simplifies gardening for users but also ensures healthy plant growth and longevity.
E-commerce platforms also regularly leverage image classification to provide improved customer features. For example, Amazon uses this technology in its Amazon lens feature, which enables a visually-driven search. Customers can snap or upload a picture of a product they like, and using image classification, Amazon can quickly identify a similar item, allowing users to conveniently locate and purchase it. Amazon’s image search not only streamlines the shopping process but also sets Amazon apart as a leader in providing innovative solutions that enhance customer experiences.
In both cases, the ability to classify images has not only addressed a customer need but has also created a competitive advantage for each business in their respective markets.
3. Moderate content
For products with user-generated or submitted content, image classification can be critical to maintaining a safe online community. By automatically detecting and flagging inappropriate or harmful images, you can ensure your platform remains welcoming and secure for users. Strong content moderation practices foster trust and encourage user participation. For example, Pet Media Group uses image classification to moderate pet listings on its sites and enforce the company’s strict animal welfare policies.
How does image classification work?
Understanding how image classification works is pivotal for comprehending its significance in modern technology. This process comprises several crucial steps:
1. Gather training images: First, you must assemble a comprehensive dataset of images. These images serve as the raw material from which the classifier learns to recognize different categories.
2. Add training labels: Typically, you will need to label each image in the dataset with the category it represents. For example, if you’re developing an image classifier to identify various dog breeds, each dog image in your dataset is labeled with its respective breed.
3. Preprocessing: Before the actual training begins, the images undergo preprocessing. This step typically includes resizing images to a uniform size, normalizing pixel values, and augmenting the dataset with variations like rotations or brightness adjustments. These preparations ensure the model’s effectiveness.
4. Training: Once your input data is ready, you can train image classification models using machine learning techniques. While you can start training from scratch, it’s increasingly common to leverage transfer learning. Transfer learning involves taking pre-trained models, such as those developed on extensive image datasets like ImageNet, and fine-tuning them to recognize your specific categories. This approach often yields superior results, given that the model already possesses a wealth of general image recognition knowledge.
5. Evaluating the model: Following training, you can assess the model’s performance using a separate dataset not utilized during training. This evaluation measures how accurately the model can classify images. Metrics like accuracy, precision, recall, and F1 score are commonly used to gauge performance.
6. Inference with new images: Once the model is trained and evaluated, it’s ready for practical deployment. When presented with new, unlabeled images, the model can classify them into the predefined categories it has learned during training.
Traditionally, the domain of machine learning (ML) was exclusive to data scientists and ML experts who had specialized knowledge to train and deploy ML models. However, a new segment of ML tools, AutoML platforms, has emerged to enable individuals without ML expertise to train models from labeled data and automate the intricate processes involved. These innovative tools have democratized machine learning, making it accessible to a broader audience.
The best image classification platforms make integrating an image classifier into your product straightforward. The process involves annotating your dataset with labels and deploying it through an API into your systems. This revolutionary approach eliminates the complexity and barriers that once held businesses without ML teams back from utilizing image classification to enhance their products and services. As a result, it’s key that your AutoML platform includes must-have features, including a data engine and active learning.
Types of image classification
There are three primary types of image classification: binary, multi-class, and multi-label. Each of these types serves distinct purposes, and selecting the appropriate one is pivotal for the success of your image classification project. When building an image classifier, it’s important to understand which type of classification you need to use. For a deeper dive, refer to our comprehensive guide on multi-class classification vs. multi-label classification.
Binary classification serves as the foundational block of image classification. In this approach, images are categorized into one of two exclusive classes, often representing a yes/no or true/false scenario. For example, consider the use of image classification in medical diagnostics. In cancer detection, binary classification might be used to categorize medical images into two exclusive classes: benign or malignant.
Multi-class classification takes image categorization a step further by enabling images to be sorted into a single label or category from a set containing three or more options. Imagine an automated vehicle recognition system. It operates as a multi-class classifier, classifying images of vehicles into various categories like cars, trucks, bicycles, and motorcycles. Each image is assigned to one specific category, facilitating efficient traffic monitoring.
Multi-label classification brings versatility to image classification by allowing images to belong to multiple categories concurrently. In the realm of content tagging for social media posts, multi-label classification proves its worth. An image posted on a social platform can be tagged with several labels, such as beach, sunset, and friends, all simultaneously. This approach enriches content discovery and user engagement.
Applications of image classification
Image classification has various practical applications across a wide range of industries. Here are a few specific use cases where image classification plays a pivotal role:
Label products for online shopping
Companies can leverage image classification to identify, sort, and label their products, streamlining inventory management and customer interactions. For example, car dealerships can use this technology to detect the make and model of a car they need to add to their online inventory. And online retailers can use it to label clothing items with their colors, style, and fabric, supporting a seamless shopping experience.
Manage digital asset inventory
If your company has an extensive library of digital assets, it’s critical to have an effective way to store, organize, find, and share them. Image classification can support strong digital asset management (DAM) by quickly and accurately labeling digital assets with their respective labels. Custom classifiers trained on your own data that use labels relevant to your business can be game changers for the DAM industry, providing a level of specificity that pre-trained classifiers can’t offer.
Detect AI-generated images
The improvements we’ve seen in AI-generated content are stunning but also present growing concerns. Identifying authentic and trustworthy images from AI-generated ones can be challenging, which is especially important in specific scenarios like news reporting. To solve this, many organizations are turning to AI to detect AI. You can now train an image classification function to detect fake images with impressive accuracy.
Flag inappropriate content on your platform
Image classification is integral to content moderation and inappropriate image detection, helping ensure online platforms remain safe and free from inappropriate content. As mentioned earlier, Pet Media Group uses image classification to do just that: monitoring pet listings that violate the company’s animal welfare policies by detecting pictures of dogs with cropped ears or images with emojis overlaid, which often hide surgical modifications that PMG doesn’t allow.
Identify your company logo
Your brand is one of your most valuable assets as a company, so monitoring how people use your logo is critical. You can use image classification to automatically detect and locate instances of your company’s logo across various digital platforms and content. This can help you maintain the integrity of your brand by spotting inappropriate uses of your logo while also providing valuable insights into brand visibility and customer sentiment.
Detect objects in remote sensing data
Remote sensors on satellites and aircraft collect a wide range of visual data that can be analyzed and interpreted for various use cases. Remote sensing data paired with image classification can detect and analyze objects or phenomena in this satellite imagery. For example, a remote sensing application could leverage image classification to identify icebergs in satellite images, aiding in planning safe ship routes in icy waters.
Custom use cases
The beauty of custom image classification is that the use cases are nearly limitless. If you have image data and labels that you want to sort that data into, chances are you can create an image classifier that will add speed and accuracy to your product or operations. These five image classification examples, our pre-trained functions, and customer stories provide some good inspiration for brainstorming even more potential use cases.
Image classification models, algorithms, and techniques
Like all types of machine learning, image classification employs a range of sophisticated models, algorithms, and techniques. Recent advancements in technology have led to the democratization of machine learning, making it so developers and product teams can incorporate image classification into their products without having to become data scientists or ML experts themselves.
While you do not need a comprehensive understanding of these methods to perform image classification, it’s helpful to understand how image classification works on a fundamental level. This section introduces you to some key concepts in image classification, including model architectures and training techniques.
At the heart of image classification are the model architectures, which define the blueprint for how a machine learning system processes images to make predictions. One of the most pivotal breakthroughs in recent years has been the rise of Convolutional Neural Networks (CNNs). CNNs have revolutionized image classification by mimicking the human visual system, allowing them to learn hierarchical features from images. They consist of convolutional layers, pooling layers, and fully connected layers, making them exceptionally suited for tasks like image recognition. As a result, image classification using CNNs has become increasingly common.
In addition to CNNs, the emergence of vision transformers has brought a new dimension to image classification. Vision transformers apply the self-attention mechanism, originally designed for natural language processing, to images. The self-attention mechanism allows a model to weigh the importance of elements in a sequence, such as pixels in an image, and to capture relationships between these elements. This approach has shown remarkable performance, enabling models like the Vision Transformer (ViT) to compete with CNNs in image classification tasks.
Successful image classification hinges on the training techniques employed to fine-tune models and optimize their performance. Transfer learning is a dominant strategy in this domain. It involves leveraging pre-trained models, such as VGG, ResNet, or Inception, which have learned rich features from massive datasets like ImageNet. By transferring this knowledge to a new image classification task, developers can achieve impressive results with relatively small datasets, significantly reducing the need for vast amounts of labeled training data.
Moreover, techniques like Support Vector Machines (SVMs), Random Forests, logistic regression, k-nearest neighbors (k-NN), and fine-tuning offer additional options for refining image classification models. These techniques allow practitioners to adapt and customize pre-existing models to specific use cases, tailoring their performance and accuracy.
Difference between supervised and unsupervised classification
In addition to having a working understanding of the most popular image classification algorithms and models, it’s also important to understand the difference between supervised and unsupervised classification.
Supervised classification is a method where a machine learning model is trained on a labeled dataset, meaning that each image in the dataset is assigned a predefined category or class. You can use this approach when you want the model to learn patterns and relationships between features in the labeled data, allowing it to generalize and make predictions on new, unlabeled data. It is highly beneficial when you have a clear understanding of the categories you want to classify images into. For example, in medical imaging, you can train a supervised classification model to identify different types of tumors based on a labeled dataset of medical images. This method allows for precise categorization and is particularly useful when you have well-defined classes and a substantial amount of labeled data.
In contrast, unsupervised classification involves grouping images without predefined categories or labels. It’s a valuable technique when you have a large dataset and want to discover hidden patterns or group similar images together based on their inherent similarities. For example, you could use unsupervised classification to cluster photos on a social media platform. Without any prior labeling, the system can group photos with similar content or themes, making it easier for users to navigate and find related content. Unsupervised classification is versatile and can reveal insights from unstructured image data, but it may not provide as precise categorization as supervised methods due to the absence of predefined classes.
Popular image classification frameworks
Machine learning (ML) frameworks are essential software tools and libraries that provide a structured environment for developing, training, and deploying machine learning models. These frameworks offer a range of functionalities, including data preprocessing, model building, optimization, and evaluation. They simplify the implementation of complex algorithms and allow researchers and developers to efficiently work with various machine learning techniques and neural network architectures.
For those with programming experience, Python is one of the most popular languages for machine learning tasks. Some popular machine learning frameworks for image classification using Python include Keras, PyTorch, Tensorflow, and SciKit Learn. While these frameworks are useful for developers with machine learning domain expertise, there are no-code or low-code APIs available that are more user-friendly and simplify the image classification process.
Image classification tools: AutoML for image classification
With image classification, the right tool can make all the difference for software engineers, product owners, and tech leaders looking to streamline their workflow without diving into the complexities of machine learning. Here, we guide you through a selection of image classification tools designed to simplify your journey. Our computer vision SaaS landscape review offers a deeper look into these top players, and our image classification benchmark offers additional side-by-side comparisons.
Nyckel stands out as a quick and accurate machine learning API tailored for developers and product teams lacking in-house ML expertise. Beyond image classification, Nyckel extends its capabilities to text and tabular classification, tagging, and search. With its user-friendly drag-and-drop interface and rapid setup, Nyckel allows you to import and label images in seconds. The model updates on the fly as new data is annotated, making it a top choice for those seeking lightning-fast machine learning without the need for deep ML knowledge.
Google Vertex AI
Google Vertex AI is Google’s unified data and AI platform. While powerful, it comes with a steep learning curve. Although it provides solid accuracy, the initial setup and labeling process can be complex. Still, its integration with the broader Google ecosystem can be advantageous for those willing to invest time in the learning curve.
Amazon Rekognition Custom Labels
Amazon Rekognition Custom Labels offers customizable computer vision APIs. While the setup process can be tedious, the platform’s interface simplifies the training process. However, it requires coding for deployment, so it may not be suitable for those without coding expertise.
Hugging Face AutoTrain
Hugging Face AutoTrain allows you to build image classification models using transformer-based architectures. It offers pre-trained models and fine-tuning options, making it suitable for various applications. However, it’s neither the fastest nor cost-effective option in this space.
Ximilar specializes in computer vision, providing user-friendly tools and pre-trained models for specific use cases. Its drag-and-drop interface streamlines data import and labeling, while model training takes around 20 minutes. Ximilar offers accurate results and an “Explain” feature that overlays heatmaps on images to visualize AI weightings.
Roboflow focuses on computer vision, offering a drag-and-drop interface for image and video upload. It provides training options for both speed and accuracy, with the ability to choose public checkpoints. Roboflow’s model training is efficient, taking just six minutes in our test, and offers various customization options through subscription plans.
Hasty is a data-centric ML platform specializing in computer vision. While it markets image classification, our test revealed a preference for object detection. The annotation process can be time-consuming, and Hasty’s model accuracy may be limited in some cases.
Levity automates tasks like tagging and classifying images, with an intuitive user interface. Model training is quick, but its core ML performance may not match some competitors.
Clarifai offers a broad ML service with a mix of ease-of-use and complexity. While the data upload process is straightforward, some terminology and functionality can be confusing.
Azure Custom Vision
Azure Custom Vision is Microsoft’s computer vision solution within Azure Cognitive Services. While it delivers accurate results, the setup process can be frustrating. Once past the initial hurdles, training and testing the model is efficient, and Azure Custom Vision offers extensive resources for support.
When choosing the best image classification tool for you, we recommend reviewing each platform to see how many of these nine features they include. Consider factors like function type support, ease of use, model accuracy, latency, pricing transparency, and developer experience. Your ideal solution will depend on your specific needs, whether it’s rapid model training, top-notch accuracy, low latency, or a smooth developer experience. To optimize your image classification workflow, be sure to make an informed choice that aligns with your priorities.
Efficient, accurate, and powerful image classification
Image classification is a vital technology that offers numerous benefits to software engineers, product owners, and tech leaders in today’s data-driven business environment. It provides a solution for efficiently managing and interpreting the ever-increasing volume of visual data, automating processes, and enhancing decision-making.
Nyckel’s image classification capabilities empower businesses by simplifying the complex world of machine learning and enabling users to harness the power of image recognition. Nyckel’s real use cases from satisfied customers highlight the practicality and effectiveness of our image classification solutions. Whether it’s streamlining inventory management for car dealerships, enhancing customer convenience in food ordering, or automating content moderation for online platforms, Nyckel has demonstrated its value across diverse industries.