Training custom machine learning models have become a crucial and powerful aspect of processing, understanding, and monetizing visual data today. Based on Artificial Intelligence (AI), this approach allows the fast and effective categorization of massive amounts of images and videos according to the particular needs of a business. 

In contrast to traditional image classification, custom models provide an important layer of flexibility and adapt to the specifics of your company and your industry. The newly trained models operate based on categories that you set — and can thus analyze and organize your image and video database in the best possible way for your particular case. 

In a nutshell, with custom ai model training, your system is taught to recognize concepts from your visual data — the concepts you care about and that hold potential for your business development. You can set any type of category for the classification process, as long as the categories are clear and don’t overlap with each other. This makes you the architect of your visual data classification.

In the sections below, we’ll go over the basics about machine learning and image categorization, and we’ll delve into how custom ai model training based on machine learning actually works. Read on for the full details. 

What Is AI Machine Learning?

Machine learning is a section of computer science and AI that has been gaining exponential popularity. The main focus is the employment of algorithms through which technology mimics the manner in which human beings learn. So, it means that technology can learn and get better at its job with time. Platforms based on machine learning have the capacity to expand their capabilities and knowledge in unprecedented ways — with precision and accuracy growing with every bit of new data processed. 

In layman’s terms, the machine learning term entails ‘learning by doing.’ In technical terms, machine learning algorithms are taught to classify data and make predictions based on statistical methods. This makes them a powerful tool for unraveling insights from data that would take years to process otherwise. Equipped with these in-depth insights, business leaders and managers can make well-informed decisions that drive business growth and development. 

Some popular uses of machine learning that we are already experiencing — even in daily life — include recommendation engines (like the series and film suggestions you get from your streaming platform) and self-driving motor vehicles. 

1. Machine Learning and Deep Learning

Going a step further in understanding ai machine learning, it’s good to present the term ‘deep learning’, too. Often, machine learning and deep learning are seen as identical, but in fact, deep learning is a sub-field of machine learning. It entails a different learning process and has been referred to as ‘scalable machine learning’ by Lex Fridman

The main difference between the two is that deep learning doesn’t require labeled datasets to learn. It can process data in raw and unstructured form, too. This makes it more independent from human input — and able to process larger amounts of data. Traditional machine learning, on the other hand, needs more actions from humans, and, in particular, more structured sets of data for learning. 

What Is AI Image Categorization?

Image categorization, sometimes also referred to as image classification, is powered by computer vision. It employs machine learning and image processing to sort images and videos by distributing them into categories, which are usually set in advance. It may as well be one of the most significant elements of digital image analysis today.  

Image categorization is widely used in a number of fields. Most notably, it’s the basic tool for automating content moderation online. However, it has numerous other uses, such as database sorting, product discovery in the field of commerce and retail, and asset management in technology and cloud services, among many others. 

In essence, the powerful business use of image categorization is that it allows you to gain control over huge image sets. The engine is taught to discern different categories through a set of local and global visual features. Once it learns them, it’s able to spot the precise category for a new visual that it processes.  

To get an idea of how Imagga’s image categorization engine works, you can check out our Visual Categorization Demo. Our Image Categorization API boasts a couple of powerful features: it’s accurate in its classification; it’s scalable even for enterprises; it’s simple and adaptable for cloud, on-premise, or edge; and it’s customizable to the specific needs of your business. 

How Does AI Image Custom Machine Learning Model Training Work?

Creating custom models is at the heart of performing effective analysis of specific visual data that businesses need today. Pre-set categories don’t always satisfy these needs — and hence, customization becomes key. 

With AI image custom model training, you can specify precisely the categories that your visual content has to be distributed to. The number of categories is limited, theoretically, but in practice, Imagga’s custom training can handle training with tens of thousands of categories.

The custom-trained model, tailored to the specifics of your business, can then be paired with the Image Categorization API, providing you with a powerful tool to classify visual data and maximize its use and impact. 

1. AI Image Custom Machine Learning Model Training Steps

  1. Feeding with datasets: The training datasets have to be inserted into the engine, containing sample visual data for each category. The categories have to be non-overlapping and straightforward. 
  2. AI Model training: Our machine learning experts build a deep learning classification model based on your data and the specific categories that you set. Content that has already been classified is used for the training so that the engine gets accurate principles for categorization. Afterwards, it can process new content and automatically classify it according to the newly created categories. 
  3. Deployment in your systems: The newly trained model is embedded in an API that is seamlessly integrated with the systems and workflows that your business is already using. Through a customer demo, you can evaluate the model in advance. 

PlantSnap Case Study: Image Categorization and Custom Training at Work

A great example of the powerful combination between Imagga’s Image Categorization API and Custom Training is the case study of our work for PlantSnap.

PlantSnap is an app that helps people identify any plant anywhere on the planet. It’s an amazing knowledge base where you can find all kinds of flora species. You just take a photo of the plant you want to identify — and the app provides you with its name and information about it. 

To power up the plant recognition, PlantSnap needed an image categorizer that could handle a massive amount of categories — as there are 320,000 different species worldwide. Most image recognition providers couldn’t address this need, as they couldn’t train such a huge amount of categories, and couldn’t guarantee accuracy decreasing due to the large volume. 

At Imagga, we decided we were up to the challenge. We invested in getting the DGX Station from NVIDIA, powerful hardware that we paired with our outstanding computer vision technology. The result is all that we expected it to be. 

The custom model that we built for PlantSnap is ten times faster in training and doesn’t compromise accuracy. It’s combined with our state-of-the-art image categorization API that boosts high accuracy rates. 

In the deployment process, we successfully resolved another challenge: plant look-alikes. Even plants with similar visual characteristics can now be identified and discerned by Imagga’s custom-trained model. 

As a result of our efforts, Imagga is now a core technology in the PlantSnap app — boosting accurate plant recognition for all types of flora species worldwide: 320,000 plant classifications with a 90% precision rate for the top 5 results of each search. 

Get Started with Your Image Custom AI Model Training 

Due to our extensive experience in providing image recognition and categorization tools for companies from a wide variety of industries, we’re equipped to create powerful, tailor-made solutions.

Want to start building your custom-trained model for image classification? Get in touch to learn more about our solutions from Imagga’s experts. 

Frequently Asked Questions

How do you custom-train image AI models?

Custom training of image AI models relies on machine learning to train the engine to discern visual data from data assets and classify it into custom categories. 

The process involves three steps. The first one includes providing existing datasets and concisely formulated, non-overlapping categories for their classification. Next, deep learning is used to build a classification model with the custom data, based on existing classified content. Then, the model is plugged into an easily embeddable API — which can start processing new visual data. The more images are analyzed and classified, the better the engine becomes at categorization. 

What are the benefits of AI Image Custom Training?

Image custom training based on AI brings unseen advantages to businesses from a number of venues. Companies that operate with huge amounts of visual data, such as user-generated images and videos or crawled visual content, need effective ways to sort and arrange the data. Manual categorization and processing is unthinkable and overwhelming due to the massive quantities. 

This makes machine learning algorithms indispensable help in this process. Image custom training, in particular, offers tailor-made solutions for the specific needs of a company. Instead of using pre-set categories for image classification, it allows a business to take control of its visual datasets by providing it with a deep-learning classification model that is trained to work with its specific categories and data.  The deployment of AI image custom training is robust and can work with any dataset size, while also being flexible and adaptable to cloud or on-premise solutions.