With technological advancements, machines can now simulate human intelligence to perform various tasks. This artificial intelligence (AI) exhibited by computer systems is increasingly adopted by businesses worldwide for strategic decision-making.
Given its escalated use, the AI software market was projected to reach $62.5 billion in 2022, an uptick of 21.3% from 2021.
AI helps businesses automate their processes, gain customer insights, uncover new opportunities, and, most importantly, interpret and sort all data types. Harnessing the power of AI, businesses can even interpret and classify their cache of data consisting of images and videos.
But what is AI-powered image classification, and how does it work?
What is AI-based Image Classification, and How Does It Work?
AI-based image classification uses deep learning models to mimic human vision recognition principles to categorize and assign predetermined labels to pixels within digital images and videos. The software can identify people, objects, writing, action, and places in images.
Image classification can be based on a single criterion or multiple criteria based on your segregation needs. If there’s only one label for classification, then it’s known as single-label classification. Similarly, if there are multiple labels for annotations and classification, it’s known as multi-label classification.
But how does it work?
How does an AI image classification model work?
Image data classification is a five-step process:
- Cleaning the data: this stage lets you prep your data. For instance, the inaccuracies in your data, such as duplicate and irrelevant data, are removed, and if some data is missing, that’s also detected. This step is essential to organize all your data.
- Discovering objects in images: in this step, objects are segregated and located within an image.
- Putting a label on the detected objects: the model applies a label on an image after detecting the patterns and classifying its characteristics. This stage is important for the model to gather information and learn from a dataset to label objects accurately. While training your model, don’t forget to give it a substantial amount of data to gorge on.
- Image classification: your model is ready to classify and interpret images based on the predetermined labels.
- Determining the inflow and outflow of data: you can now connect your model to an AI workflow and determine the inflow—how the model will receive new data—and outflow—where will the classified data go—of your data.
Remember, the data fed to the AI model for training will determine your model’s accuracy and reliability in classifying datasets. So ensure that you feed only high-quality data to your model.
But what do you use your image classifier model for?
How can businesses use an image classifier model?
Businesses can use image classification models for a lot of purposes, but the most important ones are:
- Automatically tag product images
- Enhance visual search and detect objects
- Automate product quality inspection
- Interpret and automate content moderation
- Monitor visual data on social media
Image recognition is widely used in the technology, BFSI, automotive, retail, security, and healthcare sectors. Based on its early adoption, the AI image recognition market stood at $2.99 million in 2021 and is estimated to grow at a CAGR of 20.97% between 2022 to 2027.
An image recognition model can be used by practically every business across industries, but building one requires exclusive software tools and huge computational power, which roughly translates to time, effort, technical knowledge, and deep pockets.
So what do you do if you’re not a giant corporation with a dedicated team, money to spend on infrastructure costs for AI models, and would like to speed up your software development? You build an AI image classifier without coding.
How can you build an AI image classifier without coding?
Building a custom AI image classifier without a single line of code isn’t as challenging as it sounds. The infrastructure can be customized as per your business needs and the outcomes you’d like to generate.
Without further ado, let’s get to it!
1. Figure out the problem statement
A solid foundation is necessary to support the load of the entire building. A well-built foundation can serve as the anchor while you focus on building an image classifier without coding to serve your business’s needs.
You can use an image classifier for sorting and categorizing pixelated data in numerous ways and into various classes. So your first step must be to determine its usage and define the problem statement you want to solve.
Having a clear picture of the problem will make it easier for you to deploy an image classifier and automate the process. Too many variables or unclear problem statements can make it difficult to look beyond the initial problem. It’ll remove your focus from the outcome you’d like to generate.
Being clear on the problem to be tackled can make it easier for you to build a no-code image classifier. For instance, your problem statement for building a classifier could be sorting and segregating hundreds or thousands of product images into distinct folders as per their attributes.
Whatever your problem statement, define it first before you begin building an AI-based image classifier.
2. Create and maintain accurate data records
As a business, you collect a lot of data from your customers from various sources and even site visitors when they land on your website. But how do you remain cognizant of your collected data and gain actionable insights?
This is where data discovery comes into the picture. If you’re not aware of data discovery, don’t worry. It’s a process using which businesses can recognize and familiarize themselves with their caches of data and consolidate the data they’ve been collecting for easy access.
Now that you’re familiar with discovery data definition, let’s look at a few benefits that make data discovery and classification tools indisposable, especially while building an image recognition tool:
- They condense all data—external and internal—in a centralized location.
- They help discover patterns and outliers in the data.
- They make data visualization easier.
- They’ll make it simpler for you to comply with data privacy laws.
- They’ll help you figure out what type of sensitive data you store, who has access to it, and how long you store it.
- They’ll let you accommodate data subject access requests (DSARs). If an individual would like to know how their data is being used or processed, or they’d like you to delete them, having data discovery tools at your disposal will let you respond to such support queries timely.
- They can be integrated with your existing systems.
Such tools can also automate your data input process to streamline your image classifier workflow.
3. ‘Train’ your AI image classifier
Until now, the process has been fairly simple. You’ve got a fair idea of the problem statement and the data you’d like to feed your classifier. But what happens next?
Next, you need to train your classifier to recognize and interpret the images or videos you feed. This is an important step and has the potential to make or break your classifier model.
Since you can’t just train your model in the traditional sense of the word, this is where things get a little technical. For instance, your sorted data needs to be analyzed and labeled, then preprocessed for training purposes.
If this seems out of your wheelhouse or something that’d take a lot of time and energy, you can take some external help and lean on tools to make this process easier and less time-consuming.
After successfully training your model, you can export the model file, bundle your data into a single container image, and ensure that your model can maintain its performance level when the volume or context is altered to meet new business goals.
4. Take your model for a test drive
Congratulations! Your model is built and ready for its first assignment.
Since a lot of the processing is done in the backend, you’ll have to evaluate how well your model performs. This stage will let you understand any gaps in the process and if external help is required to optimize the model.
If you do need some outside help, consider building and managing a part-time software developer team.
5. Give your model opportunities to scale up
You’ve got a working model that delivers the desired results. It’s solving your problem statements, but what next?
Think of the model as an intern who joined the company freshly out of college. As the intern gains more experience and undertakes more responsibilities, they keep scaling the organization’s ranks. To grow in their career, the intern has to be supervised, mentored, and helped at various levels.
Similarly, you need to monitor the results of your model from day one to let it grow. Constant human intervention will improve your model’s performance and make it more dependable.
Automate your processes by leveraging your custom image classifier
Your custom AI image classifier will be instrumental in solving your problem statements and automating the entire process while reducing the scope of human errors.
But remember that the model requires sufficient time, energy, infrastructure, and software and hardware knowledge to turn your dreams into reality.
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