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Nov 02, 2023

An Introduction to Automated Data Labeling

Note: Thanks to Superb AI for the thought leadership/ Educational article above.

Note: Thanks to Superb AI for the thought leadership/ Educational article above. Superb AI has supported and sponsored this Content.

Artificial intelligence has made waves throughout the past decade, where advancements are showing up in everyday applications. But getting there requires a ton of data, and curating that data and putting it into action requires a lot of work. ML professionals have turned their attention to automated data labeling to implement ML models into real-world applications faster, and it's easy to understand why. Every ML practitioner knows that a successful model requires thousands of data labels. Doing that manually means putting in thousands of hours of work, streamlining strategy, and overseeing each step in the process. For most practitioners, automated data labeling is a no-brainer.

Data labeling in the machine learning pipeline is notorious for having large bottlenecks and slowdowns. It requires an expansive team to individually annotate the important objects in each image, which can sometimes be heavily detailed and time-consuming. Leading a team of labelers often entails ensuring that each person follows the same uniform pattern for every image because any differences can confuse the model. In addition, hiring a team of in-house data labelers is very expensive, and outsourcing leads to miscommunications and errors. If you haven't gathered by now, manual data labeling is tedious. And through each step, data annotation must be overseen by QA professionals, and mistakes must be corrected.

Adding automation to your machine learning project counteracts many of the issues described above. Though no project is entirely without a human-in-the-loop influence, minimizing that need reduces cost, minimizes error, negates the need for outsourcing, and ensures a faster end-to-end operation. Introducing automation into your workflow tackles the bottleneck that has been plaguing ML professionals since the introduction of artificial intelligence.

Automation makes the most sense for certain projects more so than others. When training a model that is reliant on thousands and thousands of data images, it's almost impossible not to automate. Using only humans is a recipe for slowdowns and errors, so the more detail your project entails, the more helpful automation will be. In addition, certain types of labeling projects go hand-and-hand with automation, and implementing this strategy just works.

In machine learning, your models are only as good as their real-world applications. In many instances, that means adapting to changing surroundings and accounting for newer innovations. With this in mind, ML practitioners need to keep updating their models so that they continue to deliver accurate results. Self-driving cars are a prime example of an application that needs continuous revision. Car models change, street signs get updated, and overall surroundings rarely stay the same. Failing to update your model can lead to dangerous errors or lead to accidents in a concept known as model decay.

On the contrary, there are examples when frequent model revision does little to no improvement in model performance. Adding more data to a model necessitates more QA and oversight, as well as additional training. Sometimes it just isn't worth it. On the other hand, if your model degrades with time, fine-tuning a retraining schedule is a part of making sure performance remains optimal. If frequent retraining is a part of your project, then automated labeling is essential.

In addition, automated labeling can be programmed to identify edge cases and calculate confidence levels. When your model is automatically labeling images, identifying the ones that it's less certain about can eliminate a lot of time in the QA process. Superb AI's uncertainty estimation tool, for example, does exactly this. It identifies edge cases prone to error and flags them for a human to inspect. This reduces the amount of human involvement required without eliminating it entirely.

Automated labeling might feel like the best option if it's available to your project type, and the good news is that it likely is. There is a plethora of annotation techniques that go hand-in-hand with a programmatic approach, which we will break down:

The least involved form of labeling for many initiatives is image classification. Annotators will set their projects up so that they can choose from a variety of tags to describe their data. Classification by itself involves selecting a label from a dropdown list; there is no drawing or outlining of objects with a mouse. Classification can be used as an add-on to other annotation projects, or it can stand alone. Once a model's ground truth is created, automation can be added to identify the objects in unclassified data.

Bounding boxes are also a simple annotation type, but that doesn't mean that it isn't highly effective for many applications. Here, an annotator simply clicks and drags their mouse until a box shape forms around the objects being labeled. Annotators should be careful to include all aspects of their labeled objects and avoid including extra space. Following these two rules alone makes forming a ground truth dataset a simple task.

Segmenting an image is a complicated, though necessary, approach to many data labeling projects. A combination of localization and classification, segmentation looks to create a precise outline of specific objects. And there are a series of approaches to doing so. Keypoints, for example, look to connect major points of an object to form a skeletal outline. On the other hand, polygon annotation outlines the image as a whole. Polylines trace linear outlines of an object, such as a crosswalk, and semantic segmentation traces each object's shape and divides them into classes. For more detail, instance segmentation distinguishes between different types of the same object, such as different people, rather than grouping them together as one. Each of these labeling strategies entails a lot of time, meaning that finding a faster way is paramount in pushing your model to market quickly and efficiently.

For many computer vision applications, video is a major component. Surveillance, for instance, now has the capability of identifying suspicious activity such as theft. Learning to understand what stealing looks like involves a well-trained computer vision algorithm. The problem? Video footage contains a lot more detail and information than images do, so labeling is a lot more laborious. Breaking each file down by individual frames is tedious, and isolating them by applicability can take countless hours. Establishing ground truth and then training it to quickly label certain objects and people can therefore be a lifesaver.

Automation is ideal for many scenarios and teams alike, as it streamlines the model-building process and reduces the overall time it takes. However, there are a few instances where programmatic implementation is less efficient.

The initial part of data labeling involves annotating a small subset of data in which to train your model. This part relies entirely on human-in-the-loop intervention to ensure that the initial data is correctly annotated. Here's why: jumping into automation relies on pre-trained datasets. More often than not, outside data is helpful but not perfect for every use case. Implementing an outside dataset into your model can be like fitting a square peg into a round hole, so it's better to work with your own data and have humans do the first leg of the work.

Additionally, building a ground truth dataset also entails that each error in this phase is corrected and guided toward the next phase of labeling. When putting together a model, one must go through each image and ensure that labeling boundaries are tight and that the labels are done correctly. If left to automation in the initial phase, your model will miss some of the important labels and set the stage for an ineffective and inaccurate model.

What's more, working with proprietary information presents its own obstacles. Regulated industries like medicine, finance, and security pose a greater risk if not overseen by humans at least in the initial stage. Training a model to detect certain types of cancer is best left to medical professionals during the initial stage of building a ground truth. With financials, a breach in your model can prove disastrous, especially for accounts holding a lot of wealth. The same is true for government models. Without careful oversight into these models, the potential for harm is much greater.

Some datasets and models are more complex than others, meaning that an automated model is likely to miss the mark on some of the labels. When a model is mostly edge cases, it will likely need human intervention. Automating a model that requires more oversight than not is highly inefficient and cancels out any of its conveniences. In other cases, using people to QA images with lower confidence levels supersedes a model's initial predictions. Working with edge cases requires a fine-toothed comb that often cannot be replaced with machines.

In a short answer: probably. Automation has proven to accelerate the labeling process and help machine learning practitioners expedite their projects. Applications that involve frequent updating are easier to oversee when manual annotation is left out of the equation. In some cases, such as in the medical field, manual labeling takes away precious time from doctors and practitioners who are the only ones qualified to identify, and therefore properly label, abnormal growths or illnesses. This should only be necessary when building your ground truth dataset and during the QA process. The same principle applies to other scenarios as well: borrowing valuable resources like engineers to oversee the manual labeling process just doesn't make sense.

Deciding which approach to take when labeling is entirely dependent on your project and which stage you’re on. If establishing ground truth, then automation is easy at first, but the results are unhelpful in the end. Taking that shortcut does nothing to save you time in the end and only yields an inaccurate model. On the other hand, complicated segmentation tasks only lead to headaches if done manually, and it's an easy solution for less complex projects such as bounding boxes. Automation, then, is key in expediting and updating machine learning projects.

At Superb AI, we specialize in bringing automation to your machine learning and computer vision projects. As we continue to expand our capabilities, you’ll find a well-integrated combination of features that humanizes the data labeling process while also making it seamless and automatic. Schedule a call with our sales team today to get started. Also, subscribe to our newsletter to stay updated on the latest computer vision news and product releases.This article is originally published on the Superb AI blog.

Caroline Lasorsa is a product marketing professional at Superb AI and is based in Boston, Massachusetts. She is an avid reader and learner and has a keen interest in artificial intelligence for medical and healthcare use cases.