Automated Data Labeling vs Manual Data Labeling

Artificial intelligence (AI) continues to push the boundaries of our technological capabilities. One radically transformative AI field is computer vision, where computers and systems construct meaning from the visual information around them. Then, they take action based on their understanding of this information. Much like the anatomical structures of the eye and visual cortex help humans to comprehend their surroundings, data labeling allows computers to quickly analyze and make sense of visual data.

By labeling data, you make objects recognizable to machines. Thus, computer vision relies on data labeling to train systems. Image annotation is an example of this labeling method. Here, an annotator will label images manually, using software and other tools.

There are different approaches to labeling data—each with its particular advantages and drawbacks. In this article, we’ll discuss the pros and cons of automatic vs manual labeling to help you determine the best method for dealing with those challenging data sets.

What is Data Labeling?

Data labeling—or data annotation—is the process of classifying raw data, such as images, text files, or videos, by adding meaningful labels. These labels, also known as tags or metadata, give the data context. This context is what helps a machine learning model to learn.

The labels describe the features of the data that are relevant to the machine learning model and its aims. The model uses this to synthesize the information so that it can make predictions and execute tasks.

For instance, you could task an AI model with recognizing a type of animal from a picture. This would involve training the model using images with labels such as “cat,” “dog,” “pig,” etc., or with more sophisticated tags that define the visual aspects of the image.

Different Approaches to Data Labeling

There are a number of different ways to perform data labeling. Your approach depends on the size of the data set, the complexity of the data, and the resources available to you—financial and otherwise. Here’s a brief overview of the various approaches.

In-House

In-house labeling is a manual process taken on by the company’s staff. If an organization has frequent data labeling needs, they may consider allocating both financial and human resources for this purpose. This could include hiring specialists to manually undertake this work as part of their job responsibility.

Aside from the financial cost of keeping things in-house, it can also be quite a time-consuming exercise. That said, it’s great for ensuring quality and accuracy.

Crowdsourced

Crowdsourcing engages a crowd or group online for data labeling purposes. You can find these groups on specific crowdsourcing websites. Here, multiple individuals (i.e., paid contractors) work together to complete the necessary labeling tasks.

For instance, various individuals will tag the same image with several different labels, making the task more manageable for the contractors—and ultimately faster as well. While this method will speed up data annotation services, the quality of the data may suffer.

Outsourced

Similar to crowdsourcing, outsourcing uses resources external to the organization to annotate data. Outsourcing, however, typically looks to individuals with a higher level of expertise to take on data labeling.

These contractors are responsible for fully annotating all data sets assigned to them. Although this is a quick method of annotating data, businesses may still run into issues with quality.

Automated

Automated data labeling involves the use of software and other data labeling tools. Experts can program AI to assign labels to raw data sets automatically. They can then monitor and analyze these tags after the fact to ensure accuracy and improve the automatic labeling model.

Automated vs Manual Data Labeling

The goal of data labeling—which we use here interchangeably with the term “image annotation”—is to make data and digital content recognizable to machines. As discussed previously, machines then use this information to execute tasks.

All of the approaches to data labeling described above essentially fall into two categories: automated data labeling and manual data labeling. Although all labeling projects will involve some level of manual work, there are a variety of automated labeling techniques that can make these tasks much easier to implement.

Both labeling strategies have advantages and disadvantages—many of which can be addressed with human-in-the-loop (HITL) labeling.

Manual Data Labeling

Manual data labeling involves human annotators identifying objects in images or video frames. These annotators utilize thousands of images (or more) and tag them to gather comprehensive, high-quality data for training AI.

Annotators assign labels based on the needs of the project. This may involve descriptive visual tags to classify images or the use of semantic segmentation to distinguish different objects within an image. Annotators may also create bounding boxes within an image so that AI will avoid specific parts of that image. Alternatively, they may plot the shapes within an image to create an outline.

Manual annotation is a lengthy process. This is one of its biggest drawbacks. Though it guarantees the highest accuracy, it also takes the longest time to complete. Human data labeling is most effective as a solution to challenging annotation tasks.

Automated Data Labeling

Manual data labeling requires a great deal of time and effort, and it can be a daunting task. This is where automated data labeling can make substantial improvements to efficiency.

With automated labeling, experts create AI systems to annotate raw data. This requires them to utilize heuristic techniques, machine learning models, or a combination of the two.

As the AI labels the data, it also learns and improves its labeling capabilities. In the heuristic method, a single set of data is subjected to predetermined rules or conditions in order to validate a particular label. While the circumstances are created by humans, the AI handles the actual labor of the labeling project.

Despite its many benefits—including speed—this approach also has significant pitfalls. Without supervision, an AI system may make errors, such as incorrectly labeling data. Further, if left to their own devices, AI systems are susceptible to developing bad habits that may throw off your labeling efforts.

Human-in-the-Loop Labeling

A hybrid strategy is able to offer some of the best results. By combining automated labeling with manual labeling, you can reduce the downsides of both methods—and boost your efficiency. This hybrid strategy is called human-in-the-loop (HITL) labeling.

Typically, in HITL labeling, humans label data in order to train an AI model. This teaches the model to label data on its own. Humans work alongside the model and tune it according to the results. Both the successes and failures of the model are useful for further training. In this scenario, the bulk of labeling work is handled by the AI systems, with humans validating and improving the results.

Human-in-the-loop labeling is widely believed to be the best strategy for organizations. It speeds up processes and cuts down on human resource utilization, while putting humans at the helm to safeguard quality and accuracy.

Data Labeling Tools

When choosing a data labeling tool, it’s important to evaluate it carefully. The right labeling tools, along with a workforce of annotators who are professionally managed and trained, can be a powerful combination. This will help you to secure the best quality data sets for machine learning.

First, think about the format (or file type) you are working with. The right labeling tool should be able to produce annotations in your desired format. This keeps the process as simple and as efficient as possible.

Next, assess the usability of the tool. On a fundamental level, you will need to confirm that the tool supports your application. Further, you should choose a tool that your designated workforce can easily learn and use. Examine the skills of your team and make sure the tool you choose matches these skill sets.

Data Annotation Services

Data annotation is an essential tool for driving business innovation. But what exactly should you be looking in a platform or service for your next project? Here are some key factors to consider.

Does the tool fit the task? The specifics of your labeling tasks will determine which tool you choose. Consider both your current labeling projects and ones you may encounter in the future to make sure that the tool can serve as a long-term solution.

How does the tool ensure quality? Choose a tool with good quality assurance processes built in, making it easy for project managers to ensure quality. Furthermore, it should be adequately trained and tested.

Does the tool have an integrated management system? Data labeling projects need to be managed. As such, you need a tool with an integrated management system to help track the data and the project overall. Consider how your tool of choice facilitates task assignment, commenting, edits, and other communication needs.

Does the tool include user guides and troubleshooting assistance? As with any work tool, there will be a learning curve to figuring it out. Because of this, you should make sure the tool you choose has proper, thorough documentation to streamline the learning process. Further, as you may run into technical issues at some point, also consider the level of troubleshooting assistance you would like.

Is the tool private and secure? This is a vital factor, as the tool needs to be secure for both organizational data and personal login credentials. You should also consider where and how data is hosted on the tool.

The help of a data labeling service that can tackle your specific concerns is a good way to check that your tool is the perfect fit.

Data Labeling Solutions

Today, data labeling is integral to an increasing number of business operations across many different industries. These range from energy to manufacturing and beyond, where computer vision and other AI disciplines are helping to companies to grow their market share.

Looking ahead, organizations will rely more and more on AI and machine learning—and it all starts with data labeling.

Data labeling allows systems to understand and analyze data such as images and videos. There are a number of approaches to this task, all falling somewhere on the spectrum of in-house to external and manual to automated.

Manual data labeling is typically great for accuracy and quality, while automated data labeling is best for speed and minimizing labor costs. We believe that the best approach lies in a mixed strategy in which humans work alongside AI.

Why Choose Helpware?

There are many options for data labeling solutions that can be used for a human-in-the-loop approach. Here at Helpware, we make it easy to find the tools that are the best fit for your organization. Helpware is a secure solution that provides a quality-first, human-labeling data platform and modern technology for anyone looking to optimize their data labeling operations.

For further information on how you can level-up your operations, contact us to discuss your needs and find the best solution.