LTS QA

Automated Data Labeling is a new feature that is currently being constantly mentioned among Data annotation trends, and some even deem it the solution for the time-consuming and resource-consuming casual manual annotation.

As the Manual Data Labeling – aka Manual Data Annotation takes hours to annotate one dataset, the Automated data labeling technology now proposes a simpler, faster and more advanced way of processing data, through the use of AI itself.

 

How we normally handle dataset

The most common and simplest approach to data labeling is, of course, a fully manual one. A human user is presented with a series of raw, unlabeled data (such as images or videos), and is tasked with labeling it according to a set of rules.

For example, when processing image data for machine learning, the most common types of annotations are classification tags, bounding boxes, polygon segmentation, and key points.

 

 

Classification tags, which are the easiest and cheapest annotation, may take as little as a few seconds whereas fine-grained polygon segmentation could take a few minutes per each instance of objects.

In order to calculate the impact of AI automation on data labeling times, let’s assume that it takes a user 10 seconds to draw a bounding box around an object, and select the object class from a given list.

In this case, provided with a typical dataset with 100,000 images and 5 objects per image, annotators would have to spend 1,500 man-hours to complete the annotation process. This eventually would cost approximately $10,000 just for data labeling. 

The price of $10.000 is only for data labeling. For annotation project managers, AI data processing takes more than that. To ensure the high quality of the training data, they are compelled to add other layers of quality control and quality assurance. This helps manually verify and review each piece of labeled data, but it would be very costly. Moreover, the quality control and quality assurance staff must be trained of the sample output so that they understand what is required in the outcome of the annotation projects, thereby increasing the labeling costs by about 10%.

 

 

Some annotation project managers might choose consensus-based quality control. By implementing this method, the whole annotation project goes through multiple annotations. The same piece of data is annotated multiple times, and the results are consolidated and compared for quality control purposes. With this method, the amount of time and money is proportional to the number of annotators working on the same task. Simply put, if you had three users label the same image three times, you would have to pay for all 3 annotations. 

All this is to emphasize that, the two most expensive steps in data labeling are:

• The data labeling itself
• Reviewing and verifying it for quality control. 

 

Looking at all the huge costs that it would take in an annotation project, many business leaders have turned into a less time-consuming and tedious solution, which is the auto annotation tool technology.

Thankfully, with the latest technologies in artificial intelligence and machine learning, automated data labeling, or auto annotation, is usable now. However, to create an effective and well-rounded auto annotation tool now, it even requires more training data and human input for correcting errors induced by the AI. Therefore, anyone has the naive attempt to entirely apply auto annotation tools, they have to be cognizant of the truth that the tools are not the one-size-fits-all solution.

 

The advantages of Automated Data Labeling

Automated data labeling is quite a new term in the field, but the technology advancement implementing and making it happen is developing with high speed, shown in the large number of tools on the market now. So what are auto data labeling and its benefits?

 

What’s automated data labeling?

Automatic labeling is a feature found in data annotation tools that apply artificial intelligence (AI) to enrich, annotate, or label a dataset. Tools with this feature augment the work of humans in the loop to save time and money on data labeling for machine learning.

 

 

Most tools allow you to load pre-annotated data into the tool. More advanced tools, which are evolving into platforms (e.g., tool plus Software Development Kit or SDK), allow you to leverage AI or bring your own algorithm to the tool to improve the data enrichment process by auto labeling data.

Other tools offer prediction models that suggest annotations so workers can validate them. Some features leverage embedded neural networks that can learn from every annotation made. All of these features can save time and resources for machine learning teams and will have a profound effect on data annotation workflows.

 

Outstanding benefits of automated data labeling

When working with organizations using tools to annotate images for machine learning, we find two optimal ways to apply auto labeling in data annotation workflow:

• Pre-annotate some or all of your dataset. Workers come behind the automation to review, correct, and complete the annotations. Automation cannot annotate everything; there will be exceptions and edge cases. It’s also far from perfect, so you must plan for people to make reviews and corrections as necessary.
• Reduce the amount of work sent to people. An auto-labeling model can assign a confidence level based on the use case, task difficulty, and other factors. It enriches the dataset with annotations, and sends annotations with lower confidence scores to a person for review or correction.

We’ve run time experiments, with one team using tools that have an automation feature versus another team that is manually annotating the same data. In some cases, we’ve seen auto labeling provide low-quality results which increase the amount of time required per annotation task. Other times, it has provided a helpful starting point and reduced task time.

 

 

In one image annotation experiment, auto labeling combined with human-powered review and improvements was 10% faster than the 100% manual labeling process. That time savings increased from 40% to 50% faster as the automation learned over time.

It also had a more than the five-pixel margin of error for vehicles and missed the objects that were farthest from the camera. As you can see in the image, an auto-labeling feature tagged a garbage bin as a person. It’s important to keep in mind that pre-annotation predictions are based on existing models and any misses in the auto labeling reflect the accuracy of those models.

Data annotation tools can include automation, also called auto labeling, such as Labelbox and Tagtog, which uses artificial intelligence to label data, and workers can confirm or correct those labels, saving time in the process.

While auto labeling is not perfect, it can provide a helpful starting point and reduce task time for data labelers.

 

 

Some tasks are ripe for pre-annotation. For example, if you use the example from our experiment, you could use pre-annotation to label images, and a team of data labelers can determine whether to resize or delete the labels, or bounding boxes.

This reduction of labeling time can be helpful for a team that needs to annotate images at pixel-level segmentation.

Our takeaway from the experiments is that applying auto labeling requires creativity. We find that our clients who use it successfully are willing to experiment, fail, and pivot their process as necessary.

As auto data labeling is one of the breakthroughs for a better outlook of the AI technology, specifically machine learning, we still have a lot to discover with this new term.

 

 

If you want to hear from our experts concerning the matter of Automated data labeling, please contact us for further details.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

 

Parallel to the fast-paced development of the Artificial Intelligence and Machine Learning market, the field of data annotation is moving forward with the most accelerating trends, both in terms of tools and workflow.

From AI-Powered Virtual Assistant to Autonomous Cars, data annotation has played an important role.

Some might think that data annotation is a boring, timid and time-consuming process, while others might deem it the crucial element of artificial intelligence’s success. 

In fact, data annotation, or AI data processing, was once the most-unwanted process of implementing AI in real life. However, with the ever-growing expansion AI in multiple fields of our daily lives, the needs for rich, versatile and high-quality datasets are higher than ever. 

In order for a machine to run, in this case, is the AI system, we have to pour training data in so that the “machine” could learn to adapt to whichever is coming at it.

With these trends in the data annotation and AI data processing market, it not only sets a new outlook for the whole market, it also proves the urgent needs for well-annotated datasets.

 

Predictive Annotation Tools – Auto Labeling Tool

It is pretty obvious that the more fields we can apply Artificial Intelligence and Machine Learning in, the more we need AI data processing. 

By saying AI data process, we also mean that we need both the data collection and data annotation.

The rapidly expanding needs of the AI and machine learning market have set a new goal for another focus of the data annotation process. As it is with the Testing market, the demands for auto labeling, or we can call it predictive annotation tools are coming to a peak.

 

Basically, the predictive annotation tools (auto labeling tools) are the tools that can automatically detect and label items with the foundation of the similar existing manual annotations.

With the implementation of the aforementioned tools, after some manually annotated data, the toolkit can subsequently annotate the similar datasets.
Throughout this process, the human intervention is limited to the minimum amount, hence saving a lot of time and effort to do such repetitive and boring tasks.

With just some scratches on the surface, auto labeling, or predictive annotation tools may be the pivotal change that will boost up the speed of the annotation process by 80%. But to put one auto labeling tool on the market, it takes years of developing sophisticated features, not to mention a large number of data types need to be put in the data annotation system of that tool. That is why you often see one tool for only one data type.

While the advantages of an auto labeling tool are undeniable, the cost for one commercial tool like that can be enormous.

 

Emphasis on Quality Control

It is sure that Quality Control plays a huge role in every process. However, the current situation only shows that QC is only circumstantial. 

In the future, data engagements at scale will be the main focus, requiring a higher emphasis on quality control.

With more data labeling solutions going into production, and later into the training model of AI systems, more edge cases will be considered.

 

Under this circumstance, it is a must that you build your own teams of QC to exclusively handle the quality of the annotated datasets. They will not work the way the old QC staff did. On the contrary, these specialized experts can function without detailed guidelines and focus on spotting and fixing issues with large datasets.

What about security? With the security, the QC team should follow a stringent process of maintaining security of the annotation process. This should be ensured throughout the whole project.

 

Involvement of metadata in data annotation process

From autonomous vehicles to medical imaging, in order for the AI system to run smoothly without glitches, a staggering amount of data is required for annotation.

Metadata is the data clarifying your data. With the same old annotations as the code snippets you put in at the Java class or method level that further define data about the given code without changing any of the actual coded logic, metadata is for data management.

 

All in all, metadata is created and collected for the better utility of that data.

If we can make good use of the metadata, any human errors including misplacing things, management malfunctions, etc. will be tackled. With metadata in hand, we will be able to find, use, preserve and reuse data in a more systematic manner.

• In finding data, metadata speeds up the process of finding the relevant information. Take a dataset in the form of audio for example. Without metadata and the management from it, it would be impossible to us to find the location of the data. This also applies to data types such as images and videos.
• In using the data, metadata gives us a better understanding of how the data is structured, definitions of the terms, its origin (where it was collected, etc.)
• In re-using data, metadata helps annotators navigate the data. In order to reuse data, annotators are to have careful reservation and documentation of the metadata.

The key to making all of this happen is data annotation. Adding metadata to datasets helps detect patterns and annotation helps models recognize objects.

With all the benefits of metadata in how we can manage and use the datasets, many firms now have grown interested in developing metadata for better management.

 

Workforce of SMEs

The rapidly growing number of the industries embracing AI, a subject-specific data annotation team is of urgent needs. 

For every domain such as healthcare, finance, automotive, etc. a team trained with custom curricular will be deployed on projects, hence expert annotators built over time. With this being done, more value and high-quality to the annotation process will be focused with a deeper approach, and this strategy will start with the validation of guidelines to time of data delivery.

 

Do you want to deploy these data annotation trends? Come and contact LQA for further details:

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance