Data Annotation and Labeling – Everything you Need to Know
Did you know almost 90% of data owned by organizations is unstructured and is growing at 55-65% each year?
That sure is a lot of unstructured data floating around! And we all know how vital high-quality training data is for implementing AI/ML projects, not to undermine the fact that unstructured data creates security and compliance risks.
So, how does one address this, especially if building an AI/ML model, and have to feed relevant information for the model to process and deliver output and inferences? Well, the output of an AI & ML model is only as good as the data being used to train it, as the model only delivers effectively when the algorithm understands what is being fed to it. Therefore, the precision with which the data is aggregated, tagged, and identified is of the utmost importance. And this process of tagging, attributing, or labeling data is called data annotation.
What is Data Annotation, and how does it help companies implement fool-proof AI/ML models
Data annotation is the categorization and labeling of data for the successful deployment of AI applications. Building an AI or ML model with human-like behavior requires large volumes of high-quality data. This training data must be precisely categorized and annotated for specific use cases to help companies build and improve AI implementations, resulting in enhanced user experience.
With data annotation, an AI model would correctly identify whether the data it receives is video, image, text, graphics, or a mix of formats. Depending on the parameters assigned and the AI model’s functionality, it would then classify the data and proceed with executing its tasks.
Data annotation ensures your models are precisely trained. So, regardless of whether you deploy the model for speech recognition, automation, chatbot, or any other process, you would get a full-proof model that delivers optimum results.
In ML, data labeling oversees the task of recognizing the raw data like text files, images, and videos with informative labels on it to train a machine learning model. Data labeling can be applied in innumerable use cases like natural language processing, computer vision, and speech recognition.
Data annotation is the process of labeling data with different metadata forms like audio, text, images to train ML models like chatbots, autonomous vehicles, and more.
This is where the vital role of the “Human in the Loop” comes to the fore. Human in the loop and Human Intelligence play a crucial role in the journey to verify, validate and fix issues in the model outcome to enhance efficiency and enable improvisation.
Therefore, data annotation and labeling can dramatically enhance the ability of an AI or ML program while at the same time decreasing time-to-market and total cost of ownership.
Data Annotation and Labeling – Scope of Application
High-quality data annotation and labeling are vital for a wide range of use cases across verticals. From healthcare to retail, mining speech to text rendering of video conferencing, to optimizing a transportation grid, and so many more, data annotation and labeling are how AI and ML algorithms get to market.
Experts predict that from being a $150 million market in 2018, data labeling will become a billion-dollar industry by 2023 (Axois) and a 2.5 billion market by 2027.
Types of Data Annotation
To successfully execute the entire AI ML model learning process, it is vital to know about distinct data annotation types depending on specific use case requirements.
One commonly used annotation data type is Bounding Boxes. These boxes are primarily used for tracking objects for computer vision or validation and testing of new sensors. Let us take self-driving cars as an example. The annotator will picture bounding boxes around the surrounding vehicles and label them accordingly. Such annotation and labeling will help the algorithm understand what the specific vehicle/car looks like. Moreover, bounding boxes increases the automation efficiency while reducing the cost.
Cuboidal annotation means drawing a cube over a specific or target object to get 3D perspectives of height, width, depth. Such annotation is widely used in road sequences to recognize the difference between roads, cars, trucks, vans, pedestrians, and more. The cuboids are drawn on the object, and the annotator will only adjust the box dimensions and sizes.
Text annotation helps in training chatbots and assistant devices to answer the questions posed by different users. Also, the ML models are trained to create search engine-specific keywords and use them at the time of critical searches.
Semantic annotation helps the machine learning model train and understand the annotation requirement by assigning each image pixel to a specific class of objects. Semantic segmentation annotation is more versatile, as it becomes easy to distinguish between objects like lanes, curbs, roads and recognize instances from them through the whole sequence.
Polylines are responsible for annotating the road lanes and other closed or open-ended objects. Polyline annotation enables the exact path recognition ahead of connected cars or autonomous vehicles. If we talk about polyline uses or applications, these perform well in self-tracking vehicles in HD maps and play a significant role in training data sets to achieve reliable self-driving models.
Besides detecting the objects or recognizing them like image labeling, video annotation has various other purposes. Video annotation trains the ML models to locate human activities and estimate the poses. In terms of autonomous vehicles, video annotation trains the AI ML model to efficiently detect, recognize, categorize and localize varying objects.
Why Amantya for Data Annotation and Image Labeling
At Amantya Technologies, we provide quality data annotation and labeling services with the help of in-house experts and NextGen data annotation & labeling tools. We work on various enterprise platforms, including our in-house platform, to execute a versatile range of annotation and labeling projects and deliver the best quality training data sets.
Our services include:
- Image labeling
- Video annotations
- Text decoding
All our services are available across a vast range of business verticals like automotive, retail, manufacturing, healthcare, finance, and governance, etc.
So, regardless of the type of data and use case, we will help you leverage our experience and expertise in data annotation to accelerate your AI journey.
Data annotation and labeling hold the key to the development of AI ML learning. Worldwide, people are already reaping the benefits of next-generation technologies like Artificial Intelligence and Machine learning. However, Machine learning is viable only with relevant and qualitative data sets, a highly daunting task in the AI world. With the rapid advancement of technology, every business vertical and industry globally will require data annotations to improvise their system’s quality and keep up with deep learning trends.
Keen to know more? Please get in touch, and our team would be happy to help you optimize your AI model.