Data Annotation Industry

For robots, drones, vehicles & farming to achieve higher levels of autonomy, they need artificial intelligence based on reliable data. Companies working on machine learning projects must juggle research, development, analysis and other tasks connected with their core functions. Their in-house employees do not necessarily have the time for annotating data at the volumes required to train machine learning algorithms. Such work can also prove to be costly, since engineers and other team members tend to command a high rate of pay.

• What are Data Annotations?

Data annotation is the process of labeling the data available in various formats like text, video or images. For supervised machine learning labeled data sets are required, so that machine can easily and clearly understand the input patterns.

• What does a data annotator do?

Data annotation (commonly referred to as data labelling) plays a crucial role in ensuring your AI and machine learning projects are trained with the right set of information to learn from. Annotators are needed to identify and annotate specific data so machines can learn to identify and classify information.

• Why is data annotation important?

Properly annotated data is very important for the development of autonomous vehicles, computer vision for aerial drones, and many other AI and robotics applications. Self-driving cars must be able to identify everything they might encounter on the road. Therefore, human data annotators need to label pedestrians, traffic signs, other vehicles, and many other items in millions of images for such cars to function safely and properly. In precision agriculture, drones can help farmers identify poorly growing crops so that they can adjust applications of fertilizer, water, or pesticide before an entire harvest is lost. Computer vision has to be trained to identify fruits and vegetables, which can vary widely in shape and orientation, in different conditions for this to work. Since data annotation is very time-consuming, many firms outsource the task to service providers that possess the necessary staffing capacity to get everything done on time and within budget.

• How is data annotation done?

In machine learning, data annotation is the process of labeling data to show the outcome you want your machine learning model to predict. You are marking – labeling, tagging, transcribing, or processing – a dataset with the features you want your machine learning system to learn to recognize.

• What are the different types of Data Annotation?

1. Bounding boxes
2. Lines and splines
3. Semantic segmentation
4. 3D cuboids
5. Polygonal segmentation
6. Landmark and key-point
7. Entity annotation
8. Content & Text Categorization

Let’s read them in detail:

Bounding boxes:

 The most common kind of data annotation is bounding boxes. These are the rectangular boxes used to identify the location of the object. It uses x and y-axis coordinates in both the upper-left and lower-right corners of the rectangle. The prime purpose of this type of data annotation is to detect the objects and locations.

Lines and splines:

This type of data annotation is created by lines and splines to detect and recognize lanes, which is required to run an autonomous vehicle.

Semantic segmentation:

This type of annotation finds its role in situations where environmental context is a crucial factor. It is a pixel-wise annotation that assigns every pixel of the image to a class (car, truck, road, park, pedestrian, etc.). Each pixel holds a semantic sense. Semantic segmentation is most commonly used to train models for self-driving cars.

3D cuboids:

This type of data annotation is almost like bounding boxes but it provides extra information about the depth of the object. Using 3D cuboids, a machine learning algorithm can be trained to provide a 3D representation of the image.

The image can further help in distinguishing the vital features (such as volume and position) in a 3D environment. For instance- 3D cuboids help driverless cars to utilize the depth information to find out the distance of objects from the vehicle.

Polygonal segmentation:

Polygonal segmentation is used to identify complex polygons to determine the shape and location of the object with the utmost accuracy. This is also one of the common types of data annotations.

Landmark and key-point:

These two annotations are used to create dots across the image to identify the object and its shape. Landmark and key-point annotations play their role in facial recognitions, identifying body parts, postures, and facial expressions.

Entity annotation:

Entity annotation is used for labeling unstructured sentences with the relevant information understandable by a machine. It can be further categorized into named entity recognition and intent extraction.

Benefits of data annotation:

Data annotation offers innumerable advantages to machine learning algorithms that are responsible for training predicting data. Here are some of the advantages of this process:

• Enhanced user experience:

Applications powered by ML-based trained models help in delivering a better experience to end-users. AI-based chatbots and virtual assistants are a perfect example of it. The technique makes these chatbots to provide the most relevant information in response to a user’s query.

• Improved precision:

Image annotations increase the accuracy of output by training the algorithm with huge data sets. Leveraging these data sets, the algo will learn various kinds of factors that will further assist the model to look for the suitable information in the database.

Formats of image annotations:

The most common annotation formats include:

• COCO
• YOLO
• Pascal VOC

Applications of data annotations in machine learning:

By now, you must be aware of the different types of data annotations. Let’s check out the applications of the same in machine learning:

• Sequencing- It includes text and time series and a label.
• Classification- Categorizing the data into multiple classes, one label, multiple labels, binary classes, and more.
• Segmentation- It is used to search the position where a paragraph splits, search transitions between different topics, and for various other purposes.
• Mapping- It can be done for language to language translation, to convert a complete text into the summary, and to accomplish other tasks.

Tools used for data annotations:

Check out below some of the common tools used for annotating images:

• Rectlabel
• LabelMe
• LabelImg
• MakeSense.AI
• VGG image annotator
  

Conclusions

In this article, we have mentioned what data annotation or labeling is, and what are its types and benefits. Besides this, we have also listed the top tools used for labeling images. The process of labeling texts, images, and other objects help ML-based algorithms to improve the accuracy of the output and offer an ultimate user experience.

A reliable and experienced machine learning company holds expertise on how to utilize these data annotations for serving the purpose an ML algorithm is being designed for. You can contact such a company or hire ML developers to develop an ML-based application for your startup or enterprise.