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Best Data Labeling Tutorial: Definitions, Tools, Datasets

Data is the building block of all machine learning and deep learning algorithms

Machine Learning and data Labeling at 24x7offshoring
Machine Learning and data Labeling at 24x7offshoring

. It’s what drives these complex and sophisticated algorithms to deliver state-of-the-art performance.


If you want to build truly reliable AI models, you must feed your algorithms with properly structured and data Labeling – labeled data.

This is where the data labeling process comes into play.

You need to label your data so that a machine learning system can use it to learn how to perform a given task.

Data labeling is easy, but it might not be easy

Here’s what we’ll cover:

1. What is data annotation?

2. Types of data annotation

3. Automatic data labeling and manual labeling

1. What is data annotation?

data annotation
data annotation

Data annotation is the task of labeling or recognizing data in various formats such as text, images, and videos.

Essentially, it boils down to labeling regions or areas of interest — this type of annotation is found exclusively in images and videos. On the other hand, labeling text data mainly consists of adding relevant information, such as metadata, and assigning them to a certain class.

In machine learning, the task of data labeling typically falls under the umbrella of supervised learning, where a learning algorithm associates inputs with corresponding outputs and optimizes itself to reduce errors.

2. Types of data annotation

Data Labeling e1588059655890

The following are the various types of data annotations and their characteristics.

2.1 Image annotation

Image annotation is the task of annotating images with labels. It ensures that machine learning algorithms recognize labeled regions as distinct objects or categories in a given image.

It involves creating bounding boxes (for object detection) and segmentation masks (for semantic and instance segmentation) to distinguish objects of different classes. You can also annotate images with tools such as keypoints, 3D boxes, polylines, keypoint skeletons, and brushes.

Image annotation is often used to create training datasets for learning algorithms.

These datasets are then used to build AI-enabled systems such as self-driving cars, skin cancer detection tools, or drones that assess damage and inspect industrial equipment.

Check out AI in Healthcare and AI in Insurance to learn more about AI applications in these industries.

Now, let us explore and understand different types of image annotation methods.

2.2 Bounding box

A bounding box involves drawing a rectangle around an object in a given image. The edges of the bounding box should touch the outermost pixels of the labeled object.

Read Labeling with Bounding Boxes: Quality Best Practices to learn more.

2.3 3D cuboid

3D Box Callouts are similar to Bounding Box Callouts, but in addition to drawing a 2D box around the object, the user must also account for depth. It can be used to annotate objects that need to be navigated on a plane, such as a car or airplane, or objects that need to be grasped by a robot.

2.4 Polygons

When creating a 3D box or bounding box, you may notice that various objects may be inadvertently included in the label area. This situation is far from ideal, as machine learning models can get confused and thus misclassify these objects.

Fortunately, there are ways to avoid this, and this is where polygons come in handy. What makes them so effective is their ability to create a mask around the desired object at the pixel level.

2.5 Polygon tool

You can select the tool and simply start drawing lines of single points around objects in the image. The lines don’t need to be perfect, as you can add labels to labeled objects once the start and end points are connected around the object.

3. Start labeling data

key point tool

Keypoint labeling is another method of labeling objects by a series of points or collections of points.

This type of approach is useful in gesture detection, facial landmark detection, and motion tracking. Keypoints can be used individually or combined to form a point map that defines the pose of an object.

Keypoint Skeleton Tool

It is used to define the 2D or 3D pose of multi-limbed objects. A keypoint skeleton has a defined set of points that can be moved to suit the appearance of an object.

You can use keypoint annotations to train machine learning models to mimic human poses and then infer their functionality for task-specific applications such as AI-enabled robots.

polyline tool

The Polyline tool allows the user to create a series of connecting lines.

You can also use it by clicking around the object of interest to create a point. Each point will create a line by connecting the current point with the previous point. It can be used to label roads, lane markings, traffic signs, etc.

semantic segmentation

Semantic segmentation is the task of grouping together similar parts or pixels of objects in a given image. Labeling data in this way allows a machine learning algorithm to learn and understand specific characteristics and can help it classify anomalies.

Semantic segmentation is very useful in medicine, where radiologists use it to identify regions of interest in X-rays, MRIs, and CT scans. This is an example of a chest x-ray callout.

video annotation

Similar to image annotation, video annotation is the task of labeling segments or clips in a video to classify, detect, or identify desired objects on a frame-by-frame basis.

Video annotation uses the same techniques as image annotation, such as bounding boxes or semantic segmentation, but on a frame-by-frame basis. It is a fundamental technique for computer vision tasks such as localization and object tracking.

text annotation

Data annotation is also essential in tasks related to Natural Language Processing (NLP).

Text annotation refers to adding information about language data by adding tags or metadata. To understand text annotation more intuitively, let’s consider two examples.

1. Assign labels

Adding a label means assigning a sentence a word that describes its type. It can be described emotionally, technically, etc. For example, the sentence “I’m happy with this product, it’s great” could be given a label like “happy”.

2. Add metadata

Similarly, in the sentence “Id like to order a pizza today”, relevant information can be added to the learning algorithm so that it can prioritize and focus on certain words. For example, you could add something like “Tonight (time) I want to order pizza (food_item)”.

Now, let’s briefly explore the various types of text annotations.

Emotional annotation

Emotion labeling is nothing more than assigning labels representing human emotions, such as sad, happy, angry, positive, negative, neutral, etc. )

intent annotation

intent annotation
intent annotation

Intent annotation also assigns labels to sentences, but it focuses on the intention or desire behind the sentence. For example, in a customer service scenario, a message like “I need to speak to Sam” could route a call to Sam alone, or a message like “I have a question about my credit card” could route a call to the team for credit card issues.

Named Entity Notation (NER)

Named entity recognition (NER) aims to detect and classify predefined named entities or special expressions in sentences.

It is used to search for words based on their meaning, such as people’s names, locations, etc. NER is useful in extracting information as well as classifying and categorizing them.

semantic annotation

As we have seen before, semantic annotation adds metadata, additional information, or tags, to text concerning concepts and entities such as people, places, or topics.

Automatic data labeling and manual labeling.

Over time, human annotators get tired and lose focus, which often leads to poor performance and errors. Data labeling is a task that requires concentration and skilled personnel, and manual labeling makes the process time-consuming and expensive.

That’s why leading ML teams are betting on automated data labeling.

However, in cases where the model fails to label correctly, humans can intervene, review and correct mislabeled data. The labeling model can then be trained again using the corrected and censored data.

Automatic data labeling can save you a lot of money and time, but may lack accuracy. In contrast, human annotation can be much more costly, but is often more accurate.

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