The advent of artificial intelligence and machine learning has made it easier to utilize the massive amount of data available over the web. The raw or unstructured data can now be used to get useful and actionable insights that can help businesses to grow and succeed. Doing this is not magic; it needs to perform a lot of processes to train the machine learning models that can deliver valuable outcomes.
Two techniques that play a key role in training machine learning models are: Data annotation and image labeling. In generic terms, data annotation is the processing of labeling data so that the machine learning algorithms can recognize them. Image labeling refers to the process of naming the objects in an image.
Image labeling refers to the process of identifying the objects in an image. For instance- image labeling helps in finding entities like drinks, animals, food items, colors, and more in an image. The process can also be used for custom image classification as per the requirements.
Labeling data through data annotation describe the ML algorithm about the features, properties, attributes, category, and other details of the data. Based on this data, the algorithm then creates patterns to make predictions.
Driverless or self-drive cars are one of the best examples where data annotation and image labeling finds its great use. Before you understand how data annotation and image labeling works, let’s check out different types of them:
Types of data annotations
This type of annotation is mainly used where complex polygons describe the location and shape of the object. With polygon segmentation, you can be sure of the precision in the object identification.
One of the common kinds of data annotations is bounding boxes. Primarily used in computer vision, these rectangle-shaped boxes help in describing the location of an object. To determine the location, bounding boxes use x and y coordinates in the upper-left and the lower-right corner of the rectangle. This type of data annotation finds its major use in localization jobs and object identification.
Along with the information offered by bounding boxes, 3D cuboid also offers extra information about an object. It shows the 3D representation of an object, which assists in differentiating an object’s volume and position. 3D cuboid’s primarily used in self-driving cars to measure the distance of the car from a particular object.
Text annotation helps in recognizing words in a sentence by associating the text with the Metadata and highlighting it with a particular color. Text annotation can be done by skilled annotators with considerable experience. This type of data annotation helps in bringing the accurate results.
Also known as Key-point and landmark annotations, it is used for identifying small objects and shapes. To recognize the same, this type of annotation creates dots across the image. It finds its major use in detecting features, emotions, expressions of a face. It can also detect human body parts and postures.
This type of annotation assigns a class to every pixel of an image, and hence called a pixel-wise annotation. Semantic segmentation is used to identify pedestrian, bus, zebra crossing, car, and other such objects. Each pixel of the image carries some meaning.
Real-world uses of Data Annotation and Image Labeling
Both the processes play a major role in detection of objects, identifying different types of objects, categorizing the types of objects, adding same type of objects into a single class, detecting faces and poses. These are used in:
- Self-driving cars or driverless cars
- Software or devices with face detection security system
- Social networking websites and applications
- To identify facial expressions in various applications
Trending data annotation and data labeling tools
Some of the most popular data annotation tools include LabelImg, LabelMe, MakeSense.AI, VGG image annotator, Scalable, and RectLabel.
Common Image Annotation Formats
One cannot deny the fact that there are no particular formats for image annotation. However, here are the most common ones:
- COCO- This type of format can be further classified into keypoint detection, panoptic segmentation, image captioning, stuff segmentation, and object detection.
- YOLO- In this type of format, each image in the same directory gets a .txt file with the same name. This .txt file includes annotations (height, width, object class, and other details) for the associated image file.
- Pascal VOC- It stocks up annotations in XML file format and offers standardized image data sets that help in identifying object class.
Data Annotation and Image Labeling- Applications in Machine Learning
Data annotation and image labeling has the following uses in machine learning:
- To classify the data into different classes, labels, binary classes, and others.
- To search transitions between various topics, find the position of a paragraph split, and more.
- To translate a language into another language, prepare the summary of a complicated text, and to perform various other functionalities.
- For sequencing texts and labels.
Why choose Quytech for Data annotation and image labeling services?
Quytech promises to deliver its clients high-quality and well-trained data sets that can be used to train machine learning models. Besides this, there are many other reasons for which you should select our company for data annotation and image labeling services:
- Secure project management
- A huge team of experienced and skilled data annotators, AI & ML experts, and other professionals
- Affordable plans
- Project scalability
- Data acceptance and precision
With Quytech, you can get services like text annotations, image/video annotations, printing & scanning documents for advanced ML algorithms, transcriptions, data repositories, and more. To know more, contact one of the professionals at the company.
Our data annotation and image labeling services are just a click away!
The Bottom Line
Data annotation and image labeling are not new terms to those who are aware of the technologies such as artificial intelligence, machine learning, big data, computer vision, neural networking, and more. These two processes are used to label data, which then trains machine learning algorithms to perform a variety of tasks, such as recognizing images, objects, and predicting various patterns to turn unstructured data into useful insights.
This article highlights what are these two processes, type of data annotations, common formats and tools for image notation, and other information. In case, if you are looking out for such services, then feel free to reach one of the top renowned data annotation and image labeling services provider, Quytech. The company offers these services at a cost-effective rate.
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