What does best artificial data collection and labeling mean?

As artificial intelligence and AI technology continue to integrate into people’s life, study and work, data processing will have great development prospects. Using accurately labeled data can help computers develop more effective algorithms to solve more problems. This makes the collection and labeling of data destined to become an indispensable and important part of the era of artificial intelligence. So, what exactly does data collection and labeling mean? Next, let us find out together.

What is data collection?

1. The meaning of data collection

The so-called data collection is to obtain data in various forms, and in the process of obtaining data, integrate, connect and clean the data to effectively transform the value density of data.

2. Classification of data collection

In general, data collection is divided into video collection, text collection, voice collection and picture collection. For example, packaging collection and license plate collection belong to image collection; English collection, Mandarin word collection, etc. all belong to voice collection.

In fact, the process of data collection is relatively simple. It only needs to collect data according to the requirements of the demand side.

What is data labeling?

1. The meaning of data labeling

The so-called data labeling is actually the process of labeling a lot of original data, such as audio, text, video, image, 3D point cloud and other content. The labeled data will be used as training data in many fields.

2. Classification of data annotation

(1) Image data annotation

In the data labeling industry, image data labeling is currently the most widely used in various industries. Image data annotation includes three-dimensional box annotation, discounted annotation, key point annotation, semantic segmentation annotation, polygon annotation and 2D bounding box annotation.

(2) Audio/voice data annotation

It mainly transcribes and classifies audio data of musical instruments, environments, animals, and people.

(3) Text data annotation

This type of annotation is mainly to enable artificial intelligence to understand the semantic meaning of text content. Often used in text transformations.

(4) 3D point cloud data annotation

This type of labeling is obtained through lidar scanning data, which is usually applied in the field of autonomous driving.


Through the introduction of the above content, I believe that everyone has a better understanding and understanding of data collection and labeling . It is precisely because of the continuous development of artificial intelligence that data collection and labeling are the basic technical support of artificial intelligence training projects, so it is very helpful for people in this field to learn more about the content of data collection and labeling of.