The current mainstream machine learning method is mainly based on supervised deep learning, which has a strong dependence on labeled data. The raw data that has not been labeled is mostly unstructured data, which is difficult to be recognized by machines. Learn. Only structured data that has been labeled can be used for algorithm model training.
What is Data Labeling?
Data annotation is the process of providing meaningful data for machine learning models. It works by adding labels to data so that machine learning models can analyze and predict correctly and efficiently. Data annotation can take many forms, including text classification, entity recognition, and image classification.
In text classification, professionals add labels to text so that machine learning models can correctly understand its content. For example, labeling news articles so that a machine learning model can correctly classify the content of the news article.
Entity recognition is another type of data annotation that can be used to identify entities that occur in a particular text. For example, labeling the names of people, places, and organizations that appear in text so that machine learning models can correctly identify them.
Image classification is a type of data annotation used to add labels to images. For example, labeling objects in an image so that a machine learning model can correctly identify the object in the image.
Why is data labeling needed?
Data annotation is actually one of the important components of artificial intelligence.
Data annotation is the basis of machine learning, which can help machines better identify and understand data. Data annotation separates different categories of data, so that machines can identify each category of data, which contributes to the accuracy and efficiency of machine learning. In addition, data annotation can also help the machine remember the specific information of each data for subsequent operations.
The role of data annotation is to convert raw data into a form that machines can understand for the application of technologies such as machine learning. Data annotation can classify, organize, and label information in raw data so that it can be understood and processed by machines. Its role is to help machine learning algorithms to accurately identify and understand data, so that inferences and predictions can be made more accurately.
Where can data annotation be applied?
1. Autonomous driving
Annotated data is used to train self-driving models so that they can sense their surroundings and move with little or no human input. Data annotation of autonomous driving scenes involves semantic segmentation of images and videos, 3D point cloud annotation, video tracking annotation, vehicle and pedestrian frame annotation, lane line annotation, etc.
2. Face recognition
From various age groups, genders, etc., multi-angle, multi-expression, multi-light, multi-scene data collection and data analysis, processing and labeling, and marking of faces, provide greater data protection for face recognition technology.
3. Intelligent security
In the field of intelligent security , data annotation is mainly used in the two main fields of computer vision and speech recognition, specifically human face key point annotation, expression analysis, pedestrian annotation, bone key point annotation, video segmentation, object detection and other annotation methods.
4. Smart healthcare
For now, artificial intelligence technology plays more of an auxiliary role in the medical field and cannot completely replace doctors. For the better development of smart medical technology, more scene-oriented, refined, professional and high-quality labeled data are needed for training and tuning the algorithm model.