Artificial intelligence data sets refer to the data sets used to process and train AI models. Currently, the most used data sets in machine learning are:

1. Face image: face detection and feature extraction (including face recognition, gender distinction, age classification, etc.).

2. Speech Artificial recognition, language processing (natural language processing and speech recognition).

3. Video image: video recognition (video tracking and understanding, video generation).

4. Image Artificial text: image retrieval (remote sensing images, natural language understanding).

5. Meteorological data: climate analysis and prediction, meteorological disaster prediction and analysis, global weather monitoring and forecasting.

6. Social data: demographic data, economic forecasts, etc.

Below we mainly introduce the three commonly used data sets:

1. Face image

Face detection refers to the recognition of human face features detected from human face images, and it is one of the most concerned issues in the current research field. Commonly used detection Artificial methods are: pixel-based segmentation and image segmentation-based methods. Block filtering based methods are used in face detection techniques. It uses a method to decompose an image into individual pixels and then clusters the image based on features. In the method based on region embedding, each pixel is embedded into a specific region, and then the pixel value in the corresponding region is determined according to the positional relationship between them, so that each region has a specific boundary. In many cases, the method can also be used for facial expression recognition and facial gender recognition.

2. Voice data

Speech data refers to the language input (i.e. speech or text) that is generated from a language recognition model. It is used to create and train natural language processing systems (e.g. deep learning systems), usually consisting of a collection of speech databases (e.g. BERT), or collections of data taken from an annotated dataset for training models for speech recognition. Speech datasets are mainly considered from two perspectives: Artificial Natural language processing. Natural language processing refers to converting the information in the corpus into a form that the computer can understand or represent. It includes preprocessing the input text, such as denoising, normalization, and grammatical analysis. Natural language processing technology is widely used in translation and speech recognition. In addition to voice data, other aspects of data are also common, such as audio and video uploaded by users on social media.

3. Video image Artificial

Such datasets are used to train models, at which stage Artificial machine learning algorithms are already able to learn the characteristics of video objects from video sequences and quantify these characteristics in the process. Video data is a very complex dataset for studying the interrelationships between objects, and things that a single model cannot handle. For example, when performing experiments on medical imaging datasets, tumors are detected to assess whether a patient has cancer. Features in the dataset generally include: video frames, image regions, object properties, and motion. Such datasets can be used to train models and interpret experimental results. There is also a large amount of research work using such datasets outside of the field of machine learning.