The main types of data annotation
l？ Computer vision
Including rectangular box labeling, key point labeling, line segment labeling, semantic segmentation, instance segmentation labeling, ocr labeling, picture classification, video labeling and so on.
l？ Voice engineering
Including ASR phonetic notation, phonetic cutting, voice cleaning, emotional judgment, voiceprint recognition, phoneme labeling, prosody labeling, pronunciation proofreading and so on.
l？ natural language understanding
Including ocr transliteration, part-of-speech tagging, named entity tagging, sentence generalization, sentiment analysis, sentence writing, slot extraction, intention matching, text judgment, text matching, text information extraction, text cleaning, machine translation and so on.
l？ Autopilot point cloud
Include 3D point cloud target detection labeling, 3D point cloud semantic segmentation labeling, 2D and 3D fusion labeling, and point cloud continuous frame labeling.
What business scenarios can data annotation be applied to?
1. Intelligent driving
Intelligent driving cars need algorithms to deal with a large number of complex scenes, and a large number of accurate and high-quality data are needed to train algorithm models. The identification algorithms of vehicles, pedestrians, obstacles, weather, lane lines, road signs and other external environments, fatigue monitoring of drivers and passengers, violation identification algorithms, voice interaction and multimodal interaction technology in the intelligent cockpit all need to be marked with data.
2. Smart security
Intelligent security is a key field of combining artificial intelligence with information technology, which requires high-quality and accurate data to train and upgrade technology. AI technologies such as access control biometrics, urban road monitoring, traffic flow monitoring, illegal behavior monitoring, high-altitude parabolic monitoring, and pedestrian re-identification all need data labeling process.
3. Smart home
It is the mainstream trend to drive smart home with AI and develop AIoT in the same direction. The AI technology of face recognition, fingerprint identification access control system, intrusion detection, sweeping robot, intelligent voice assistant, intelligent terminal control and other scenes all need degree data to mark.
4. Smart finance
AI empowers the traditional financial industry and retail industry, and simplifies the commercial purchase process. AI technologies such as identity authentication, intelligent customer service, intelligent marketing, intelligent risk control, product images of virtual shopping scenes, bills and documents, face recognition, and designated corpora all need data annotation support.
5. Smart Internet
Intelligent Internet includes intelligent application, entertainment interaction, intelligent search and content review. AI technologies such as chat bots, graphic retrieval, multimodal intention judgment, sentiment analysis, illegal content review, and intelligent beauty all need data annotation support.
6. Intelligent industry
The four application scenarios of intelligent industrial vision are measurement, recognition, guidance and detection. Algorithms including complex defect detection, helmet reflective clothing identification, defect detection, fireworks detection, illegal building detection, and sleeping on duty all need data tagging services.
What does the data labeling company mainly do?
1. Definition
The data labeling company is to assist artificial intelligence enterprises to solve the corresponding problems of data labeling in the whole artificial intelligence chain. Tag business can be divided into four categories: image tag, voice tag, text tag and 3D point cloud tag, covering AI application fields such as computer vision, voice engineering and natural language processing.
2.？ Data label company team
Establish a team of data labeling companies, including labeling personnel, quality inspectors, project managers, operation directors, etc.
l？ Tagger data tagger is the core position of data tag company. Its main job is to process the learning data of artificial intelligence with the help of marking tools. General information is pictures, videos, words, etc. By constantly pulling boxes and punctuation marks, enough data sets are provided for artificial intelligence. The entry threshold for applicants is low, and patience and care are needed in the work process.
l？ Quality inspector A quality inspector is a person who selects excellent personnel from the annotators to review and check the marking data. Generally, there are many kinds of items marked by quality inspectors and many scenes encountered, so it is easier and more professional to accurately judge whether the marked elements are correct.
l？ Project manager The project manager is mainly responsible for the overall project management of each project of the company. The project manager must have a deep understanding of the training needs of computer vision, voice engineering, natural language processing and other algorithms, have enough project experience, and can easily enter the project when docking with the demanders. He needs rich experience in communicating needs, coordinating resources, managing projects and controlling progress.
l？ Business needs to go to major AI companies or laboratories to seek cooperation, constantly develop new customers, maintain old customers, and let our company become the supplier of major Party A companies as much as possible.
3. Type of data label company
The types of data label companies are divided into self-built team mode and crowdsourcing mode according to the modes.
l？ Self-built team model
Self-built labeling factory means that the supplier directly sets up a full-time labeling team, and the company will send a suitable professional labeling team and project manager to implement it after receiving the task.
l？ Crowdsourcing
Crowdsourcing mode means that the demander directly publishes tasks on the crowdsourcing platform, and individuals or label teams take over the tasks.
4. What factors should I look for when choosing a good data labeling company?
Judging whether the data labeling company is of high quality can be seen from its company qualification, business ability, team building, technical barriers, data security compliance and other aspects.
l？ Company qualification is supplier qualification.
Whether there are ISO900 1 quality system, ISO2700 1 information security management system and ISO2770 1 privacy information management system, labeling companies that have passed relevant quality and safety management audits generally have mature operation and maintenance systems.
l？ Professional competence
Whether to support multi-data types, multi-algorithm fields, high threshold and high-order data labeling services.
l？ team building
Whether there are mature project managers, mature annotators and quality inspectors; Whether a perfect training system and team management system have been established.
l？ technical barrier
Whether there is a professional marking platform and R&D technical team; Whether the labeling efficiency can be guaranteed by technology.
l？ Data security compliance
Whether the data security is legal and compliant, that is, whether the supplier confidentiality agreement is signed, and the information privacy protection scheme is formulated and improved.
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