1. What is data annotation?
1. Data annotation definition
Data annotation is the process of processing unprocessed voice, picture, text, video and other data, and converting it into machine-recognizable information. Raw data is generally obtained through data collection, and the subsequent data labeling is equivalent to processing the data, and then sending it to the artificial intelligence algorithm and model to complete the call.
2. Why do data labeling?
At present, the mainstream machine learning method is mainly based on supervised deep learning method. Under this machine learning method, there is a strong dependence on the labeled data. The raw data that has not been labeled is mostly unstructured data. These data It cannot be recognized and learned by machines. Only the data that has been labeled and processed becomes structured data that can be used by algorithm training.
3. Main types of data annotation
computer vision
Including rectangular box labeling, key point labeling, line segment labeling, semantic segmentation, instance segmentation labeling, ocr labeling, image classification, video labeling, etc.
voice engineering
Including ASR voice transcription, voice cutting, voice cleaning, emotion judgment, voiceprint recognition, phoneme labeling, prosodic labeling, pronunciation proofreading, etc.
natural language understanding
Including ocr transcription, part-of-speech tagging, named entity tagging, sentence generalization, sentiment analysis, sentence writing, slot extraction, intent matching, text judgment, text matching, text information extraction, text cleaning, machine translation, etc.
Autonomous Driving Point Cloud
Including 3D point cloud target detection labeling, 3D point cloud semantic segmentation labeling, 2D3D fusion labeling, point cloud continuous frame labeling, etc.
4. What business scenarios can data labeling be applied to?
1. Intelligent driving
Intelligent driving cars need to use algorithms to deal with a large number of complex scenes, and a large amount of accurate and high-quality data is required to train the algorithm model. The recognition algorithm of the vehicle, pedestrians, obstacles, weather, lane lines, road signs and other external environment, and the fatigue of drivers and passengers Algorithms for monitoring and illegal behavior identification, voice interaction and multi-modal interaction technology in the smart cockpit all require labeled data.
2. Intelligent security
Intelligent security is a key field combining artificial intelligence and information technology, which requires high-quality and accurate data to train and upgrade technology. AI technologies such as access control biometrics, urban road monitoring, vehicle flow monitoring, illegal behavior monitoring, high-altitude parabolic monitoring, and pedestrian re-identification all require data labeling.
3. Smart home
AI-driven smart home, AIoT, which is developing in the same direction, is the current mainstream trend. AI technologies in scenarios such as face recognition, fingerprint recognition access control systems, illegal intrusion detection, sweeping robots, intelligent voice assistants, and intelligent terminal control all require data to be labeled.
4. Smart Finance
AI empowers the traditional financial industry and the retail industry and simplifies the business purchase process. AI technologies such as identity authentication, intelligent customer service, intelligent marketing, intelligent risk control, product images in virtual shopping scenes, bills, face recognition, and designated corpus all require data annotation support.
5. Smart Internet
The smart Internet includes major scenarios such as smart applications, cultural and entertainment interactions, smart search, and content auditing. AI technologies such as chatbots, image-text retrieval, multi-modal intent judgment, sentiment analysis, illegal content auditing, and smart beautification require data labeling support.
6. Smart industry
The four major application scenarios of smart industrial vision are measurement, recognition, guidance, and detection. Algorithms including complex defect detection, helmet reflective clothing recognition, defect detection, pyrotechnics detection, illegal construction detection, and sleeping post detection all require data labeling services.
2. What does the data labeling company mainly do?
- definition
The data labeling company is to assist artificial intelligence companies to solve the corresponding problems in the data labeling link in the entire artificial intelligence chain. The labeling business section can be divided into four categories: image labeling, voice labeling, text labeling, and 3D point cloud labeling, covering computer vision, Speech engineering, natural language processing and other AI application fields.
- Team building for data labeling companies
The team building of the data labeling company includes labelers, quality inspectors, project managers, operations directors, etc.
- Annotator
The data labeler is the core position of the data labeling company. The main job is to process the artificial intelligence learning data with the help of labeling tools. The data is generally pictures, videos, texts, etc., through continuous operations such as drawing frames and punctuation, to provide artificial intelligence Provide a sufficient dataset. Annotators have low entry barriers and need to be patient and careful during the work process.
- quality inspector
The quality inspector is a person who selects excellent personnel from the annotators to review and inspect the marked data. The quality inspector generally has many types of items and encounters many scenarios, so it is easier to accurately judge whether the marked elements are correct. Be more professional.
- project manager
The project manager is mainly responsible for the overall project management of the company's various projects. The project manager must have a deep understanding of the algorithm training needs of computer vision, speech engineering, and natural language processing, and have sufficient project experience. You can easily enter the project at any time, and you need to have rich experience in communicating needs, coordinating resources, managing projects, and controlling progress.
- business
Business needs to go to major AI companies or laboratories to seek cooperation, constantly develop new customers, maintain old customers, and make our company a supplier of major Party A companies as much as possible.
3. Data labeling company type
According to the model, data labeling companies can be divided into two types: self-built team model and crowdsourcing model.
Self-built team mode
Self-built labeling factory means that the supplier directly sets up a full-time labeling team, and after receiving the task, the company will send a suitable professional labeling team and project manager to execute it.
Crowdsourcing
Crowdsourcing mode means that the demand side directly releases tasks on the crowdsourcing platform, and the individual or labeling team takes over and executes them.
4. What factors should be considered when choosing a good data labeling company
Judging whether a data labeling company is high-quality can be based on its company qualifications, business capabilities, team building, technical barriers, and data security compliance.
Company qualification is supplier qualification
Whether there is an ISO9001 quality system, ISO27001 information security management system, ISO27701 privacy information management system, and labeling companies that have passed the relevant quality and safety management review generally have a mature operation and maintenance system.
Operational capacity
Whether to support multi-data types, multi-algorithm fields, high-threshold, high-level data labeling business.
Team building
Whether there are mature project managers, mature labelers, and quality inspectors; whether a sound training system and team management system have been established.
technical barriers
Is there a dedicated What is Data Labeling? What do data labeling companies mainly do? The industry's labeling platform and R&D technical team; whether technology can be used to ensure labeling efficiency.
Data Security Compliance
Whether data security is legal and compliant, that is, whether to sign a supplier confidentiality agreement, formulate and improve information privacy protection plans, etc.
JLW Technology
Provide AI data collection, data labeling, data set products, fake fingerprint collection and fingerprint anti-counterfeiting algorithm services for thousands of artificial intelligence companies and university scientific research institutions around the world. Jinglianwen has always practiced the corporate mission of "being the data consultant of customers in the global AI industry", helping artificial intelligence technology to accelerate the quality change, power change and efficiency change of industries related to the digital economy, and empowering the intelligent transformation and upgrading of traditional industries.
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