Data Labeling and Annotation Outsourcing Service
New Trends and Challenges in Data Annotation Industry
Data Annotation Industry and Market Size
Data annotation technique is used to make the objects recognizable and understandable for machine learning models. It is critical for the development of machine learning (ML) industries such as face recognition, autonomous driving, aerial drones, and many other AI and robotics applications.
The global data annotation market was valued at US$ 695.5 million in 2019 and is projected to reach US$ 6.45 billion by 2027, according to Research And Markets’ report. Expected to grow at a CAGR of 32.54% from 2020 to 2027, the booming data annotation market is witnessing tremendous growth in the forthcoming future.
The data annotation industry is driven by the increasing growth of the AI industry. At present, the commercialization of Artificial intelligence has reached a stage of basic maturity in terms of computing power and algorithm. In order to better meet the landing needs and solve specific pain points in the industry, scaleable annotated data for algorithm training is still indispensable.
It is said that data determines the success of AI implementation. Moreover, forward-looking data products and highly customized data services have become the mainstream of industry development.
In the next few years, the data annotation industry will have the following trends and challenges.
Trend: Industry Reshuffles, Intensifying Competition
After years of development, the data annotation industry has entered a period of rapid growth.
From the micro point of view, the continuous expansion of the market means more participants and more competition. Due to the low entry threshold and the excessive dependence on human resources, a large number of small and medium-sized data service providers are clustered in the industry.
With the improvement of the technical threshold, the various demands of AI enterprises, and the increase of labor costs, small and medium-sized data service providers will face increasing cost pressure. In the next 1–2 years, the industry will likely usher in a wave of “shuffling period”.
With the speeding up of commercial landing, the AI companies also put forward new requirements for data service. The quality, refinement, and customization are more and more popular on the demand side. On the supply side, technical strength, controlled management, and so on have brought new challenges.
Challenges: the Outmoded Industry Development Under the New Demand
As mentioned, more forward-looking data products and highly customized data services have become the mainstream of industry development. However, the current level of industry development is far from meeting these new needs. The data annotation industry faces the following challenges:
1. Different industries and business scenarios have different requirements for data annotation. The existing annotation ability is not refined enough to support customization services.
Data annotation has a wide range of application scenarios, including autonomous driving, intelligent security, new retail, AI education, industrial robots, intelligent agriculture, and other fields.
Different scenarios have different labeling requirements, for example, the automatic driving industry mainly focuses on pedestrian recognition, vehicle identification, traffic lights, road recognition, etc. The security industry mainly focuses on face recognition, face detection, visual search, key points, and license plate recognition.
2. Customer pain points: low labeling efficiency, poor data quality, lack of human-machine cooperation.
The particularity of the data annotation industry determines its high dependence on manpower. Currently, the mainstream annotation method is that the annotator completes the work with the help of labeling tools.
Due to the uneven ability of the annotators and the imperfect functions of the annotation tools, the data service is always found deficient in efficiency and data quality.
In addition, at present, many data service providers ignore or do not have human-machine cooperation capability, and do not realize the mutual effect of the AI industry on data annotation. In fact, the AI-assisted tool can not only effectively improve efficiency but also greatly improve accuracy.
3. Data labeling service providers, who rely on crowdsourcing and subcontracting, fail to guarantee quality.
At present, data labeling mainly relies on human resources, and human resources account for the most part of the total cost. Therefore, many data service providers give up their in-house labeling teams and turn to subcontract to complete the labeling business.
Compared to the in-house labeling team, crowdsourcing and subcontracting have lower costs and become more flexible. However, the labeling loop is too long to cooperate and data quality is difficult to control. From a long-term perspective, the in-house labeling team is more in line with the needs of industrial development.
To sum up, the data annotation industry has a broad prospect, but it also faces many challenges.
In the foreseeable period of industry transformation, both medium-sized and large-sized data service providers cannot avoid the changement. Only by enhancing the self-developed technical strength and by speeding up the evolution can they be competitive in the new era.
ByteBridge.io, a Human-Powered and ML-powered Data Labeling SAAS Platform
ByteBridge is a labeling SAAS platform with robust tools and real-time workflow management. It provides accurate and consistent high-quality training data for the machine learning industry.
Accuracy
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
- All results are thoroughly assessed and verified by a human workforce and machine
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
Communication Cost Saving
On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.
Here is the operational guideline:
Control your own Project — 2D Images Labeling
In addition, researchers can create the data project by themselves, upload raw data, download processed results, check ongoing labeling progress simultaneously on a pay-per-task model with clear estimated time and take control over the project status.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
In addition, we can provide personalized annotation tools and services according to customer requirements.
Data Security
We comply with principles and rules in each region and we respect data the way your company does.
- The CEO of the company supervises data management as a DPO (Data Protection Officer)
- According to the guideline, if there is data leakage, we will inform the customer within 72 hours
- GDPR personal privacy and data protection regulations compliance
- Workers location, process, and authority restriction
- No original data leak as the data is compressed and preprocessed
- Support private cloud and privatization deployment
- ISO27001 certification for information and facility security
3D Point Cloud Annotation Service
ByteBridge self-developed 3D Point Cloud labeling, quality inspection tool, and pre-labeling functions can complete high-quality and high-precision 3D point cloud annotation for 2D-3D fusion or 3D images provided by different manufacturers and equipment, and provide one-station management service of labeling, QA, and QC.
More info: ByteBridge Launches World’s First Mobile 3D Point Cloud Data Labeling Service
3D Point Cloud Annotation Types:
- Sensor Fusion Cuboids: 49 categories include car, truck, heavy vehicle, two-wheeled vehicle, pedestrian, etc.
- Sensor Fusion Segmentation: obstacles classification, different types of lanes differentiation
- Sensor Fusion Cuboids Tracking
① Tracking the same object with the same ID, labeling the leaving state;
② Time-aligned 2D images could be provided, point clouds outputs only.
Advantages of Our 3D Point Cloud Annotation Service:
· Support 2D to 3D mapping, support multiple cameras
· Support scalable data annotation
· AI-powered sensor fusion tool: labeling at 2X-5X speed
· Ease of using QC tool: real-time revision and synchronous feedback
Cost-effective
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
End
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: support@bytebridge.io
Relevant Articles:
1 How to Get Good Training Data in Machine Learning?
2 Data Labeling Service: Automated Data Labeling VS Manual Data
3 Importance of High-Quality Training Data in Different AI Algorithm Stage
4 How to Make Data Annotation More Efficient?
5 Data Annotation Service and Its Key Advantage — Flexibility
