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How Data Annotation Service Empowers AR/VR? — Part2

What is the Future Development of the Industry?

A Glance of Chinese AR/VR Market

VR, as a key technology sector that the government is focusing on, will vigorously promote technology development and investment in related fields.

IDC predicts that China’s AR/VR market begins to grow substantially from 2021, and the compound growth rate of the AR/VR market will reach 77.2% in the next five years. Zhao Siquan, an analyst at IDC’s China Terminal System Research Department, said that the current domestic VR headset manufacturers’ competition landscape has undergone significant changes this year. For a long time in the past, they have been paying attention to the commercial market with education and training as a typical scenario.

Focus on Consumer Market

Since the beginning of this year, with the advent of low-cost all-in-one products, many manufacturers have shifted their strategic focus to the consumer market. Different manufacturers have made new strategic choices based on their own characteristics and product positioning in the new outlet, which will surely bring new challenges to the competitive environment. Supporting games and content applications are gradually enriched, and demand beyond games is yet to be released.

In the consumer market, in addition to games and entertainment, whether VR can develop new demands based on its own immersive experience and interactive advantages, such as fitness, virtual social networking, etc., is also a new aspect worth looking forward to.

More read: How AI empowers the fitness industry?

Commando VR Training System

The 28th China Electric Power Research Institute has released the “Smart Commando Virtual Reality (VR) Training System”, a unit-level immersive simulation training system that constructs a virtual parallel battlefield space, which can truly simulate various battlefield environments and provide micro-posture display support, red and blue virtual and real AI confrontation, post-war analysis, multi-person immersive collaborative training.

It achieves the three-terminal coordination of guidance, command, and combat as well as helping troops verify tactics through training and quickly improve their actual combat capabilities.

Earlier, the US Army signed a $480 million contract with Microsoft to use 100,000 customized HoloLens headsets for battlefield training. The U.S. Department of Defense has also publicly tendered information on existing solutions for virtual training and testing platforms for U.S. defense forces operating in battlefield nuclear warfare (BNW) environments or performing radioactive threat object finding and interception operations.

Why the High-Quality Training Data is so Important to AI Machine Learning?

From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.

In fact, getting high-quality labeled data is the toughest part of building a machine learning model. If the data quality is unqualified, the algorithm model cannot be well developed, AI company needs to label the data again. Timing is important, once, behind the schedule, the product may be overtaken by competitors.

In the process of machine learning project development, 25% of the time is used for data annotation. Only 5% of the time is spent on training algorithms. The reasons for spending a lot of time on data labeling are as follows:

The algorithm engineer needs to go through repeated tests to determine which label data is more suitable for the training algorithm.

Training a model needs tens of thousands or even millions of training data, which takes a lot of time. For example, an in-house team composed of 10 labelers and 3 QA inspectors can complete around 10,000 automatic driving lane image labeling in 8 days.

ByteBridge.io, a Human-Powered and ML-powered Data Annotation Platform

ByteBridge, a data labeling tooling platform with real-time workflow management, provides training data for the machine learning industry.

Accuracy Guarantee

  • 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 work results are completely screened and inspected by the machine and human workforce
ByteBridge: a Human-powered and ML-powered Data Labeling SAAS Platform

In this way, ByteBridge can affirm our 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.

ByteBridge Labeling Guideline Templates

For example, you can choose a Bounding Box and Classification Template on the dashboard:

ByteBridge Data Labeling Platform Tutorial: Bounding Box and Classification Template Updated

Configure Your Own Annotation Project

In addition, clients can iterate data features, attributes, and workflow, scale up or down, make changes based on what they are learning about the model’s performance in each step of test and validation.

As a fully managed platform, it enables developers to manage and monitor the overall data labeling process and provides API for data transfer. The platform also allows users to get involved in the QC process.

ByteBridge: a Human-powered and ML-powered Data Labeling SaaS Platform

These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.

We can provide personalized annotation tools and services according to customer requirements.

Cost-effective

A collaboration of the human-work force and AI algorithms ensure a 50%lower price compared to the conventional market.

End

“High-quality data is the fuel that keeps the AI engine running smoothly. The more accurate annotation is, the better algorithm performance will be” said Brian Cheong, founder, and CEO of ByteBridge.

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

Source: https://baijiahao.baidu.com/sid=1709510388683092287&wfr

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ByteBridge

ByteBridge

A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. — https://bytebridge.io/#/

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