查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
全球愿景 变压器市场 - 2023-2030
市场调查报告书
商品编码
1396650

全球愿景 变压器市场 - 2023-2030

Global Vision Transformers Market - 2023-2030
出版日期: | 出版商: DataM Intelligence | 英文 199 Pages | 商品交期: 约2个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

概述

全球愿景变压器市场在2022年达到1.474亿美元，预计2030年将达到14.155亿美元，2023-2030年预测期间CAGR为33.2%。

随着机器学习演算法的不断进步，视觉变换器已成为影像处理的突破性技术。视觉变换器能够超越局部特征提取的限制，掌握影像中的全局资讯。与卷积神经网路相比，视觉 Transformer 在各种电脑视觉任务中提供了卓越的效能。

市场上的一些主要参与者相互合作，加速其最先进的模型的发展。例如，2023 年 6 月 13 日，Hugging Face 和 AMD 合作，加速中央处理单元 (CPU) 和图形处理单元 (GPU) 平台的最先进模型。新的合作关係设定了新的性价比标准。

北美是人工智慧、机器学习和电脑视觉领域的主要研发中心。该地区拥有领先的科技公司、大学和研究机构，他们积极致力于视觉变换器技术的进步。该地区的许多新创公司专注于视觉转换器的广泛应用，从医疗保健到自动驾驶汽车。

动力学

自动化需求不断成长

在製造和工业环境中，视觉转换器用于品质控制、缺陷检测和流程最佳化。它实现了生产线上产品检测的自动化，减少了人工检测的需要，并提高了生产效率。自动化在零售和电子商务领域至关重要，视觉转换器用于库存追踪、货架库存和无收银结帐系统。这些应用程式简化了操作并增强了购物体验。视觉转换器透过提供即时监控和威胁检测来实现安全和监控系统的自动化。这对于公共安全和资产保护至关重要。

在农业中，视觉转换器用于农作物监测、疾病检测和产量估算等任务。农业自动化有助于优化资源利用并提高作物产量。物流和仓储自动化涉及库存管理、包裹分类和自动导引车等任务。视觉转换器透过提供视觉感知能力在优化这些过程中发挥作用。

视觉变压器的卓越性能

视觉转换器在各种电脑视觉任务中提供卓越的性能，并实现影像分类、物件侦测和语义分割。它捕捉影像中的远端依赖关係的能力使其成为许多应用程式的首选。视觉转换器高度适应不同的资料集和影像尺寸，使其用途广泛，适合广泛的工业应用。

一些视觉转换器能够透过更少的标记训练范例来实现强大的性能。对于标籤资料有限或资料集较小的企业来说，资料效率特别有吸引力。视觉变换器领域持续的研究和创新促进了新架构、技术和微调策略的发展。该研究正在推动视觉转换器及其应用的进步。

安装成本高

视觉转换器需要大量且多样化的资料集进行训练。对于存取标记资料有限的企业或组织来说，取得和准备此类资料集既昂贵又耗时。训练视觉变换器运算量大且耗时，需要强大的硬体加速器，例如图形处理单元和张量处理单元。对于资源有限的小型组织来说，这是一个限制。

与传统的捲积神经网路 (CNN) 相比，视觉变换器具有更大的模型尺寸。这会影响训练和部署的记忆体和储存需求。视觉变换器在处理较小的资料集时容易过度拟合，导致泛化性能降低。视觉转换器中的自註意力机制使得解释模型决策和理解模型如何达到特定输出变得具有挑战性。

目录

第 1 章：方法与范围

  • 研究方法论
  • 报告的研究目的和范围

第 2 章：定义与概述

第 3 章：执行摘要

  • 按产品分类的片段
  • 按应用程式片段
  • 最终使用者的片段
  • 按地区分類的片段

第 4 章：动力学

  • 影响因素
    • 司机
      • 自动化需求不断成长
      • 视觉变压器的卓越性能
    • 限制
      • 安装成本高
    • 机会
    • 影响分析

第 5 章：产业分析

  • 波特五力分析
  • 供应链分析
  • 定价分析
  • 监管分析
  • 俄乌战争影响分析
  • DMI 意见

第 6 章：COVID-19 分析

  • COVID-19 分析
    • 新冠疫情爆发前的情景
    • 新冠疫情期间的情景
    • 新冠疫情后的情景
  • COVID-19 期间的定价动态
  • 供需谱
  • 疫情期间政府与市场相关的倡议
  • 製造商策略倡议
  • 结论

第 7 章：透过奉献

  • 解决方案
  • 专业的服务
    • 部署与安装
    • 支援与维护
    • 咨询
  • 其他的

第 8 章：按应用

  • 影像分割
  • 物体侦测
  • 图片字幕
  • 其他的

第 9 章：最终用户

  • 媒体与娱乐
  • 零售与电子商务
  • 汽车
  • 资讯科技和电信
  • 政府和国防
  • 教育
  • 其他的

第 10 章：按地区

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 俄罗斯
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 亚太其他地区
  • 中东和非洲

第 11 章：竞争格局

  • 竞争场景
  • 市场定位/份额分析
  • 併购分析

第 12 章：公司简介

  • Google
    • 公司简介
    • 产品组合和描述
    • 财务概览
    • 主要进展
  • OpenAI
  • Meta
  • AWS
  • NVIDIA Corporation
  • LeewayHertz
  • Synopsys
  • Hugging Face
  • Microsoft
  • Qualcomm

第 13 章：附录

简介目录
Product Code: ICT7551

Overview

Global Vision Transformers Market reached US$ 147.4 million in 2022 and is expected to reach US$ 1,415.5 million by 2030, growing with a CAGR of 33.2% during the forecast period 2023-2030.

With the growing advancements in machine learning algorithms, Vision Transformers have emerged as a groundbreaking technique for image processing. Vision Transformers are able to grasp global information within images transcending the limitations of local feature extraction. Vision Transformers give superior performance compared to convolutional neural networks in various computer vision tasks.

Some major key players in the market collaborated with each other to accelerate its state-of-the-art models. For instance, On June 13, 2023, Hugging Face and AMD partnered together to accelerate state-of-the-art models for central processing unit (CPU) and graphics processing unit (GPU) platforms. The new partnership set a new cost performance standard.

North America is a major hub for research and development in artificial intelligence, machine learning and computer vision. The region is home to leading tech companies, universities and research institutions that are actively working on vision transformer technology advancements. Many startups in the region focus on vision transformers wide range of applications, from healthcare to autonomous vehicles.

Dynamics

Growing Demand for Automation

In manufacturing and industrial settings, vision transformers are used for quality control, defect detection and process optimization. It automates the inspection of products on production lines, reducing the need for manual inspection and improving production efficiency. Automation is essential in the retail and e-commerce sectors, where vision transformers are used for inventory tracking, shelf stocking and cashierless checkout systems. The applications streamline operations and enhance the shopping experience. Vision transformers automate security and surveillance systems by providing real-time monitoring and threat detection. The is essential for public safety and asset protection.

In agriculture, vision transformers are used for tasks such as crop monitoring, disease detection and yield estimation. Automation in agriculture helps optimize resource utilization and improve crop yields. Automation in logistics and warehousing involves tasks like inventory management, package sorting and autonomous guided vehicles. Vision transformers play a role in optimizing these processes by providing visual perception capabilities.

Superior Performance of Vision Transformer

Vision transformers give superior performance in various computer vision tasks and result in image classification, object detection and semantic segmentation. Its ability to capture long-range dependencies in images has made them a preferred choice for many applications. Vision transformers are highly adaptable to different datasets and image sizes, making them versatile and suitable for a wide range of industrial applications.

Some vision transformers have the capability to achieve strong performance with fewer labeled training examples. The data efficiency is particularly appealing for businesses with limited labeled data or small datasets. Ongoing research and innovation in the field of vision transformers have led to the development of new architectures, techniques and fine-tuning strategies. The research is driving the advancement of vision transformers and their applications.

High Installation Cost

Vision transformers require large and diverse datasets for training. Acquiring and preparing such datasets is costly and time-consuming for businesses or organizations with limited access to labeled data. Training vision transformers are computationally intensive and time-consuming, requiring powerful hardware accelerators such as graphical processing units and tensor processing units. The is a limitation for smaller organizations with resource constraints.

Vision transformers have larger model sizes compared to traditional convolutional neural networks (CNNs). The impacts memory and storage requirements for both training and deployment. Vision transformers are prone to overfitting when dealing with smaller datasets, which leads to reduced generalization performance. The self-attention mechanisms in vision transformers make it challenging to interpret model decisions and understand how the model arrived at a particular output.

Segment Analysis

The global vision transformers market is segmented based on offering, application, end-user and region.

Solutions Offering Segment Dominating the Vision Transformers Market

Based on the offering, the global vision transformer market is divided into solutions, professional services and others. The vision transformers solutions segment accounted for the largest market share in the global vision transformers market. Vision transformers give superior performance in many computer vision tasks and have achieved state-of-the-art results in object detection and image classification. Its ability to capture long-range dependencies in images has made it a preferred choice for many applications.

Vision transformers are highly adaptable to different datasets and image sizes, making them suitable for various applications across various industries such as media & entertainment, retail & e-commerce and others. Some vision transformers have the capability to achieve strong performance. The data efficiency is particularly appealing for businesses with limited labeled data. Growing research and innovation in the field of vision transformers have led to the development of new techniques, architectures and fine-tuning strategies. The research is driving the advancement of vision transformers and their applications.

Geographical Penetration

Growing Adoption of the Vision Transformers in North America

North America is dominating the global vision transformers market due to various factors such as large enterprises with sophisticated IT infrastructure. The U.S. and Canada accounted for the largest share of the vision transformer market due to the growing adoption of innovative solutions.

Growing investment in AI by the major key players in the region such as Microsoft, Google, Facebook and Amazon helped to boost market growth. Major key players in the region follow merger and acquisition strategies to expand their business. For instance, on August 15, 2023, Edge Impulse, a machine learning development platform completed a partnership with AWS for the integration of Nvidia TAO toolkit 5.0. With the Nvidia TAO toolkit integration developers access pre-trained AI models tailored to computer vision applications.

Competitive Landscape

The major global players in the market include: Google, OpenAI, Meta, AWS, NVIDIA Corporation, LeewayHertz, Synopsys, Hugging Face, Microsoft and Qualcomm.

COVID-19 Impact Analysis

The pandemic disrupted research activities, including data collection, experimentation and collaboration, which are vital for the development and improvement of vision transformers. Many research institutions and labs had to limit their operations. The pandemic disrupted the supply chain for hardware components, such as GPUs and specialized hardware accelerators, which are crucial for training and deploying vision transformers. Shortages and delays in hardware availability affected research and development efforts.

Data labeling, a critical step in training machine learning models, was hampered as crowdsourcing and in-person data labeling activities were limited due to social distancing measures. Some vision transformers research institutions and organizations had to shift their priorities temporarily to focus on COVID-19-related projects or to address pandemic-related challenges.

Economic uncertainty during the pandemic led to caution in investment and funding for research and development projects, including those related to vision transformers. Startups and research initiatives faced challenges in securing funding.

Russia-Ukraine War Impact Analysis

The conflict between Russia and Ukraine disrupts the global supply chain for hardware components like GPUs and specialized hardware accelerators used in training and deploying vision transformers. The disruptions affect the production and availability of vision transformers-related technologies, potentially leading to delays and increased costs. Geopolitical tensions and sanctions affect research collaboration between institutions and researchers in different regions. It hinders the progress of vision transformers research and development as international cooperation has been instrumental in many technological advancements.

Restrictions on travel and work visas negatively impact the mobility of talent in the field of computer vision, including vision transformers. It affects the ability of key players to attract and retain top talent from globally. Research institutions and major key players need to allocate resources and investments differently in response to geopolitical challenges. The impacted the focus and funding available for vision transformers research and development.

By Offering

  • Solutions
  • Professional Services
  • Others

By Application

  • Image Segmentation
  • Object Detection
  • Image Captioning

By End-User

  • Media & Entertainment
  • Retail & ECommerce
  • Automotive
  • Others

By Region

  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • France
    • Italy
    • Russia
    • Rest of Europe
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa

Key Developments

  • On August 22, 2023, Apple released mobile-friendly vision transformers in the market which is designed for quick and easy deployment. FastViT's performance is impressive on the ImageNet dataset, the architecture is 3.5x faster than CMT and 4.9x faster than Efficient Net.
  • On June 20, 2023, Quadric, announced its Chimera general-purpose neural processing unit processor intellectual property supports vision transformer (ViT) machine learning (ML) inference models. ViT models are the latest state-of-the-art ML models for image and vision processing in embedded systems.
  • On July 25, 2023, NVIDIA TAO Toolkit 5.O provided a low-code AI framework to accelerate the vision AI model which is suitable for skill levels from novice beginners to expert data scientists. The new features include transformer-based pre-trained models, source-open architecture and AI-assisted data annotation.

Why Purchase the Report?

  • To visualize the global vision transformers market segmentation based on offering, application, end-user and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities by analyzing trends and co-development.
  • Excel data sheet with numerous data points of vision transformers market-level with all segments.
  • PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
  • Product mapping available as excel consisting of key products of all the major players.

The global vision transformers market report would provide approximately 61 tables, 62 figures and 199 Pages.

Target Audience 2023

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Offering
  • 3.2. Snippet by Application
  • 3.3. Snippet by End-User
  • 3.4. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Growing Demand for Automation
      • 4.1.1.2. Superior Performance of Vision Transformer
    • 4.1.2. Restraints
      • 4.1.2.1. High Installation Cost
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis
  • 5.5. Russia-Ukraine War Impact Analysis
  • 5.6. DMI Opinion

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID
    • 6.1.2. Scenario During COVID
    • 6.1.3. Scenario Post COVID
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Offering

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 7.1.2. Market Attractiveness Index, By Offering
  • 7.2. Solutions*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Professional Services
    • 7.3.1. Deployment & Installation
    • 7.3.2. Support & Maintenance
    • 7.3.3. Consulting
  • 7.4. Others

8. By Application

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 8.1.2. Market Attractiveness Index, By Application
  • 8.2. Image Segmentation*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Object Detection
  • 8.4. Image Captioning
  • 8.5. Others

9. By End-User

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 9.1.2. Market Attractiveness Index, By End-User
  • 9.2. Media & Entertainment*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. Retail & ECommerce
  • 9.4. Automotive
  • 9.5. IT and Telecom
  • 9.6. Government and Defense
  • 9.7. Education
  • 9.8. Others

10. By Region

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 10.1.2. Market Attractiveness Index, By Region
  • 10.2. North America
    • 10.2.1. Introduction
    • 10.2.2. Key Region-Specific Dynamics
    • 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.2.6.1. U.S.
      • 10.2.6.2. Canada
      • 10.2.6.3. Mexico
  • 10.3. Europe
    • 10.3.1. Introduction
    • 10.3.2. Key Region-Specific Dynamics
    • 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.3.6.1. Germany
      • 10.3.6.2. UK
      • 10.3.6.3. France
      • 10.3.6.4. Italy
      • 10.3.6.5. Russia
      • 10.3.6.6. Rest of Europe
  • 10.4. South America
    • 10.4.1. Introduction
    • 10.4.2. Key Region-Specific Dynamics
    • 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.4.6.1. Brazil
      • 10.4.6.2. Argentina
      • 10.4.6.3. Rest of South America
  • 10.5. Asia-Pacific
    • 10.5.1. Introduction
    • 10.5.2. Key Region-Specific Dynamics
    • 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
    • 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 10.5.6.1. China
      • 10.5.6.2. India
      • 10.5.6.3. Japan
      • 10.5.6.4. Australia
      • 10.5.6.5. Rest of Asia-Pacific
  • 10.6. Middle East and Africa
    • 10.6.1. Introduction
    • 10.6.2. Key Region-Specific Dynamics
    • 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
    • 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

  • 11.1. Competitive Scenario
  • 11.2. Market Positioning/Share Analysis
  • 11.3. Mergers and Acquisitions Analysis

12. Company Profiles

  • 12.1. Google*
    • 12.1.1. Company Overview
    • 12.1.2. Product Portfolio and Description
    • 12.1.3. Financial Overview
    • 12.1.4. Key Developments
  • 12.2. OpenAI
  • 12.3. Meta
  • 12.4. AWS
  • 12.5. NVIDIA Corporation
  • 12.6. LeewayHertz
  • 12.7. Synopsys
  • 12.8. Hugging Face
  • 12.9. Microsoft
  • 12.10. Qualcomm

LIST NOT EXHAUSTIVE

13. Appendix

  • 13.1. About Us and Services
  • 13.2. Contact Us