表紙:ビジョントランスフォーマーの世界市場-2023年~2030年
市場調査レポート
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1396650

ビジョントランスフォーマーの世界市場-2023年~2030年

Global Vision Transformers Market - 2023-2030

出版日: | 発行: DataM Intelligence | ページ情報: 英文 199 Pages | 納期: 約2営業日

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価格
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ビジョントランスフォーマーの世界市場-2023年~2030年
出版日: 2023年12月15日
発行: DataM Intelligence
ページ情報: 英文 199 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
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  • 概要
  • 目次
概要

概要

ビジョントランスフォーマーの世界市場は、2022年に1億4,740万米ドルに達し、2023-2030年の予測期間中に33.2%のCAGRで成長し、2030年には14億1,550万米ドルに達すると予測されています。

機械学習アルゴリズムの進歩に伴い、ビジョントランスフォーマーは画像処理の画期的な技術として台頭してきました。ビジョントランスフォーマーは、局所的な特徴抽出の限界を超え、画像内の世界な情報を把握することができます。ビジョントランスフォーマーは、様々なコンピュータ・ビジョン・タスクにおいて、畳み込みニューラルネットワークと比較して優れた性能を発揮します。

同市場の主要なプレーヤーは、最先端のモデルを加速させるために互いに協力しています。例えば、2023年6月13日、ハギング・フェイスとAMDは、中央処理装置(CPU)およびグラフィックス・プロセッシング・ユニット(GPU)プラットフォーム向けの最先端モデルを加速するために提携しました。この新たな提携は、新たなコストパフォーマンスの基準を打ち立てた。

北米は人工知能、機械学習、コンピュータービジョンの研究開発の主要拠点です。この地域には、ビジョントランスフォーマー技術の進歩に積極的に取り組んでいる大手ハイテク企業、大学、研究機関があります。この地域の多くの新興企業は、ヘルスケアから自律走行車まで、ビジョントランスフォーマーの幅広い用途に注力しています。

力学

自動化への需要の高まり

製造・産業現場において、ビジョントランスは品質管理、欠陥検出、プロセス最適化に使用されています。生産ラインでの製品検査を自動化することで、手作業による検査の必要性を減らし、生産効率を向上させます。自動化は小売業やeコマース分野で不可欠であり、ビジョントランスフォーマーは在庫追跡、棚卸し、レジなし精算システムなどに使用されています。このアプリケーションはオペレーションを合理化し、ショッピング体験を向上させます。ビジョントランスフォーマーはリアルタイムの監視と脅威の検出を提供することで、セキュリティと監視システムを自動化します。これは公共の安全と資産保護に不可欠です。

農業分野では、作物のモニタリング、病気の検出、収穫量の推定などの作業にビジョントランスが使用されています。農業における自動化は、資源利用を最適化し、作物の収量を向上させるのに役立ちます。物流や倉庫管理の自動化には、在庫管理、荷物の仕分け、自律走行車などのタスクが含まれます。ビジョントランスフォーマーは、視覚認識機能を提供することで、これらのプロセスを最適化する役割を果たします。

ビジョントランスフォーマーの優れた性能

ビジョントランスフォーマーは、様々なコンピュータ・ビジョン・タスクにおいて優れた性能を発揮し、画像分類、物体検出、セマンティック・セグメンテーションを実現します。画像内の長距離依存関係を捕捉する能力により、多くの用途で好まれる選択肢となっています。ビジョン変換器は様々なデータセットや画像サイズに適応できるため、汎用性が高く、幅広い産業用途に適しています。

ビジョン変換器の中には、少ないラベル付けされた学習例で強力な性能を達成できるものもあります。このようなデータ効率は、ラベル付けされたデータが限られていたり、データセットが少なかったりするビジネスにとって特に魅力的です。ビジョントランスフォーマー分野の継続的な研究開発と技術革新は、新しいアーキテクチャ、技術、微調整戦略の開発につながりました。この調査がビジョントランスフォーマーとその応用の進歩を後押ししています。

高い導入コスト

視覚変換器には、トレーニング用の大規模で多様なデータセットが必要です。ラベル付きデータへのアクセスが限られている企業や組織にとって、このようなデータセットの取得と準備はコストと時間がかかります。視覚変換器のトレーニングは計算集約的で時間がかかり、グラフィカル・プロセッシング・ユニットやテンソル・プロセッシング・ユニットなどの強力なハードウェア・アクセラレータが必要です。これは、リソースに制約のある小規模な組織にとっては制限となります。

ビジョントランスフォーマーは、従来の畳み込みニューラルネットワーク(CNN)に比べてモデルサイズが大きく、これは、学習と展開の両方に必要なメモリとストレージに影響します。視覚変換器は、小さいデータセットを扱うときにオーバーフィッティングを起こしやすく、汎化性能の低下につながります。視覚変換器の自己注意メカニズムは、モデルの決定を解釈し、モデルがどのようにして特定の出力に到達したかを理解することを困難にします。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • 自動化需要の高まり
      • ビジョントランスフォーマーの優れた性能
    • 抑制要因
      • 高い設置コスト
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • ロシア・ウクライナ戦争の影響分析
  • DMIの見解

第6章 COVID-19分析

第7章 オファリング別

  • ソリューション
  • プロフェッショナルサービス
    • 配備と設置
    • サポート&メンテナンス
    • コンサルティング
  • その他

第8章 用途別

  • 画像セグメンテーション
  • オブジェクト検出
  • 画像キャプション
  • その他

第9章 エンドユーザー別

  • メディア&エンターテイメント
  • 小売・EC
  • 自動車
  • IT・通信
  • 政府・防衛
  • 教育
  • その他

第10章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第11章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第12章 企業プロファイル

  • Google
    • 会社概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な発展
  • OpenAI
  • Meta
  • AWS
  • NVIDIA Corporation
  • LeewayHertz
  • Synopsys
  • Hugging Face
  • Microsoft
  • Qualcomm

第13章 付録

目次
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