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1396650

全球願景變壓器市場 - 2023-2030

Global Vision Transformers Market - 2023-2030

出版日期: 2023年12月15日 | 出版商:

DataM Intelligence | 英文 199 Pages | 商品交期: 約2個工作天內

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簡介目錄

概述

全球願景變壓器市場在2022年達到1.474億美元，預計2030年將達到14.155億美元，2023-2030年預測期間CAGR為33.2%。

隨著機器學習演算法的不斷進步，視覺變換器已成為影像處理的突破性技術。視覺變換器能夠超越局部特徵提取的限制，掌握影像中的全局資訊。與卷積神經網路相比，視覺 Transformer 在各種電腦視覺任務中提供了卓越的效能。

市場上的一些主要參與者相互合作，加速其最先進的模型的發展。例如，2023 年 6 月 13 日，Hugging Face 和 AMD 合作，加速中央處理單元 (CPU) 和圖形處理單元 (GPU) 平台的最先進模型。新的合作關係設定了新的性價比標準。

北美是人工智慧、機器學習和電腦視覺領域的主要研發中心。該地區擁有領先的科技公司、大學和研究機構，他們積極致力於視覺變換器技術的進步。該地區的許多新創公司專注於視覺轉換器的廣泛應用，從醫療保健到自動駕駛汽車。

動力學

自動化需求不斷成長

在製造和工業環境中，視覺轉換器用於品質控制、缺陷檢測和流程最佳化。它實現了生產線上產品檢測的自動化，減少了人工檢測的需要，並提高了生產效率。自動化在零售和電子商務領域至關重要，視覺轉換器用於庫存追蹤、貨架庫存和無收銀結帳系統。這些應用程式簡化了操作並增強了購物體驗。視覺轉換器透過提供即時監控和威脅檢測來實現安全和監控系統的自動化。這對於公共安全和資產保護至關重要。

在農業中，視覺轉換器用於農作物監測、疾病檢測和產量估算等任務。農業自動化有助於最佳化資源利用並提高作物產量。物流和倉儲自動化涉及庫存管理、包裹分類和自動導引車等任務。視覺轉換器透過提供視覺感知能力在最佳化這些過程中發揮作用。

視覺變壓器的卓越性能

視覺轉換器在各種電腦視覺任務中提供卓越的性能，並實現影像分類、物件偵測和語義分割。它捕捉影像中的遠端依賴關係的能力使其成為許多應用程式的首選。視覺轉換器高度適應不同的資料集和影像尺寸，使其用途廣泛，適合廣泛的工業應用。

一些視覺轉換器能夠透過更少的標記訓練範例來實現強大的性能。對於標籤資料有限或資料集較小的企業來說，資料效率特別有吸引力。視覺變換器領域持續的研究和創新促進了新架構、技術和微調策略的發展。該研究正在推動視覺轉換器及其應用的進步。

安裝成本高

視覺轉換器需要大量且多樣化的資料集進行訓練。對於存取標記資料有限的企業或組織來說，取得和準備此類資料集既昂貴又耗時。訓練視覺變換器運算量大且耗時，需要強大的硬體加速器，例如圖形處理單元和張量處理單元。對於資源有限的小型組織來說，這是一個限制。

與傳統的捲積神經網路 (CNN) 相比，視覺變換器具有更大的模型尺寸。這會影響訓練和部署的記憶體和儲存需求。視覺變換器在處理較小的資料集時容易過度擬合，導致泛化性能降低。視覺轉換器中的自註意力機制使得解釋模型決策和理解模型如何達到特定輸出變得具有挑戰性。

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

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