Summary

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

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