Vision Transformers Market

The global Vision Transformers Market is expected to grow from USD 0.2 billion in 2023 to USD 1.2 billion by 2028, achieving a compound annual growth rate (CAGR) of 34.2% during the forecast period. Some important factors that boost the growth of the vision transformers market include the increasing demand for attention mechanisms, transfer learning, and technology advancements. The increasing use of computer vision in various industries for tasks like image recognition, object detection, and video analysis is another factor for market growth. Several tech companies and startups are actively working on vision transformer models and applications, leading to increased innovation and competition in the market. This competition is driving advancements and expanding the market's offerings.

To know about the assumptions considered for the study, Request for Free Sample Report

To know about the assumptions considered for the study, download the pdf brochure

Recession Impact on the Vision Transformers Market

This report includes an analysis of the impact of the global recession on the vision transformers market. In this fast-changing environment, the exact effect of the downturn is unknown to the world. Hence, scenario-based approaches, such as rising interest rates, the weakening of currencies, and mounting public debt, have been considered to assess the economic impact and recovery period at the global level. The impact and recovery period for each country/region will be different.

Rising inflation, increasing interest rates, unemployment, and energy crises will lead to slow economic growth. As a result, end-user industries experience the deterioration of their businesses, cash flow, and ability to obtain financing, delaying or canceling product purchase plans. Similarly, vendors who provide electronic components to these OEMs experience similar problems, which impact their ability to fulfill orders or meet agreed service and quality levels.

The demand for components such as AI in computer vision in end-use markets depends primarily on CAPEX by operators/organizations for constructing, rebuilding, or upgrading their networking systems. The recession affects the amount of CAPEX spending and the company’s sales and profitability; there can be no assurance that existing capital spending will continue or that spending will not decrease during the economic downturn, which might harm the adoption of vision transformers hardware equipment such as CPUs, GPUs, ASICs, FPGAs, storage, and memory devices used in residential and industrial environments.

According to the World Economic Forum, adopting AI will create USD 13 trillion in value by 2030, and the current economic slowdown is likely to accelerate this trend as companies seek to streamline their operations and stay competitive. The World Bank has predicted that the global economic downturn will push enterprises toward employee layoffs and adopting AI-assisted tools; this would usher in a potential increase in investment in vision transformer solutions as businesses seek to leverage synthetically generated data to drive growth and competitiveness. Considering the persistent supply-chain disruption, supply-chain diversification and digitalization are the priorities for most organizations in the coming years.

Vision Transformers Market Dynamics

Driver: Increasing demand for automation

Automation is a driving force across industries, and vision transformation technologies play a pivotal role in automating tasks such as quality control, object detection, and visual inspection. Automation and efficiency can help businesses save time and resources and improve the accuracy of decision-making processes; this is why many industries are now implementing vision transformers to automate their processes and enhance efficiency.

In retail, computer vision systems track inventory levels and sales trends to aid retailers in making informed decisions about which products to stock and how to allocate resources. The medical field also benefits from vision transformers. For instance, computer vision systems can analyze medical images to help diagnose and treat diseases, reducing the time and resources required for manual analysis.

Restraint: High installation cost

The high cost of making image recognition systems could hinder the market’s growth. Most enabling technologies, such as face recognition, deep learning, computer vision, AI, ML, and gesture recognition, have substantial development costs. Thus, companies that lack financial resources do not opt for image recognition offerings even if they are interested in such solutions to increase productivity. Well-known vendor solutions such as Microsoft Computer Vision API, Microsoft Emotion API, Amazon Rekognition, Google Cloud Vision API, and IBM Watson Visual Recognition are highly priced, making it difficult for small companies to deploy them. The massive cost of implementing image recognition solutions and training AI enablers to execute specific tasks would deter small businesses; this could be a restraint for image recognition solution vendors.

Deploying vision transformers often requires specialized hardware, such as high-performance GPUs or TPUs, to efficiently process and infer on large-scale models. These hardware components can be expensive to purchase and install, especially considering the infrastructure needed to support them, such as servers or data center facilities. The initial investment in hardware can be a substantial barrier for organizations, notably smaller businesses or startups with limited budgets. High installation costs may delay or deter their adoption of vision transformers for image recognition tasks. In addition to hardware, installing image recognition solutions often involves infrastructure expenses; this includes the cost of setting up and maintaining data centers, cloud computing resources, or edge computing facilities capable of accommodating the computational demands of vision transformers. Deploying vision transformers and image recognition solutions in real-world environments may involve integration with existing systems, software development, and maintenance costs. These infrastructure expenses can strain an organization’s financial resources.

Opportunity: Integration of AI capabilities

Integrating AI techniques with vision transformation enables more advanced and accurate image analysis, enhancing decision-making processes in applications like autonomous vehicles and industrial automation. Major image recognition market players like Microsoft and their partners enable companies across different verticals to optimize business operations efficiently by transitioning from manual to AI-based operations. For example, Clobotics, a Chinese intelligent computer vision provider for retailers, has developed the Cloud Image Recognition solution that uses AI, advanced computer vision, and machine learning technologies to provide real-time insights on product placement, shelf optimization, product tracking, and planogram compliance through Microsoft Power BI. Microsoft’s Australia-based partner Lakeba has combined its computer vision technology with intelligent image capturing and Microsoft Azure, a cloud-based data analytics solution, to provide optimal on-shelf stock management. AI-powered image recognition solutions interpret humongous amounts of data and provide actionable insights that are impossible to achieve with human operations. Hence, the proliferation of AI and Machine Learning (ML) technology amalgamated with image recognition capabilities will create opportunities for vision transformers.

Challenge: Limited understanding and technical expertise in vision transformers

Limited understanding and technical expertise in vision transformers are critical challenges for the market. AI and computer vision are complex technologies that require specialized knowledge and skills to implement effectively; this can make it difficult for organizations to fully understand the capabilities and limitations of these systems and utilize them to solve real-world problems effectively. Additionally, vision transformer systems can be highly technical, requiring advanced programming skills and a deep understanding of mathematical algorithms and machine learning concepts. It makes it challenging for organizations to find and hire the technical expertise to develop and deploy these systems. Another challenge is the limited understanding of vision transformer systems’ ethical and societal implications. As these systems become more prevalent, organizations need to understand the potential impact that they may have on privacy, security, and other important societal values.

Despite these challenges, many initiatives are underway to address these limitations, such as educational programs and online courses to increase understanding and technical expertise in vision transformers. Additionally, as the field grows, more organizations will likely invest in developing the necessary technical expertise to implement these systems effectively. While limited understanding and technical expertise in vision transformers are challenges, there are opportunities for future growth and development. As more organizations invest in building their expertise and understanding of these technologies, they will likely become more widely adopted and utilized in various applications and industries.

Vision Transformers Market Ecosystem

This section highlights the vision transformers ecosystem comprising software providers, hardware providers, AI framework developers, cloud computing providers, and verticals. Hardware providers such as NVIDIA, Intel, and AMD supply GPUs, TPUs, and other accelerators optimized for deep learning workloads. They are critical in enabling efficient vision transformer model training and inference. Software providers offer tools, libraries, and pre-trained models that facilitate developing, training, and deploying vision transformer models. Examples include Hugging Face Transformers and TensorFlow Hub. Cloud providers like AWS, Microsoft Azure, and Google Cloud offer cloud-based infrastructure, AI services, and GPU/TPU instances for vision transformer model development and deployment. Organizations like Facebook AI Research, Google AI, and OpenAI develop deep learning frameworks (e.g., PyTorch, TensorFlow) and libraries that support vision transformer model development. The verticals adopt vision transformer solutions to enhance their operations, automate tasks, and gain insights from visual data.

The deployment & integration segment will witness the second-highest CAGR during the forecast period based on professional service.

Deployment and integration services in the Vision Transformers market are specialized offerings aimed at helping organizations integrate vision transformer models into their existing systems, applications, and workflows, ensuring that they operate seamlessly and deliver value in real-world scenarios. These services involve the technical aspects of setting up and configuring vision transformer solutions for production use. Deployment services focus on taking vision transformer models from the development and training stage to operational use; this includes setting up the necessary infrastructure and environments to host the models. Service providers help organizations set up the hardware and software infrastructure for deploying Vision Transformers models.

Based on application, the image segmentation segment holds the largest market share in 2023.

Image segmentation in the vision transformers market divides images into meaningful and distinct segments or regions. Each element typically corresponds to a particular object, area, or category within the image. Vision transformers, which have gained popularity for their ability to capture complex relationships in visual data, are increasingly used for image segmentation tasks. Image segmentation is crucial in computer vision for various applications, including object detection, medical imaging, autonomous vehicles, and more. The goal is to assign each pixel in an image to a specific class or label, effectively partitioning the image into regions of interest. Vision transformers are neural network architectures initially designed for image classification but adapted for image segmentation tasks.

The US market contributes the largest share of North America’s vision transformers market during the forecast period.

The US is estimated to account for North America’s most significant share of the vision transformers market in 2023, and the trend will continue until 2028. Due to several factors, including advanced IT infrastructure, numerous businesses, and the availability of technical skills, it is the most developed market for adopting vision transformers. The United States has prominent technology hubs, including Silicon Valley, Seattle, Boston, and the San Francisco Bay Area. These regions host a concentration of tech companies, startups, and research institutions focused on artificial intelligence (AI) and computer vision, making them hotbeds for Vision transformers development and adoption. Leading US tech giants like Google, Facebook (now Meta), Microsoft, and Amazon actively engage in vision transformers research and development.

Key Market Players

The key technology vendors in the market include Google (US), OpenAI (US), Meta (US), AWS (US), NVIDIA Corporation (US), LeewayHertz (US), Synopsys (US), Hugging Face (US), Microsoft (US), Qualcomm (US), Intel (US), Clarifai (US), Quadric (US), Viso.ai (Switzerland), Deci (Israel), and V7 Labs (UK). Most key players have adopted partnerships and product developments to cater to the demand for vision transformers.

Want to explore hidden markets that can drive new revenue in Vision Transformers Market?

Unlock Insights

Click on image to enlarge

Scope of the Report

Vision Transformers Market Highlights

This research report categorizes the Vision Transformers Market to forecast revenues and analyze trends in each of the following submarkets:

Recent Developments:

• In October 2023, the Amazon SageMaker Model Registry now supports registering machine learning (ML) models stored in private Docker repositories. This feature enables the convenient monitoring of all ML models from various private repositories, whether AWS or non-AWS, within a single centralized service; this streamlines the management of ML operations (MLOps) and enhances ML governance, especially when dealing with a large-scale ML environment.
• In September 2023, with the introduction of OpenVINO version 2023.1, Intel extended the capabilities of Generative AI to everyday desktops and laptops, enabling the execution of these models in local, resource-limited settings; this empowers developers to experiment with and seamlessly integrate Generative AI into their applications.
• In August 2023, in the M110 version of Vertex AI workbench’s user-managed notebooks, the following enhancements have been incorporated:
• Inclusion of support for Tensorflow 2.13 with Python 3.10 on Debian 11.
• Introduction of support for Tensorflow 2.8 with Python 3.10 on Debian 11.
• Implementation of various software updates for improved performance and functionality
• In July 2023, Edge Impulse, a platform for creating, optimizing, and deploying machine learning models and algorithms on edge devices, revealed the integration of the latest NVIDIA TAO Toolkit 5.0 into its edge AI platform.
• In July 2023, NVIDIA introduced the TAO Toolkit 5.0, which brings several groundbreaking features to enhance AI model development. Key highlights of this release include the ability to export models in an open ONNX format, enabling deployment on various platforms, advanced training for vision transformers, AI-assisted data annotation for faster labeling of segmentation masks, support for new computer vision tasks and pre-trained models, and the open-source availability of customizable solutions. These enhancements empower developers to create more accurate and robust AI models while simplifying the development and integration processes. It enables users to improve the accuracy and robustness of Vision AI Apps with vision transformers and NVIDIA TAO.
• In June 2023, Hugging Face collaborated with AMD by including AMD in their Hardware Partner Program. AMD and Hugging Face collaborate to achieve top-tier transformer model performance on AMD’s CPUs and GPUs. This partnership holds significant promise for the broader Hugging Face community, as it will soon grant access to the latest AMD platforms for training and inference purposes.
• In March 2023, OpenAI released GPT-4, the latest version of its hugely popular AI chatbot ChatGPT. The new model can respond to images, for instance, by providing recipe suggestions from photos of ingredients and writing captions and descriptions. It can also process up to 25,000 words, about eight times as many as ChatGPT. OpenAI spent six months on the safety features of GPT-4 and trained it on human feedback. GPT-4 will initially be available to ChatGPT Plus subscribers, who pay USD 20 per month for premium access to the service. It is already powering Microsoft’s Bing search engine platform.
• In March 2023, With Azure OpenAI Service, over 1,000 customers are using the most advanced AI models—including Dall-E 2, GPT-3.5, Codex, and other large language models backed by Azure’s unique supercomputing and enterprise capabilities—to innovate in new ways. Now, with ChatGPT in preview in Azure OpenAI Service, developers can integrate custom AI-powered experiences directly into their applications, including enhancing existing bots to handle unexpected questions, recapping call center conversations to enable faster customer support resolutions, creating new ad copy with personalized offers, automating claims processing, and more.

Frequently Asked Questions (FAQ):

To speak to our analyst for a discussion on the above findings, click Speak to Analyst