AIMultiple ResearchAIMultiple Research

Large Vision Models: Examples, 7 Use Cases & Challenges in 2024

Large vision models have significantly advanced the field of computer vision. Initially, these models excelled at understanding and interpreting complex image data. However, their ability to scale effectively across various industries posed a challenge. The resolution came with the development of more specialized, domain-specific models. These advanced models are not only efficient in processing and analyzing visual data but also adaptable to the specific needs of different business domains.

In this article, we explain large vision models, their structures and potential business use cases.

What is a large vision model (LVM)?

Large vision models (LVMs) refer to advanced artificial intelligence (AI) models designed to process and interpret visual data, typically images or videos. They can be understood as the visual version of large language models (LLMs). These models are “large” in the sense that they have a significant number of parameters, often in the order of millions or even billions, allowing them to learn complex patterns in visual data.

Structure and design

Large vision models are built using advanced neural network architectures. Originally, Convolutional Neural Networks (CNNs) were predominant in processing images due to their ability to efficiently handle pixel data and detect hierarchical patterns (like edges in lower layers, and complex objects in higher layers). More recently, transformer models, which were initially designed for natural language processing, have also been adapted for many different vision tasks, offering improved performance in some scenarios.


Training large vision models involves feeding them a vast amount of visual data, such as internet images or videos, along with relevant labels or annotations in the novel sequential modeling approach. Trainers label vast image libraries to feed the models. For example, in image classification tasks, each image is labeled with the class it belongs to. The model learns by adjusting its parameters to minimize the difference between its predictions and the actual labels. This process requires significant computational power and a large, diverse dataset to ensure the model can generalize well to new, unseen data.

Large vision models training diagram

Source: OpenAI1

If you need training data for your image classification, you can check out our data vendor deep dive on image data collection services.

What are the examples of large vision models?

The three most famous examples of large vision models, widely recognized for their significant impact on the field of computer vision and AI, are:

  1. OpenAI’s CLIP (Contrastive Language–Image Pretraining)2:
    • CLIP is a neural network trained on a variety of images and text captions. It learns to understand and describe the content of images in a way that aligns with natural language descriptions. This model can perform various vision tasks, including zero-shot classification, by understanding images in the context of natural language.
    • It’s trained on 400 million (image, text) pairs, allowing it to effectively bridge the gap between computer vision tasks and natural language processing. This enables it to perform tasks like caption prediction or image summary without being explicitly trained for these specific tasks.
  2. Landing AI’s LandingLens3:
    • LandingLens is a platform designed to simplify the development and deployment of computer vision models. It allows users to create and test AI projects for visual data, catering to a range of industries without requiring deep expertise in AI or complex programming.
    • The platform standardizes deep learning solutions, reducing development time and easily scaling projects globally. Users can build their own deep learning models and optimize inspection accuracy without impacting production speed. Landing AI LVMs focus on significantly reducing development time from months to weeks, simplifying labeling, training, and deploying models.
    • It offers a step-by-step user interface that simplifies the development process, enabling teams to create domain specific LVMs without requiring deep technical knowledge.
  3. Google’s Vision Transformer (ViT)4:
    • Vision Transformer is a model that applies the transformer architecture, originally used in natural language processing, to image recognition tasks. It processes images in a manner similar to how transformers process sequences of words, showing effectiveness in learning relevant features from image data for classification and analysis tasks.
    • In Vision Transformer, images are treated as a sequence of patches. Each patch is flattened into a single vector, similar to how word embeddings are used in transformers for text. This approach allows ViT to independently learn the structure of images and predict class labels.

What are the use cases of large vision models?

1- Healthcare and medical imaging

  • Disease diagnosis: Detecting diseases from medical imagery such as X-rays, MRIs, or CT scans. For example, identifying tumors, fractures, or abnormalities.
  • Pathology: Analyzing tissue samples in pathology for signs of diseases like cancer.
  • Ophthalmology: Assisting in diagnosing diseases from retinal images.

2- Autonomous vehicles and robotics

  • Navigation and obstacle detection: Helping autonomous vehicles and drones to navigate and avoid obstacles by interpreting real-time visual data.
  • Robotics in manufacturing: AI vision enabled applications can help robots in tasks like sorting, assembling, and quality inspection.

3- Security and surveillance

  • Facial recognition: Used in security systems for identity verification and tracking.
  • Activity Monitoring: Analyzing video feeds to detect unusual or suspicious behavior.

4- Retail and commerce

  • Visual search: Enabling customers to search for products using images instead of text.
  • Inventory management: Automating the process of monitoring and managing inventory through visual recognition.

5- Agriculture

  • Crop monitoring and analysis: Monitoring crop health and growth using drone or satellite imagery.
  • Pest detection: Identifying pests and diseases affecting crops.

6- Environmental monitoring

  • Wildlife tracking: Identifying and tracking wildlife for conservation efforts.
  • Land use and land cover analysis: Monitoring changes in land use and vegetation cover over time.

7- Content creation and entertainment

  • Film and video editing: Automating aspects of video editing and post-production.
  • Game development: Enhancing the creation of realistic environments and characters.
  • Photo and video enhancement: Improving the quality of images and videos.
  • Content moderation: Automatically detecting and flagging inappropriate or harmful visual content.

What are the challenges of large vision models?

  1. Computational resources: Training and deploying these models require significant computational power and memory, making them resource-intensive.
  2. Data requirements: They need vast and diverse datasets for training. Collecting, labeling, and processing such large datasets can be challenging and expensive. However, crowdsource companies can help handle this.
  3. Bias and Fairness: Models can inherit biases present in their training data, leading to unfair or unethical outcomes, particularly in sensitive applications like facial recognition.
  4. Interpretability and explainability: Understanding how these models make decisions can be difficult, which is a concern for applications where transparency is critical.
  5. Generalization: While they perform well on data similar to their training set, they may struggle with completely new or different types of data.
  6. Privacy concerns: The use of large visual models, especially in surveillance and facial recognition, raises significant privacy concerns.
  7. Regulatory and ethical challenges: Ensuring that the use of these models complies with legal and ethical standards is increasingly important, particularly as they become more integrated into society.

If you have questions or need help in finding vendors, reach out:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read


Your email address will not be published. All fields are required.