Introducing Domain-Specific Large Vision Models (LVMs)

Landing AI is thrilled to announce the launch of this groundbreaking advancement that enables businesses with vast image libraries to bring artificial intelligence to their proprietary image data-introducing domain-specific Large Vision Models (LVMs).
Landing AI’s LVMs are domain-specific foundational models that are trained using your proprietary large dataset of images from a specific business domain. These models are designed to achieve high performance in computer vision tasks, such as defect detection or object location. By adopting LVMs, businesses can ensure optimal performance and contribute to long-term success.
While generic LVMs built on internet images are one-size-fits-all, Landing AI LVMs focus on one domain at a time, helping solve proprietary problems enterprises face. Today, enterprises spend a lot of effort training individual models for each vision task, even when these tasks belong to the same business domain. With domain-specific LVMs, the goal is for companies to use a limited set of LVMs, one for each business domain, and meet their needs to solve many vision tasks in the
With LVMs, companies will more quickly pinpoint solutions for tasks such as object detection, image segmentation, visual prompting, or other AI vision-enabled applications. The LVMs will also be easier to retrain as needs change rather than dealing with many smaller models.

[@TheMrWARLORD]

Great info, thanks alot!!!

[@deveshbhatt4063]

Insightful💡

[@booooompower]

Man. I was thinking about starting modular aquaponic systems for urban households. Would like to provide fishsafety as a service. And maybe also connect it to thermalimages. Very informative hopefully they will be available in the cloud soon. X) 🐙🙂😅🆒🆗😁

[@user-pc7gm5lu1g]

very informative

[@abdulqadar9580]

😍😍😍

[@ihorrible]

Give please the examples of large vision models ?

[@Alice8000]

Love this guy Ng

[@DominikLausch]

Excellent marketing :-D What is the advantage over a very specific model that has been trained with 10-30 examples? The accuracy is much higher, the effort is much lower (about 20 min) and the model is much easier to control and change. Also, as you correctly said, industry examples are very specific and the cases are often very defined.

[@user-my2zq6td8z]

What's that semi conductor thing

[@marineboy305]

Could you share the source of the original work on the domain specific LVM illustrated at 1:31?