Q Yeah. I want to give Joseph a chance to comment. Does that also mirror your, we know your story about going into, into vision, but like in the past year, since we did our podcast on Sam, um, what's been the impact that you've seen?
A Segment anything set a new standard in computer vision, you know, recapping from, from the first release to, to present. Sam introduces the ability for models to near zero shot, meaning without any training, identify kind of perfect polygons And outlines of items and objects inside images. And that capability previously required lots of manual labeling, lots of manual preparation, clicking very meticulously to create outlines of individuals and people. And there were some models that attempted to do zero shot segmentation of items inside images, though none were as High quality as Segment Anything. And with the introduction of Segment Anything, you can pass an image with SAM one, SAM two videos as well, and get perfect, pixel perfect outlines of most everything inside the images. Now, there are some edge cases across domains, and similar to the human eye, sometimes you need to say, like, which item maybe you most care about for the downstream task and problem you're working on. Though, SAM has accelerated the rate at which developers are able to use computer vision in production applications. So at RoboFlow, we were very quick to enable the community of computer vision developers and engineers to use SAM and apply it to their problems. The principle ways is using SAM. You could kind of use SAM as is to like pass an image and receive back masks. Another use case for SAM Is in pr…
AI assessment note: “SAM has accelerated the rate at which developers are able to use computer vision”