Segment Anything, recently released by Facebook Research, does something that most people who have dabbled in computer vision have found daunting: reliably figure out which pixels in an image belong to an object. Making that easier is the goal of the Segment Anything Model (SAM), just released under the Apache 2.0 license.
The online demo has a bank of examples, but also works with uploaded images.
The results look fantastic, and there’s an interactive demo available where you can play with the different ways SAM works. One can pick out objects by pointing and clicking on an image, or images can be automatically segmented. It’s frankly very impressive to see SAM make masking out the different objects in an image look so effortless. What makes this possible is machine learning, and part of that is the fact that the model behind the system has been trained on a huge dataset of high-quality images and masks, making it very effective at what it does.
Once an image is segmented, those masks can be used to interface with other systems like object detection (which identifies and labels what an object is) and other computer vision applications. Such system work more robustly if they already know where to look, after all. This blog post from Meta AI goes into some additional detail about what’s possible with SAM, and fuller details are in the research paper.
Systems like this rely on quality datasets. Of course, nothing beats a great collection of real-world data but we’ve also seen that it’s possible to machine-generate data that never actually existed, and get useful results.
