With the release of the Segment Anything Model¹ (SAM) released by Meta AI Research last year, the lie of the land changed quite substantially in Computer Vision, as now images could be segmented easily, with great results even zero-shot. With the release of SAM2² earlier this year, I wanted to get hands on and experiment with these models myself.

This post walks you through how SAM2 could be used in practice, provides a mini analysis of segmentation results and will be released with code so that you can explore further if you want to. This could be expanded to interesting use cases, such as facilitating object grasping in robotic systems, branded product addition or removal in marketing images, or mapping changes in forested areas from satellite imagery over time for environmental monitoring.

Duplicate photos are annoying and unwanted. Wouldn’t you rather make those post-holiday rundowns with the family as impressive (short) as possible? The burden of boiling your photo set down to the most memorable and ones with the best angle is greatly reduced by this app. It works because you can visualize all images together in the order they were taken.

I now use my Dash app routinely after every holiday, as it makes the deduplication process much faster, yielding a cleaner set of photos without maxing out your hard drive!