Segment Anything Model (SAM)

On adoption, SAM is already a high-recognition foundation model in image segmentation and is worth serious attention first; however, the current evidence proves attention, discussion volume, and continued iteration more strongly than universal superiority in every domain. Meta’s official X post for SAM 2 had very strong reach, which is good proof of heat and ecosystem momentum. But reposts and hype are not usability proof. The better evidence for usefulness here comes from long-form explainers, comparisons, and fine-tuning tutorials on Zhihu, which support that SAM is genuinely useful for interactive segmentation, research validation, and prototype building.\n\nIn practice, SAM is not a text-to-image model, not a classic object detector, and not a turnkey system that fully understands complex business semantics. A better analogy is a promptable general segmentation backbone or an interactive mask-generation engine. Users provide points or boxes, and the model returns masks. The evidence shows usage for image segmentation and later video segmentation, while community writeups also frame it as a strong starting point for downstream fine-tuning.