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Segment Anything

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2304.02643

Summary

Segmentation has had no general-purpose pretrained model like the large language models in NLP. One reason is that pixel-level masks can't be collected from the web the way text can. Meta AI's Segment Anything project addresses this with three linked parts. The first is a promptable segmentation task: given points, a box, a rough mask or text, the system must return a reasonable mask, even when the prompt is ambiguous. The second is SAM. It pairs a heavy ViT image encoder, run once per image, with a light prompt encoder and mask decoder. The decoder answers in about 50 ms and outputs several candidate masks to handle ambiguity. The third is a model-in-the-loop data engine. It moved from assisted manual labelling to semi-automatic and then fully automatic annotation, which produced the SA-1B dataset. SAM was tested zero-shot on 23 datasets and on edge detection, object proposals and instance segmentation. It often matches or beats fully supervised systems. The released model and data give vision research a reusable component for building larger systems.

Key points

  • SA-1B: 11M licensed, privacy-protecting, high-resolution images with about 1.1B masks (around 100 per image). That is roughly 400× more masks than any earlier segmentation dataset. All final masks were generated automatically by prompting SAM with a uniform grid of points.
  • Encoder scaling: ViT-B (91M), ViT-L (308M) and ViT-H (636M) were compared. ViT-H is clearly better than ViT-B but only slightly better than ViT-L. The encoder is initialised with MAE, but most of its ability comes from supervised training.
  • Data ablations: training on about 1M images (~10% of SA-1B) nearly matches using the full set. Training only on automatic masks performs about as well as combining data from all three engine stages.
  • Text prompts: SAM is trained on CLIP image embeddings and then given CLIP text embeddings at inference. The authors call this text-to-mask ability exploratory and not yet robust.
  • Release and limits: SAM is available under Apache 2.0 and SA-1B for research use, at segment-anything.com. Known weaknesses are missed fine structures, small spurious fragments, and no clear way to prompt for semantic or panoptic segmentation.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

348 papers of our library cite it (of the 140673 whose bibliographies we read); 14 of them have a page here, 334 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2304.02643 · arXiv 2304.02643

@misc{kirillov2023segment,
  title = {{Segment Anything}},
  author = {Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Dollar, Piotr and Girshick, Ross},
  year = {2023},
  doi = {10.48550/arxiv.2304.02643},
  eprint = {2304.02643},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2304.02643}
}

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