{
  "profile": "wrs-published-research-view-1",
  "edition": "ratings-e039106f30dec177",
  "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS",
  "entry": {
    "id": "kirillov-et-al-segment-anything-2023",
    "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset",
    "summary": "Introduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations.",
    "boundary": "Meta's exact 2023 Segment Anything paper is distinct from later SAM 2/video systems. Zero-shot comparisons and dataset properties are those reported in the paper.",
    "area": "Promptable image segmentation",
    "topic": "Artificial intelligence",
    "year": 2023,
    "lane": "HUMAN",
    "result": "ALGORITHM",
    "package_id": "kirillov-et-al-segment-anything-2023",
    "target": {
      "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
      "version": 1,
      "content_hash": "sha256:3ca51de9766586333243d010865914e92bd4047d1203d64eaae973ff549fd14b"
    },
    "humans": [
      "Alexander Kirillov",
      "Eric Mintun",
      "Nikhila Ravi",
      "Hanzi Mao",
      "Chloe Rolland",
      "Laura Gustafson",
      "Tete Xiao",
      "Spencer Whitehead",
      "Alexander C. Berg",
      "Wan-Yen Lo",
      "Piotr Dollár",
      "Ross Girshick"
    ],
    "authors_complete": true,
    "human_role_note": "Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.",
    "agents": [],
    "related": [],
    "subresults": [],
    "scientific_assessment": {
      "source_review": "SOURCE_CURATED",
      "scientific_review": "NOT_REVIEWED_BY_WRS",
      "formal_replay": "NOT_RUN",
      "provider_attestation": "NONE"
    },
    "sources": [
      {
        "id": "arxiv-segment-anything-2304-02643",
        "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
        "url": "https://arxiv.org/abs/2304.02643v1"
      },
      {
        "id": "meta-segment-anything-2023",
        "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — company research page",
        "url": "https://ai.meta.com/research/publications/segment-anything/"
      }
    ],
    "points": "60000",
    "published_grade": {
      "id": "kirillov-et-al-segment-anything-2023",
      "entry_id": "kirillov-et-al-segment-anything-2023",
      "band": "A",
      "modifier": null,
      "target": {
        "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
        "version": 1,
        "content_hash": "sha256:3ca51de9766586333243d010865914e92bd4047d1203d64eaae973ff549fd14b"
      },
      "what_changed": "Introduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations.",
      "scope": "Introduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations. Scope limitation: Meta's exact 2023 Segment Anything paper is distinct from later SAM 2/video systems. Zero-shot comparisons and dataset properties are those reported in the paper.",
      "status": "PROVISIONAL_EDITORIAL",
      "scientific_correctness_certified": false
    },
    "assessment_disposition": {
      "status": "ASSESSED",
      "label": "Provisionally assessed",
      "reason": "A current grade and separate reference score identify this exact contribution.",
      "target": {
        "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
        "version": 1,
        "content_hash": "sha256:3ca51de9766586333243d010865914e92bd4047d1203d64eaae973ff549fd14b"
      },
      "edition": "ratings-e039106f30dec177"
    }
  },
  "version": {
    "kind": "PUBLIC_RECORD_VIEW",
    "previous": null,
    "published_at": "2026-09-27T00:00:00Z",
    "publisher": {
      "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33",
      "role": "WRS_EDITORIAL_PUBLICATION"
    },
    "record": {
      "contributors": [
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Alexander Kirillov",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Eric Mintun",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Nikhila Ravi",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Hanzi Mao",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Chloe Rolland",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Laura Gustafson",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Tete Xiao",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Spencer Whitehead",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Alexander C. Berg",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Wan-Yen Lo",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Piotr Dollár",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Ross Girshick",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        }
      ],
      "evidence": [
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-0",
          "kind": "source_reference",
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
            "url": "https://arxiv.org/abs/2304.02643v1"
          }
        },
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-1",
          "kind": "source_reference",
          "source": {
            "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — company research page",
            "url": "https://ai.meta.com/research/publications/segment-anything/"
          }
        }
      ],
      "origin": "HUMAN_LEGACY",
      "record_profile": "wrs-local-research-notes-0.1",
      "record_type": "research_note",
      "rrp_version": "0.1",
      "scope": {
        "description": "Meta's exact 2023 Segment Anything paper is distinct from later SAM 2/video systems. Zero-shot comparisons and dataset properties are those reported in the paper.",
        "domain": "Promptable image segmentation"
      },
      "sources": [
        {
          "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
          "url": "https://arxiv.org/abs/2304.02643v1"
        },
        {
          "citation": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — company research page",
          "url": "https://ai.meta.com/research/publications/segment-anything/"
        }
      ],
      "statement": "# Kirillov and colleagues: Segment Anything model and SA-1B dataset\n\nIntroduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations.\n\n## Scientific scope\n\nMeta's exact 2023 Segment Anything paper is distinct from later SAM 2/video systems. Zero-shot comparisons and dataset properties are those reported in the paper.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nAlexander Kirillov; Eric Mintun; Nikhila Ravi; Hanzi Mao; Chloe Rolland; Laura Gustafson; Tete Xiao; Spencer Whitehead; Alexander C. Berg; Wan-Yen Lo; Piotr Dollár; Ross Girshick\n\nComplete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.\n\n\n\n## Original sources\n\n- [Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper](https://arxiv.org/abs/2304.02643v1)\n- [Kirillov and colleagues: Segment Anything model and SA-1B dataset — company research page](https://ai.meta.com/research/publications/segment-anything/)\n",
      "summary": "Introduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations.",
      "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset"
    },
    "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
    "version": 1,
    "source_ref": {
      "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
      "version": 1,
      "content_hash": "sha256:3ca51de9766586333243d010865914e92bd4047d1203d64eaae973ff549fd14b"
    },
    "export_view": "wrs-public-editorial-view-1",
    "public_view_hash": "sha256:e30a73a91e46737aa21c021733c653f97d8786cc110db6990e6032fe5775d8b3"
  },
  "target": {
    "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
    "version": 1,
    "content_hash": "sha256:3ca51de9766586333243d010865914e92bd4047d1203d64eaae973ff549fd14b"
  },
  "published_grade": {
    "profile": "wrs-published-grade-1",
    "edition": "2026-10-28.1",
    "id": "kirillov-et-al-segment-anything-2023",
    "entry_id": "kirillov-et-al-segment-anything-2023",
    "revision": 1,
    "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset",
    "context": "Promptable image segmentation",
    "track": "Human-designed AI research method or resource",
    "band": "A",
    "modifier": null,
    "status": "PROVISIONAL_EDITORIAL",
    "assessed_on": "2026-09-27",
    "evidence_cutoff": "2026-09-27",
    "achievement_state": "SUPPORTED_SCOPED_RESULT",
    "what_changed": "Introduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations.",
    "scope": "Introduced a promptable image-segmentation task and model plus the SA-1B dataset of over one billion masks on 11 million images, with reported zero-shot transfer evaluations. Scope limitation: Meta's exact 2023 Segment Anything paper is distinct from later SAM 2/video systems. Zero-shot comparisons and dataset properties are those reported in the paper.",
    "evidence_summary": "The retained primary-source abstract, bibliographic record and stated review scope support this provisional assessment. No independent experimental replication by WRS is claimed. Meta's exact 2023 Segment Anything paper is distinct from later SAM 2/video systems. Zero-shot comparisons and dataset properties are those reported in the paper.",
    "references": [
      {
        "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — original paper",
        "url": "https://arxiv.org/abs/2304.02643v1",
        "locator": "Abstract and complete bibliographic byline for 2304.02643v1; not an independent proof or experimental replication."
      },
      {
        "title": "Kirillov and colleagues: Segment Anything model and SA-1B dataset — company research page",
        "url": "https://ai.meta.com/research/publications/segment-anything/",
        "locator": "Source-linked contribution and stated scientific limits; not an independent replication."
      }
    ],
    "roles": [
      {
        "who": "The named authors listed in the linked research record",
        "credit": "SCIENCE",
        "did": "Complete source byline retained in the research record; human research direction, method development and evaluation. Individual labor shares are not measured."
      }
    ],
    "target": {
      "record_id": "urn:uuid:b68ce083-c348-4299-af78-6fac3a2a8196",
      "version": 1,
      "content_hash": "sha256:3ca51de9766586333243d010865914e92bd4047d1203d64eaae973ff549fd14b"
    },
    "scientific_correctness_certified": false,
    "independent_expert_calibration": false,
    "method": "PROPRIETARY",
    "points": "60000"
  },
  "canonical_agent_submission": false,
  "scientific_verification_awarded": false,
  "reader_guide": null,
  "reading_status": "SOURCE_LINKED_NOTE"
}
