{
  "id": "yolov8-segment",
  "identity": {
    "family": "yolov8",
    "display_name": "Ultralytics YOLOv8 segment",
    "hub_id": "Ultralytics/YOLOv8 (yolov8n-seg.pt)",
    "api_name": null,
    "revision": null,
    "variants": [
      "yolov8s-seg.pt",
      "yolov8m-seg.pt",
      "yolov8l-seg.pt",
      "yolov8x-seg.pt"
    ],
    "license": "AGPL-3.0",
    "commercial_use": false,
    "commercial_use_note": "AGPL-3.0: offering it over a network obliges publishing the whole application's source under AGPL; closed commercial use needs an Ultralytics Enterprise license.",
    "release_date": "2023-01-10",
    "paper_url": null,
    "repo_url": "https://github.com/ultralytics/ultralytics"
  },
  "task": {
    "category": "segmentation",
    "modalities_in": [
      "image"
    ],
    "modalities_out": [
      "boxes",
      "masks",
      "labels",
      "scores"
    ],
    "best_at": "Fast segment for the fixed COCO label set; n size runs in real time on CPU.",
    "worst_at": "Closed label set (needs fine-tuning for new classes); license blocks closed commercial use; small or crowded objects at 640 input."
  },
  "io": {
    "input": {
      "image": {
        "resolution": "640x640 letterboxed",
        "color": "RGB"
      },
      "prompts": "none (fixed classes)"
    },
    "output": {
      "boxes": "xyxy pixels",
      "masks": "per-instance bitmaps (prototype masks x coefficients) or polygons",
      "labels": "COCO 80 classes"
    }
  },
  "mixpeek_wiring": {
    "extractors": [],
    "feature_uris": [],
    "vector_indexes": [],
    "retriever_stages": [],
    "composes_with": [
      "detector boxes -> crops -> SigLIP vectors per object (one document per object, or a multi-vector per frame)",
      "labels -> payload field -> attribute filter in a retriever",
      "scene splits -> keyframes -> detector -> object documents with frame timestamps"
    ]
  },
  "run": {
    "status": "not_runnable",
    "status_reason": "Blocked by AGPL-3.0 and pickle .pt weights. Would run as a custom extractor plugin that exports the checkpoint to ONNX once (ultralytics export format=onnx) and serves it with onnxruntime on CPU, emitting boxes, labels and crops.",
    "serve_apps": [],
    "runtime": "ultralytics YOLO (or onnxruntime after export)",
    "weights_format": "pickle",
    "trust_remote_code": false,
    "size_on_disk_mb": null,
    "params_millions": 3.4,
    "preprocessing": "Letterbox to 640x640 (stride-aligned padding), RGB, scale to 0..1.",
    "postprocessing": "Confidence threshold then NMS, mask decode from prototypes",
    "serving_shape": "both",
    "batch_size": null,
    "serve_batch": null,
    "scale_to_zero": {
      "min_replicas": 0,
      "max_replicas": 1,
      "idle_timeout_s": 600,
      "memory_gb": 1,
      "num_cpus": 1,
      "num_gpus": 0,
      "cold_start_s": {
        "warm_node_s": null,
        "new_node_s": null,
        "source": null
      },
      "cpu_fallback": true
    }
  },
  "cost": {
    "unmeasured": true,
    "measured": null,
    "note": "No committed measurement; plan row 3 measures it in the standalone image."
  },
  "evidence": {
    "unmeasured": true,
    "items": []
  },
  "refs": [
    "hf://Ultralytics/YOLOv8@27f858f2dc238503a66ce10248cf7ec71a3ded5d"
  ],
  "formats": [
    {
      "ref": "hf://Ultralytics/YOLOv8@27f858f2dc238503a66ce10248cf7ec71a3ded5d",
      "format": "pytorch_pt",
      "file": "5 files, e.g. yolov8l.pt",
      "loader": "torch.load (pickle); Mixpeek refuses pickle weights (shared/security/torch_pickle.py)",
      "supported_today": false
    }
  ],
  "use_cases": [
    "segmentation",
    "object-detection"
  ],
  "recommended": {
    "value": false,
    "for": [],
    "reason": null
  },
  "demos": [],
  "seo": {
    "title": "Ultralytics YOLOv8 segment: segmentation model",
    "description": "Ultralytics YOLOv8 segment is a image segmentation model (AGPL-3.0). Not runnable on Mixpeek today.",
    "keywords": [
      "Ultralytics YOLOv8 segment",
      "yolov8",
      "segmentation",
      "Ultralytics/YOLOv8 (yolov8n-seg.pt)",
      "object-detection"
    ],
    "faqs": [
      {
        "q": "What is Ultralytics YOLOv8 segment best at?",
        "a": "Fast segment for the fixed COCO label set; n size runs in real time on CPU."
      },
      {
        "q": "What are the limits of Ultralytics YOLOv8 segment?",
        "a": "Closed label set (needs fine-tuning for new classes); license blocks closed commercial use; small or crowded objects at 640 input."
      },
      {
        "q": "Can Ultralytics YOLOv8 segment be used commercially?",
        "a": "No. AGPL-3.0: offering it over a network obliges publishing the whole application's source under AGPL; closed commercial use needs an Ultralytics Enterprise license."
      },
      {
        "q": "How does Ultralytics YOLOv8 segment run on Mixpeek?",
        "a": "Blocked by AGPL-3.0 and pickle .pt weights. Would run as a custom extractor plugin that exports the checkpoint to ONNX once (ultralytics export format=onnx) and serves it with onnxruntime on CPU, emitting boxes, labels and crops."
      }
    ]
  },
  "last_verified": "2026-11-05",
  "sources": [
    "https://huggingface.co/Ultralytics/YOLOv8",
    "https://docs.ultralytics.com/models/yolov8/",
    "https://www.ultralytics.com/license",
    "https://roboflow.com/model-licenses/yoloe"
  ],
  "registry": null,
  "templates": [],
  "usage": {
    "namespaces": null,
    "collections": null,
    "uncounted": true,
    "templates": 0,
    "counted_at": null,
    "source": null
  },
  "facets": {
    "status": "not_runnable",
    "category": "segmentation",
    "family": "yolov8",
    "license": "AGPL-3.0",
    "commercial_use": false,
    "modalities_in": [
      "image"
    ],
    "modalities_out": [
      "boxes",
      "masks",
      "labels",
      "scores"
    ],
    "use_cases": [
      "segmentation",
      "object-detection"
    ],
    "recommended": false,
    "release_date": "2023-01-10",
    "params_millions": 3.4,
    "size_on_disk_mb": null,
    "vector_dims": null,
    "vector_distance": null,
    "weights_format": "pickle",
    "formats": [
      "pytorch_pt"
    ],
    "loadable_today": false,
    "trust_remote_code": false,
    "cpu_fallback": true,
    "num_gpus": 0,
    "memory_gb": 1,
    "min_replicas": 0,
    "serving_shape": "both",
    "served": false,
    "ref_schemes": [
      "hf"
    ],
    "has_evidence": false,
    "cost_measured": false,
    "template_count": 0,
    "usage_namespaces": null,
    "usage_collections": null,
    "usage_uncounted": true,
    "has_demo": false,
    "rank_score": 0
  },
  "rank_score": 0
}