{
    "activation text": "",
    "description": "yolo11n-seg\u3067\u5b66\u7fd2\u3057\u305f\u3001\u30a4\u30e9\u30b9\u30c8\u3067\u306e\u30ef\u30f3\u30d4\u30fc\u30b9\u6c34\u7740(\u7af6\u6cf3\u6c34\u7740)\u3092\u691c\u51fa\u3059\u308b\u30e2\u30c7\u30eb\u3067\u3059\u3002\u6c34\u7740\u304c\u8155\u306a\u3069\u3067\u5206\u65ad\u3055\u308c\u3066\u3044\u308b\u3082\u306e\u306f\u3001\u305d\u308c\u305e\u308c\u306e\u9818\u57df\u3092\u30de\u30b9\u30af\u3057\u3066\u5b66\u7fd2\u3057\u307e\u3057\u305f\u3002\u307e\u305f\u3001\u5f8c\u308d\u59ff\u3068\u6a2a\u304b\u3089\u306e\u69cb\u56f3\u3067\u3082\u4e00\u5fdc\u691c\u51fa\u3059\u308b\u3068\u601d\u3044\u307e\u3059\u3002\u30b9\u30ea\u30f3\u30b0\u30b7\u30e7\u30c3\u30c8\u6c34\u7740\u3084\u30d3\u30ad\u30cb\u3001\u30b9\u30ab\u30fc\u30c8\u30fb\u30d5\u30ea\u30eb\u306a\u3069\u304c\u4ed8\u3044\u305f\u30c7\u30b6\u30a4\u30f3\u306f\u5b66\u7fd2\u3057\u3066\u3044\u307e\u305b\u3093\u3002yolo11n-seg\u3067\u5b66\u7fd2\u3057\u305f\u305f\u3081\u3001\u4f7f\u7528\u3057\u3066\u3044\u308b\u4eee\u60f3\u74b0\u5883\u5185\u306eultralytics\u30d0\u30fc\u30b8\u30e7\u30f3\u30928.3.0\u4ee5\u4e0a\u306b\u30a2\u30c3\u30d7\u30c7\u30fc\u30c8\u3057\u3066\u304f\u3060\u3055\u3044\u3002pip install -u ultralyticsdetection model confidence threshold = 0.6 \u4ee5\u4e0a\u3067\u306e\u4f7f\u7528\u3092\u63a8\u5968\u30e2\u30c7\u30eb\u6027\u80fd(v1.02):mAP50:0.823mAP50-98:0.672Precision:0.949Recall:0.685\u30e2\u30c7\u30eb\u6027\u80fd(v1.042):mAP50:0.835mPA50-95:0.713Precision:0.8947Recall:0.7286\u8aa4\u691c\u51fa\u3057\u3084\u3059\u3044\u7269\u4f53:\u30cb\u30fc\u30cf\u30a4(thighhighs)\u6c34\u7740\u306b\u96a3\u63a5\u3059\u308b\u30b0\u30ed\u30fc\u30d6(elbow gloves)\u691c\u51fa\u3057\u96e3\u3044\u30b1\u30fc\u30b9:\u9006\u3055\u307e\u69cb\u56f3(upside-down)\u6c34\u7740\u3068\u80cc\u666f\u8272\u304c\u8fd1\u3044\u5834\u5408\u6c34\u7740\u306b\u30cf\u30a4\u30e9\u30a4\u30c8\u306a\u3069\u304c\u3042\u308a\u3001\u4e00\u90e8\u80cc\u666f\u3068\u540c\u5316\u3057\u3066\u3044\u308b\u5834\u5408\u8155\u3092\u7d44\u3093\u3067\u3044\u308b\u5834\u5408\u306e\u3001\u4e0b\u8179\u90e8\u306e\u9818\u57dfEnglish:Claude 3.5 sonnet.A YOLO11n-seg trained model for detecting one-piece swimsuits (competitive swimwear) in illustrations.Areas where swimsuits are divided by arms or other body parts were masked separately during training. The model should also detect swimsuits from back and side view compositions.Sling shot swimsuits, bikinis, and designs with skirts/frills were not included in the training data.Please update to ultralytics version 8.3.0 or higher in the virtual environment you are using, as it was learned on yolo11n-seg.pip install -u ultralyticsRecommended to use with detection model confidence threshold = 0.6 or higherModel Performance(v1.02):mAP50:0.823mAP50-98:0.672Precision:0.949Recall:0.685Model Performance(v1.042):mAP50:0.835mPA50-95:0.713Precision:0.8947Recall:0.7286Objects prone to false detection:thighhighselbow gloves adjacent to swimsuitsDifficult detection cases:Upside-down compositionsWhen swimsuit and background colors are similarWhen highlights on the swimsuit partially blend with the backgroundLower abdominal area when arms are crossed",
    "sd version": "Other",
    "modelId": 989087,
    "sha256": "94F3C2E8DDFF2FC3E89A39DD76AFC596B393ED43BFFAE80C2E7E1C2B9A5023BB",
    "unpackList": [
        "one-pice_swimsuit_seg_v1.02.pt"
    ]
}