import { Triple } from '../types/common';
export declare const IMAGENET1K_MEAN: Triple<number>;
export declare const IMAGENET1K_STD: Triple<number>;
/**
 * COCO dataset class labels used by **RF-DETR** and **SSDLite** object detection models.
 *
 * This enum is **1-indexed** and contains **91 classes**, matching the original COCO
 * dataset category IDs. For **YOLO** models (object detection or instance segmentation),
 * use {@link CocoLabelYolo} instead — a 0-indexed, 80-class variant.
 * @see {@link CocoLabelYolo} for the YOLO-specific variant
 * @category Types
 */
export declare enum CocoLabel {
    PERSON = 1,
    BICYCLE = 2,
    CAR = 3,
    MOTORCYCLE = 4,
    AIRPLANE = 5,
    BUS = 6,
    TRAIN = 7,
    TRUCK = 8,
    BOAT = 9,
    TRAFFIC_LIGHT = 10,
    FIRE_HYDRANT = 11,
    STREET_SIGN = 12,
    STOP_SIGN = 13,
    PARKING = 14,
    BENCH = 15,
    BIRD = 16,
    CAT = 17,
    DOG = 18,
    HORSE = 19,
    SHEEP = 20,
    COW = 21,
    ELEPHANT = 22,
    BEAR = 23,
    ZEBRA = 24,
    GIRAFFE = 25,
    HAT = 26,
    BACKPACK = 27,
    UMBRELLA = 28,
    SHOE = 29,
    EYE = 30,
    HANDBAG = 31,
    TIE = 32,
    SUITCASE = 33,
    FRISBEE = 34,
    SKIS = 35,
    SNOWBOARD = 36,
    SPORTS = 37,
    KITE = 38,
    BASEBALL = 39,
    SKATEBOARD = 41,
    SURFBOARD = 42,
    TENNIS_RACKET = 43,
    BOTTLE = 44,
    PLATE = 45,
    WINE_GLASS = 46,
    CUP = 47,
    FORK = 48,
    KNIFE = 49,
    SPOON = 50,
    BOWL = 51,
    BANANA = 52,
    APPLE = 53,
    SANDWICH = 54,
    ORANGE = 55,
    BROCCOLI = 56,
    CARROT = 57,
    HOT_DOG = 58,
    PIZZA = 59,
    DONUT = 60,
    CAKE = 61,
    CHAIR = 62,
    COUCH = 63,
    POTTED_PLANT = 64,
    BED = 65,
    MIRROR = 66,
    DINING_TABLE = 67,
    WINDOW = 68,
    DESK = 69,
    TOILET = 70,
    DOOR = 71,
    TV = 72,
    LAPTOP = 73,
    MOUSE = 74,
    REMOTE = 75,
    KEYBOARD = 76,
    CELL_PHONE = 77,
    MICROWAVE = 78,
    OVEN = 79,
    TOASTER = 80,
    SINK = 81,
    REFRIGERATOR = 82,
    BLENDER = 83,
    BOOK = 84,
    CLOCK = 85,
    VASE = 86,
    SCISSORS = 87,
    TEDDY_BEAR = 88,
    HAIR_DRIER = 89,
    TOOTHBRUSH = 90,
    HAIR_BRUSH = 91
}
/**
 * COCO dataset class labels used by **YOLO** models for instance segmentation and object detection.
 *
 * This enum is **0-indexed** (values start at 0) and contains exactly **80 classes** —
 * the standard COCO detection subset without gaps. This differs from {@link CocoLabel},
 * which is 1-indexed with 91 classes and includes extra categories not present in the
 * YOLO label set.
 *
 * Use this enum when working with YOLO models (e.g. `yolo26n-seg`).
 * For RF-DETR or SSDLite models, use {@link CocoLabel}.
 * @see {@link CocoLabel} for the RF-DETR / SSDLite variant
 * @category Types
 */
export declare enum CocoLabelYolo {
    PERSON = 0,
    BICYCLE = 1,
    CAR = 2,
    MOTORCYCLE = 3,
    AIRPLANE = 4,
    BUS = 5,
    TRAIN = 6,
    TRUCK = 7,
    BOAT = 8,
    TRAFFIC_LIGHT = 9,
    FIRE_HYDRANT = 10,
    STOP_SIGN = 11,
    PARKING_METER = 12,
    BENCH = 13,
    BIRD = 14,
    CAT = 15,
    DOG = 16,
    HORSE = 17,
    SHEEP = 18,
    COW = 19,
    ELEPHANT = 20,
    BEAR = 21,
    ZEBRA = 22,
    GIRAFFE = 23,
    BACKPACK = 24,
    UMBRELLA = 25,
    HANDBAG = 26,
    TIE = 27,
    SUITCASE = 28,
    FRISBEE = 29,
    SKIS = 30,
    SNOWBOARD = 31,
    SPORTS_BALL = 32,
    KITE = 33,
    BASEBALL_BAT = 34,
    BASEBALL_GLOVE = 35,
    SKATEBOARD = 36,
    SURFBOARD = 37,
    TENNIS_RACKET = 38,
    BOTTLE = 39,
    WINE_GLASS = 40,
    CUP = 41,
    FORK = 42,
    KNIFE = 43,
    SPOON = 44,
    BOWL = 45,
    BANANA = 46,
    APPLE = 47,
    SANDWICH = 48,
    ORANGE = 49,
    BROCCOLI = 50,
    CARROT = 51,
    HOT_DOG = 52,
    PIZZA = 53,
    DONUT = 54,
    CAKE = 55,
    CHAIR = 56,
    COUCH = 57,
    POTTED_PLANT = 58,
    BED = 59,
    DINING_TABLE = 60,
    TOILET = 61,
    TV = 62,
    LAPTOP = 63,
    MOUSE = 64,
    REMOTE = 65,
    KEYBOARD = 66,
    CELL_PHONE = 67,
    MICROWAVE = 68,
    OVEN = 69,
    TOASTER = 70,
    SINK = 71,
    REFRIGERATOR = 72,
    BOOK = 73,
    CLOCK = 74,
    VASE = 75,
    SCISSORS = 76,
    TEDDY_BEAR = 77,
    HAIR_DRIER = 78,
    TOOTHBRUSH = 79
}
//# sourceMappingURL=commonVision.d.ts.map