react-native-executorch
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An easy way to run AI models in React Native with ExecuTorch
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TypeScript
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