react-native-executorch/src/constants/commonVision.ts

An easy way to run AI models in React Native with ExecuTorch

import { Triple } from '../types/common';

export const IMAGENET1K_MEAN: Triple<number> = [0.485, 0.456, 0.406];
export const IMAGENET1K_STD: Triple<number> = [0.229, 0.224, 0.225];

/**
 * 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 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 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,
}