Vision Constants

Constants for Computer Vision models

ImageNet Normalization

IMAGENET1K_MEAN

Per-channel mean values for ImageNet1K normalization.

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

These values represent the mean RGB values calculated across the ImageNet1K dataset:

• R (Red): 0.485
• G (Green): 0.456
• B (Blue): 0.406

Used for normalizing input images during preprocessing for models trained on ImageNet.

IMAGENET1K_STD

Per-channel standard deviation values for ImageNet1K normalization.

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

These values represent the standard deviation RGB values calculated across the ImageNet1K dataset:

• R (Red): 0.229
• G (Green): 0.224
• B (Blue): 0.225

Used in conjunction with IMAGENET1K_MEAN for normalizing input images.

Usage Example

import { IMAGENET1K_MEAN, IMAGENET1K_STD } from 'react-native-executorch';

const preprocessorConfig = {
  normMean: IMAGENET1K_MEAN,
  normStd: IMAGENET1K_STD,
};

COCO Dataset Labels

CocoLabel

COCO dataset class labels used for object detection.

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,
}

Description

The CocoLabel enum contains all 91 object classes from the COCO (Common Objects in Context) dataset. These labels are used by object detection models trained on COCO data.

Usage Example

import { CocoLabel } from 'react-native-executorch';

const detections = await objectDetection.forward(imageUri);

detections.forEach((detection) => {
  if (detection.label === 'PERSON') {
    console.log(`Found person at confidence: ${detection.score}`);
  }
});

Notes

• Label IDs are 1-indexed (starting from 1, not 0)
• The enum values match the official COCO dataset class IDs
• Some IDs are skipped in the sequence (e.g., 40 is missing)
• Used by models like SSDLITE_320_MOBILENET_V3_LARGE and RF_DETR_NANO