Version: 0.10.0

Variable: models

const models: object

Registry of pre-configured ExecuTorch models.

This provides Hugging Face repository URLs and baseline configurations for tasks, allowing quick model loading and execution without manual option setup.

Models published for more than one backend expose their exports as named variants (XNNPACK_INT8, COREML_FP16, ...) plus a DEFAULT alias. The alias is chosen for the device the app runs on: Core ML then MLX on iOS hardware, Vulkan on Android, XNNPACK as the fallback everywhere and the only option on the iOS simulator — always narrowed to the backends the app actually linked in. Reach for a named variant to override that.

Type Declaration

classification

classification: object

Image classification models that categorize input images into pre-defined classes.

classification.EFFICIENTNET_V2_S

EFFICIENTNET_V2_S: object & object

EfficientNetV2-S image classification model pre-trained on ImageNet-1k (1000 categories, see IMAGENET1K_LABELS). Compact and efficient architecture providing high accuracy for general-purpose image classification.

Type Declaration

COREML_FP16

COREML_FP16: ClassifierModel<"tench, Tinca tinca" | "goldfish, Carassius auratus" | "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias" | "tiger shark, Galeocerdo cuvieri" | "hammerhead, hammerhead shark" | "electric ray, crampfish, numbfish, torpedo" | "stingray" | "cock" | "hen" | "ostrich, Struthio camelus" | "brambling, Fringilla montifringilla" | "goldfinch, Carduelis carduelis" | "house finch, linnet, Carpodacus mexicanus" | "junco, snowbird" | "indigo bunting, indigo finch, indigo bird, Passerina cyanea" | "robin, American robin, Turdus migratorius" | "bulbul" | "jay" | "magpie" | "chickadee" | "water ouzel, dipper" | "kite" | "bald eagle, American eagle, Haliaeetus leucocephalus" | "vulture" | "great grey owl, great gray owl, Strix nebulosa" | "European fire salamander, Salamandra salamandra" | "common newt, Triturus vulgaris" | "eft" | "spotted salamander, Ambystoma maculatum" | "axolotl, mud puppy, Ambystoma mexicanum" | "bullfrog, Rana catesbeiana" | "tree frog, tree-frog" | "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui" | "loggerhead, loggerhead turtle, Caretta caretta" | "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea" | "mud turtle" | "terrapin" | "box turtle, box tortoise" | "banded gecko" | "common iguana, iguana, Iguana iguana" | "American chameleon, anole, Anolis carolenensis" | "whiptail, whiptail lizard" | "agama" | "frilled lizard, Chlamydosaurus kingi" | "alligator lizard" | "Gila monster, Heloderma suspectum" | "green lizard, Lacerta viridis" | "African chameleon, Chamaeleo chamaeleon" | "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis" | "African crocodile, Nile crocodile, Crocodylus niloticus" | "American alligator, Alligator mississipiensis" | "triceratops" | "thunder snake, worm snake, Carphophis amoenus" | "ringneck snake, ring-necked snake, ring snake" | "hognose snake, puff adder, sand viper" | "green snake, grass snake" | "king snake, kingsnake" | "garter snake, grass snake" | "water snake" | "vine snake" | "night snake, Hypsiglena torquata" | "boa constrictor, Constrictor constrictor" | "rock python, rock snake, Python sebae" | "Indian cobra, Naja naja" | "green mamba" | "sea snake" | "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus" | "diamondback, diamondback rattlesnake, Crotalus adamanteus" | "sidewinder, horned rattlesnake, Crotalus cerastes" | "trilobite" | "harvestman, daddy longlegs, Phalangium opilio" | "scorpion" | "black and gold garden spider, Argiope aurantia" | "barn spider, Araneus cavaticus" | "garden spider, Aranea diademata" | "black widow, Latrodectus mactans" | "tarantula" | "wolf spider, hunting spider" | "tick" | "centipede" | "black grouse" | "ptarmigan" | "ruffed grouse, partridge, Bonasa umbellus" | "prairie chicken, prairie grouse, prairie fowl" | "peacock" | "quail" | "partridge" | "African grey, African gray, Psittacus erithacus" | "macaw" | "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita" | "lorikeet" | "coucal" | "bee eater" | "hornbill" | "hummingbird" | "jacamar" | "toucan" | "drake" | "red-breasted merganser, Mergus serrator" | "goose" | "black swan, Cygnus atratus" | "tusker" | "echidna, spiny anteater, anteater" | "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus" | "wallaby, brush kangaroo" | "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus" | "wombat" | "jellyfish" | "sea anemone, anemone" | "brain coral" | "flatworm, platyhelminth" | "nematode, nematode worm, roundworm" | "conch" | "snail" | "slug" | "sea slug, nudibranch" | "chiton, coat-of-mail shell, sea cradle, polyplacophore" | "chambered nautilus, pearly nautilus, nautilus" | "Dungeness crab, Cancer magister" | "rock crab, Cancer irroratus" | "fiddler crab" | "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica" | "American lobster, Northern lobster, Maine lobster, Homarus americanus" | "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish" | "crayfish, crawfish, crawdad, crawdaddy" | "hermit crab" | "isopod" | "white stork, Ciconia ciconia" | "black stork, Ciconia nigra" | "spoonbill" | "flamingo" | "little blue heron, Egretta caerulea" | "American egret, great white heron, Egretta albus" | "bittern" | "crane" | "limpkin, Aramus pictus" | "European gallinule, Porphyrio porphyrio" | "American coot, marsh hen, mud hen, water hen, Fulica americana" | "bustard" | "ruddy turnstone, Arenaria interpres" | "red-backed sandpiper, dunlin, Erolia alpina" | "redshank, Tringa totanus" | "dowitcher" | "oystercatcher, oyster catcher" | "pelican" | "king penguin, Aptenodytes patagonica" | "albatross, mollymawk" | "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus" | "killer whale, killer, orca, grampus, sea wolf, Orcinus orca" | "dugong, Dugong dugon" | "sea lion" | "Chihuahua" | "Japanese spaniel" | "Maltese dog, Maltese terrier, Maltese" | "Pekinese, Pekingese, Peke" | "Shih-Tzu" | "Blenheim spaniel" | "papillon" | "toy terrier" | "Rhodesian ridgeback" | "Afghan hound, Afghan" | "basset, basset hound" | "beagle" | "bloodhound, sleuthhound" | "bluetick" | "black-and-tan coonhound" | "Walker hound, Walker foxhound" | "English foxhound" | "redbone" | "borzoi, Russian wolfhound" | "Irish wolfhound" | "Italian greyhound" | "whippet" | "Ibizan hound, Ibizan Podenco" | "Norwegian elkhound, elkhound" | "otterhound, otter hound" | "Saluki, gazelle hound" | "Scottish deerhound, deerhound" | "Weimaraner" | "Staffordshire bullterrier, Staffordshire bull terrier" | "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier" | "Bedlington terrier" | "Border terrier" | "Kerry blue terrier" | "Irish terrier" | "Norfolk terrier" | "Norwich terrier" | "Yorkshire terrier" | "wire-haired fox terrier" | "Lakeland terrier" | "Sealyham terrier, Sealyham" | "Airedale, Airedale terrier" | "cairn, cairn terrier" | "Australian terrier" | "Dandie Dinmont, Dandie Dinmont terrier" | "Boston bull, Boston terrier" | "miniature schnauzer" | "giant schnauzer" | "standard schnauzer" | "Scotch terrier, Scottish terrier, Scottie" | "Tibetan terrier, chrysanthemum dog" | "silky terrier, Sydney silky" | "soft-coated wheaten terrier" | "West Highland white terrier" | "Lhasa, Lhasa apso" | "flat-coated retriever" | "curly-coated retriever" | "golden retriever" | "Labrador retriever" | "Chesapeake Bay retriever" | "German short-haired pointer" | "vizsla, Hungarian pointer" | "English setter" | "Irish setter, red setter" | "Gordon setter" | "Brittany spaniel" | "clumber, clumber spaniel" | "English springer, English springer spaniel" | "Welsh springer spaniel" | "cocker spaniel, English cocker spaniel, cocker" | "Sussex spaniel" | "Irish water spaniel" | "kuvasz" | "schipperke" | "groenendael" | "malinois" | "briard" | "kelpie" | "komondor" | "Old English sheepdog, bobtail" | "Shetland sheepdog, Shetland sheep dog, Shetland" | "collie" | "Border collie" | "Bouvier des Flandres, Bouviers des Flandres" | "Rottweiler" | "German shepherd, German shepherd dog, German police dog, alsatian" | "Doberman, Doberman pinscher" | "miniature pinscher" | "Greater Swiss Mountain dog" | "Bernese mountain dog" | "Appenzeller" | "EntleBucher" | "boxer" | "bull mastiff" | "Tibetan mastiff" | "French bulldog" | "Great Dane" | "Saint Bernard, St Bernard" | "Eskimo dog, husky" | "malamute, malemute, Alaskan malamute" | "Siberian husky" | "dalmatian, coach dog, carriage dog" | "affenpinscher, monkey pinscher, monkey dog" | "basenji" | "pug, pug-dog" | "Leonberg" | "Newfoundland, Newfoundland dog" | "Great Pyrenees" | "Samoyed, Samoyede" | "Pomeranian" | "chow, chow chow" | "keeshond" | "Brabancon griffon" | "Pembroke, Pembroke Welsh corgi" | "Cardigan, Cardigan Welsh corgi" | "toy poodle" | "miniature poodle" | "standard poodle" | "Mexican hairless" | "timber wolf, grey wolf, gray wolf, Canis lupus" | "white wolf, Arctic wolf, Canis lupus tundrarum" | "red wolf, maned wolf, Canis rufus, Canis niger" | "coyote, prairie wolf, brush wolf, Canis latrans" | "dingo, warrigal, warragal, Canis dingo" | "dhole, Cuon alpinus" | "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus" | "hyena, hyaena" | "red fox, Vulpes vulpes" | "kit fox, Vulpes macrotis" | "Arctic fox, white fox, Alopex lagopus" | "grey fox, gray fox, Urocyon cinereoargenteus" | "tabby, tabby cat" | "tiger cat" | "Persian cat" | "Siamese cat, Siamese" | "Egyptian cat" | "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor" | "lynx, catamount" | "leopard, Panthera pardus" | "snow leopard, ounce, Panthera uncia" | "jaguar, panther, Panthera onca, Felis onca" | "lion, king of beasts, Panthera leo" | "tiger, Panthera tigris" | "cheetah, chetah, Acinonyx jubatus" | "brown bear, bruin, Ursus arctos" | "American black bear, black bear, Ursus americanus, Euarctos americanus" | "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus" | "sloth bear, Melursus ursinus, Ursus ursinus" | "mongoose" | "meerkat, mierkat" | "tiger beetle" | "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle" | "ground beetle, carabid beetle" | "long-horned beetle, longicorn, longicorn beetle" | "leaf beetle, chrysomelid" | "dung beetle" | "rhinoceros beetle" | "weevil" | "fly" | "bee" | "ant, emmet, pismire" | "grasshopper, hopper" | "cricket" | "walking stick, walkingstick, stick insect" | "cockroach, roach" | "focused mantis, mantid" | "cicada, cicala" | "leafhopper" | "lacewing, lacewing fly" | "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk" | "damselfly" | "admiral" | "ringlet, ringlet butterfly" | "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus" | "cabbage butterfly" | "sulphur butterfly, sulfur butterfly" | "lycaenid, lycaenid butterfly" | "starfish, sea star" | "sea urchin" | "sea cucumber, holothurian" | "wood rabbit, cottontail, cottontail rabbit" | "hare" | "Angora, Angora rabbit" | "hamster" | "porcupine, hedgehog" | "fox squirrel, eastern fox squirrel, Sciurus niger" | "marmot" | "beaver" | "guinea pig, Cavia cobaya" | "sorrel" | "zebra" | "hog, pig, grunter, squealer, Sus scrofa" | "wild boar, boar, Sus scrofa" | "warthog" | "hippopotamus, hippo, river horse, Hippopotamus amphibius" | "ox" | "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis" | "bison" | "ram, tup" | "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis" | "ibex, Capra ibex" | "hartebeest" | "impala, Aepyceros melampus" | "gazelle" | "Arabian camel, dromedary, Camelus dromedarius" | "llama" | "weasel" | "mink" | "polecat, fitch, foulmart, foumart, Mustela putorius" | "black-footed ferret, ferret, Mustela nigripes" | "otter" | "skunk, polecat, wood pussy" | "badger" | "armadillo" | "three-toed sloth, ai, Bradypus tridactylus" | "orangutan, orang, orangutang, Pongo pygmaeus" | "gorilla, Gorilla gorilla" | "chimpanzee, chimp, Pan troglodytes" | "gibbon, Hylobates lar" | "siamang, Hylobates syndactylus, Symphalangus syndactylus" | "guenon, guenon monkey" | "patas, hussar monkey, Erythrocebus patas" | "baboon" | "macaque" | "langur" | "colobus, colobus monkey" | "proboscis monkey, Nasalis larvatus" | "marmoset" | "capuchin, ringtail, Cebus capucinus" | "howler monkey, howler" | "titi, titi monkey" | "spider monkey, Ateles geoffroyi" | "squirrel monkey, Saimiri sciureus" | "Madagascar cat, ring-tailed lemur, Lemur catta" | "indri, indris, Indri indri, Indri brevicaudatus" | "Indian elephant, Elephas maximus" | "African elephant, Loxodonta africana" | "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens" | "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca" | "barracouta, snoek" | "raw eel, eel" | "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch" | "rock beauty, Holocanthus tricolor" | "anemone fish" | "sturgeon" | "gar, garfish, garpike, billfish, Lepisosteus osseus" | "lionfish" | "puffer, pufferfish, blowfish, globefish" | "abacus" | "abaya" | "academic gown, academic robe, judge's robe" | "accordion, piano accordion, squeeze box" | "acoustic guitar" | "aircraft carrier, carrier, flattop, attack aircraft carrier" | "airliner" | "airship, dirigible" | "altar" | "ambulance" | "amphibian, amphibious vehicle" | "analog clock" | "apiary, bee house" | "apron" | "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin" | "assault rifle, assault gun" | "backpack, back pack, knapsack, packsack, rucksack, haversack" | "bakery, bakeshop, bakehouse" | "balance beam, beam" | "balloon" | "ballpoint, ballpoint pen, ballpen, Biro" | "Band Aid" | "banjo" | "bannister, banister, balustrade, balusters, handrail" | "barbell" | "barber chair" | "barbershop" | "barn" | "barometer" | "barrel, cask" | "barrow, garden cart, lawn cart, wheelbarrow" | "baseball" | "basketball" | "bassinet" | "bassoon" | "bathing cap, swimming cap" | "bath towel" | "bathtub, bathing tub, bath, tub" | "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon" | "beacon, lighthouse, beacon light, pharos" | "beaker" | "bearskin, busby, shako" | "beer bottle" | "beer glass" | "bell cote, bell cot" | "bib" | "bicycle-built-for-two, tandem bicycle, tandem" | "bikini, two-piece" | "binder, ring-binder" | "binoculars, field glasses, opera glasses" | "birdhouse" | "boathouse" | "bobsled, bobsleigh, bob" | "bolo tie, bolo, bola tie, bola" | "bonnet, poke bonnet" | "bookcase" | "bookshop, bookstore, bookstall" | "bottlecap" | "bow" | "bow tie, bow-tie, bowtie" | "brass, memorial tablet, plaque" | "brassiere, bra, bandeau" | "breakwater, groin, groyne, mole, bulwark, seawall, jetty" | "breastplate, aegis, egis" | "broom" | "bucket, pail" | "buckle" | "bulletproof vest" | "bullet train, bullet" | "butcher shop, meat market" | "cab, hack, taxi, taxicab" | "caldron, cauldron" | "candle, taper, wax light" | "cannon" | "canoe" | "can opener, tin opener" | "cardigan" | "car mirror" | "carousel, carrousel, merry-go-round, roundabout, whirligig" | "carpenter's kit, tool kit" | "carton" | "car wheel" | "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM" | "cassette" | "cassette player" | "castle" | "catamaran" | "CD player" | "cello, violoncello" | "cellular telephone, cellular phone, cellphone, cell, mobile phone" | "chain" | "chainlink fence" | "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour" | "chain saw, chainsaw" | "chest" | "chiffonier, commode" | "chime, bell, gong" | "china cabinet, china closet" | "Christmas stocking" | "church, church building" | "cinema, movie theater, movie theatre, movie house, picture palace" | "cleaver, meat cleaver, chopper" | "cliff dwelling" | "cloak" | "clog, geta, patten, sabot" | "cocktail shaker" | "coffee mug" | "coffeepot" | "coil, spiral, volute, whorl, helix" | "combination lock" | "computer keyboard, keypad" | "confectionery, confectionery store, candy store" | "container ship, containership, container vessel" | "convertible" | "corkscrew, bottle screw" | "cornet, horn, trumpet, trump" | "cowboy boot" | "cowboy hat, ten-gallon hat" | "cradle" | "crash helmet" | "crate" | "crib, cot" | "Crock Pot" | "croquet ball" | "crutch" | "cuirass" | "dam, dike, dyke" | "desk" | "desktop computer" | "dial telephone, dial phone" | "diaper, nappy, napkin" | "digital clock" | "digital watch" | "dining table, board" | "dishrag, dishcloth" | "dishwasher, dish washer, dishwashing machine" | "disk brake, disc brake" | "dock, dockage, docking facility" | "dogsled, dog sled, dog sleigh" | "dome" | "doormat, welcome mat" | "drilling platform, offshore rig" | "drum, membranophone, tympan" | "drumstick" | "dumbbell" | "Dutch oven" | "electric fan, blower" | "electric guitar" | "electric locomotive" | "entertainment center" | "envelope" | "espresso maker" | "face powder" | "feather boa, boa" | "file, file cabinet, filing cabinet" | "fireboat" | "fire engine, fire truck" | "fire screen, fireguard" | "flagpole, flagstaff" | "flute, transverse flute" | "folding chair" | "football helmet" | "forklift" | "fountain" | "fountain pen" | "four-poster" | "freight car" | "French horn, horn" | "frying pan, frypan, skillet" | "fur coat" | "garbage truck, dustcart" | "gasmask, respirator, gas helmet" | "gas pump, gasoline pump, petrol pump, island dispenser" | "goblet" | "go-kart" | "golf ball" | "golfcart, golf cart" | "gondola" | "gong, tam-tam" | "gown" | "grand piano, grand" | "greenhouse, nursery, glasshouse" | "grille, radiator grille" | "grocery store, grocery, food market, market" | "guillotine" | "hair slide" | "hair spray" | "half track" | "hammer" | "hamper" | "hand blower, blow dryer, blow drier, hair dryer, hair drier" | "hand-held computer, hand-held microcomputer" | "handkerchief, hankie, hanky, hankey" | "hard disc, hard disk, fixed disk" | "harmonica, mouth organ, harp, mouth harp" | "harp" | "harvester, reaper" | "hatchet" | "holster" | "home theater, home theatre" | "honeycomb" | "hook, claw" | "hoopskirt, crinoline" | "horizontal bar, high bar" | "horse cart, horse-cart" | "hourglass" | "iPod" | "iron, smoothing iron" | "jack-o'-lantern" | "jean, blue jean, denim" | "jeep, landrover" | "jersey, T-shirt, tee shirt" | "jigsaw puzzle" | "jinrikisha, ricksha, rickshaw" | "joystick" | "kimono" | "knee pad" | "knot" | "lab coat, laboratory coat" | "ladle" | "lampshade, lamp shade" | "laptop, laptop computer" | "lawn mower, mower" | "lens cap, lens cover" | "letter opener, paper knife, paperknife" | "library" | "lifeboat" | "lighter, light, igniter, ignitor" | "limousine, limo" | "liner, ocean liner" | "lipstick, lip rouge" | "Loafer" | "lotion" | "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system" | "loupe, jeweler's loupe" | "lumbermill, sawmill" | "magnetic compass" | "mailbag, postbag" | "mailbox, letter box" | "maillot" | "maillot, tank suit" | "manhole cover" | "maraca" | "marimba, xylophone" | "mask" | "matchstick" | "maypole" | "maze, labyrinth" | "measuring cup" | "medicine chest, medicine cabinet" | "megalith, megalithic structure" | "microphone, mike" | "microwave, microwave oven" | "military uniform" | "milk can" | "minibus" | "miniskirt, mini" | "minivan" | "missile" | "mitten" | "mixing bowl" | "mobile home, manufactured home" | "Model T" | "modem" | "monastery" | "monitor" | "moped" | "mortar" | "mortarboard" | "mosque" | "mosquito net" | "motor scooter, scooter" | "mountain bike, all-terrain bike, off-roader" | "mountain tent" | "mouse, computer mouse" | "mousetrap" | "moving van" | "muzzle" | "nail" | "neck brace" | "necklace" | "nipple" | "notebook, notebook computer" | "obelisk" | "oboe, hautboy, hautbois" | "ocarina, sweet potato" | "odometer, hodometer, mileometer, milometer" | "oil filter" | "organ, pipe organ" | "oscilloscope, scope, cathode-ray oscilloscope, CRO" | "overskirt" | "oxcart" | "oxygen mask" | "packet" | "paddle, boat paddle" | "paddlewheel, paddle wheel" | "padlock" | "paintbrush" | "pajama, pyjama, pj's, jammies" | "palace" | "panpipe, pandean pipe, syrinx" | "paper towel" | "parachute, chute" | "parallel bars, bars" | "park bench" | "parking meter" | "passenger car, coach, carriage" | "patio, terrace" | "pay-phone, pay-station" | "pedestal, plinth, footstall" | "pencil box, pencil case" | "pencil sharpener" | "perfume, essence" | "Petri dish" | "photocopier" | "pick, plectrum, plectron" | "pickelhaube" | "picket fence, paling" | "pickup, pickup truck" | "pier" | "piggy bank, penny bank" | "pill bottle" | "pillow" | "ping-pong ball" | "pinwheel" | "pirate, pirate ship" | "pitcher, ewer" | "plane, carpenter's plane, woodworking plane" | "planetarium" | "plastic bag" | "plate rack" | "plow, plough" | "plunger, plumber's helper" | "Polaroid camera, Polaroid Land camera" | "pole" | "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria" | "poncho" | "pool table, billiard table, snooker table" | "pop bottle, soda bottle" | "pot, flowerpot" | "potter's wheel" | "power drill" | "prayer rug, prayer mat" | "printer" | "prison, prison house" | "projectile, missile" | "projector" | "puck, hockey puck" | "punching bag, punch bag, punching ball, punchball" | "purse" | "quill, quill pen" | "quilt, comforter, comfort, puff" | "racer, race car, racing car" | "racket, racquet" | "radiator" | "radio, wireless" | "radio telescope, radio reflector" | "rain barrel" | "recreational vehicle, RV, R.V." | "reel" | "reflex camera" | "refrigerator, icebox" | "remote control, remote" | "restaurant, eating house, eating place, eatery" | "revolver, six-gun, six-shooter" | "rifle" | "rocking chair, rocker" | "rotisserie" | "rubber eraser, rubber, pencil eraser" | "rugby ball" | "rule, ruler" | "running shoe" | "safe" | "safety pin" | "saltshaker, salt shaker" | "sandal" | "sarong" | "sax, saxophone" | "scabbard" | "scale, weighing machine" | "school bus" | "schooner" | "scoreboard" | "screen, CRT screen" | "screw" | "screwdriver" | "seat belt, seatbelt" | "sewing machine" | "shield, buckler" | "shoe shop, shoe-shop, shoe store" | "shoji" | "shopping basket" | "shopping cart" | "shovel" | "shower cap" | "shower curtain" | "ski" | "ski mask" | "sleeping bag" | "slide rule, slipstick" | "sliding door" | "slot, one-armed bandit" | "snorkel" | "snowmobile" | "snowplow, snowplough" | "soap dispenser" | "soccer ball" | "sock" | "solar dish, solar collector, solar furnace" | "sombrero" | "soup bowl" | "space bar" | "space heater" | "space shuttle" | "spatula" | "speedboat" | "spider web, spider's web" | "spindle" | "sports car, sport car" | "spotlight, spot" | "stage" | "steam locomotive" | "steel arch bridge" | "steel drum" | "stethoscope" | "stole" | "stone wall" | "stopwatch, stop watch" | "stove" | "strainer" | "streetcar, tram, tramcar, trolley, trolley car" | "stretcher" | "studio couch, day bed" | "stupa, tope" | "submarine, pigboat, sub, U-boat" | "suit, suit of clothes" | "sundial" | "sunglass" | "sunglasses, dark glasses, shades" | "sunscreen, sunblock, sun blocker" | "suspension bridge" | "swab, swob, mop" | "sweatshirt" | "swimming trunks, bathing trunks" | "swing" | "switch, electric switch, electrical switch" | "syringe" | "table lamp" | "tank, army tank, armored combat vehicle, armoured combat vehicle" | "tape player" | "teapot" | "teddy, teddy bear" | "television, television system" | "tennis ball" | "thatch, thatched roof" | "theater curtain, theatre curtain" | "thimble" | "thresher, thrasher, threshing machine" | "throne" | "tile roof" | "toaster" | "tobacco shop, tobacconist shop, tobacconist" | "toilet seat" | "torch" | "totem pole" | "tow truck, tow car, wrecker" | "toyshop" | "tractor" | "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi" | "tray" | "trench coat" | "tricycle, trike, velocipede" | "trimaran" | "tripod" | "triumphal arch" | "trolleybus, trolley coach, trackless trolley" | "trombone" | "tub, vat" | "turnstile" | "typewriter keyboard" | "umbrella" | "unicycle, monocycle" | "upright, upright piano" | "vacuum, vacuum cleaner" | "vase" | "vault" | "velvet" | "vending machine" | "vestment" | "viaduct" | "violin, fiddle" | "volleyball" | "waffle iron" | "wall clock" | "wallet, billfold, notecase, pocketbook" | "wardrobe, closet, press" | "warplane, military plane" | "washbasin, handbasin, washbowl, lavabo, wash-hand basin" | "washer, automatic washer, washing machine" | "water bottle" | "water jug" | "water tower" | "whiskey jug" | "whistle" | "wig" | "window screen" | "window shade" | "Windsor tie" | "wine bottle" | "wing" | "wok" | "wooden spoon" | "wool, woolen, woollen" | "worm fence, snake fence, snake-rail fence, Virginia fence" | "wreck" | "yawl" | "yurt" | "web site, website, internet site, site" | "comic book" | "crossword puzzle, crossword" | "street sign" | "traffic light, traffic signal, stoplight" | "book jacket, dust cover, dust jacket, dust wrapper" | "menu" | "plate" | "guacamole" | "consomme" | "hot pot, hotpot" | "trifle" | "ice cream, icecream" | "ice lolly, lolly, lollipop, popsicle" | "French loaf" | "bagel, beigel" | "pretzel" | "cheeseburger" | "hotdog, hot dog, red hot" | "mashed potato" | "head cabbage" | "broccoli" | "cauliflower" | "zucchini, courgette" | "spaghetti squash" | "acorn squash" | "butternut squash" | "cucumber, cuke" | "artichoke, globe artichoke" | "bell pepper" | "cardoon" | "mushroom" | "Granny Smith" | "strawberry" | "orange" | "lemon" | "fig" | "pineapple, ananas" | "banana" | "jackfruit, jak, jack" | "custard apple" | "pomegranate" | "hay" | "carbonara" | "chocolate sauce, chocolate syrup" | "dough" | "meat loaf, meatloaf" | "pizza, pizza pie" | "potpie" | "burrito" | "red wine" | "espresso" | "cup" | "eggnog" | "alp" | "bubble" | "cliff, drop, drop-off" | "coral reef" | "geyser" | "lakeside, lakeside road, lakeshore" | "promontory, headland, head, foreland" | "sandbar, sand bar" | "seashore, coast, seacoast, sea-coast" | "valley, vale" | "volcano" | "ballplayer, baseball player" | "groom, bridegroom" | "scuba diver" | "rapeseed" | "daisy" | "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum" | "corn" | "acorn" | "hip, rose hip, rosehip" | "buckeye, horse chestnut, conker" | "coral fungus" | "agaric" | "gyromitra" | "stinkhorn, carrion fungus" | "earthstar" | "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa" | "bolete" | "ear, spike, capitulum" | "toilet tissue, toilet paper, bathroom tissue"> = EFFICIENTNET_V2_S_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ClassifierModel<"tench, Tinca tinca" | "goldfish, Carassius auratus" | "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias" | "tiger shark, Galeocerdo cuvieri" | "hammerhead, hammerhead shark" | "electric ray, crampfish, numbfish, torpedo" | "stingray" | "cock" | "hen" | "ostrich, Struthio camelus" | "brambling, Fringilla montifringilla" | "goldfinch, Carduelis carduelis" | "house finch, linnet, Carpodacus mexicanus" | "junco, snowbird" | "indigo bunting, indigo finch, indigo bird, Passerina cyanea" | "robin, American robin, Turdus migratorius" | "bulbul" | "jay" | "magpie" | "chickadee" | "water ouzel, dipper" | "kite" | "bald eagle, American eagle, Haliaeetus leucocephalus" | "vulture" | "great grey owl, great gray owl, Strix nebulosa" | "European fire salamander, Salamandra salamandra" | "common newt, Triturus vulgaris" | "eft" | "spotted salamander, Ambystoma maculatum" | "axolotl, mud puppy, Ambystoma mexicanum" | "bullfrog, Rana catesbeiana" | "tree frog, tree-frog" | "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui" | "loggerhead, loggerhead turtle, Caretta caretta" | "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea" | "mud turtle" | "terrapin" | "box turtle, box tortoise" | "banded gecko" | "common iguana, iguana, Iguana iguana" | "American chameleon, anole, Anolis carolenensis" | "whiptail, whiptail lizard" | "agama" | "frilled lizard, Chlamydosaurus kingi" | "alligator lizard" | "Gila monster, Heloderma suspectum" | "green lizard, Lacerta viridis" | "African chameleon, Chamaeleo chamaeleon" | "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis" | "African crocodile, Nile crocodile, Crocodylus niloticus" | "American alligator, Alligator mississipiensis" | "triceratops" | "thunder snake, worm snake, Carphophis amoenus" | "ringneck snake, ring-necked snake, ring snake" | "hognose snake, puff adder, sand viper" | "green snake, grass snake" | "king snake, kingsnake" | "garter snake, grass snake" | "water snake" | "vine snake" | "night snake, Hypsiglena torquata" | "boa constrictor, Constrictor constrictor" | "rock python, rock snake, Python sebae" | "Indian cobra, Naja naja" | "green mamba" | "sea snake" | "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus" | "diamondback, diamondback rattlesnake, Crotalus adamanteus" | "sidewinder, horned rattlesnake, Crotalus cerastes" | "trilobite" | "harvestman, daddy longlegs, Phalangium opilio" | "scorpion" | "black and gold garden spider, Argiope aurantia" | "barn spider, Araneus cavaticus" | "garden spider, Aranea diademata" | "black widow, Latrodectus mactans" | "tarantula" | "wolf spider, hunting spider" | "tick" | "centipede" | "black grouse" | "ptarmigan" | "ruffed grouse, partridge, Bonasa umbellus" | "prairie chicken, prairie grouse, prairie fowl" | "peacock" | "quail" | "partridge" | "African grey, African gray, Psittacus erithacus" | "macaw" | "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita" | "lorikeet" | "coucal" | "bee eater" | "hornbill" | "hummingbird" | "jacamar" | "toucan" | "drake" | "red-breasted merganser, Mergus serrator" | "goose" | "black swan, Cygnus atratus" | "tusker" | "echidna, spiny anteater, anteater" | "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus" | "wallaby, brush kangaroo" | "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus" | "wombat" | "jellyfish" | "sea anemone, anemone" | "brain coral" | "flatworm, platyhelminth" | "nematode, nematode worm, roundworm" | "conch" | "snail" | "slug" | "sea slug, nudibranch" | "chiton, coat-of-mail shell, sea cradle, polyplacophore" | "chambered nautilus, pearly nautilus, nautilus" | "Dungeness crab, Cancer magister" | "rock crab, Cancer irroratus" | "fiddler crab" | "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica" | "American lobster, Northern lobster, Maine lobster, Homarus americanus" | "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish" | "crayfish, crawfish, crawdad, crawdaddy" | "hermit crab" | "isopod" | "white stork, Ciconia ciconia" | "black stork, Ciconia nigra" | "spoonbill" | "flamingo" | "little blue heron, Egretta caerulea" | "American egret, great white heron, Egretta albus" | "bittern" | "crane" | "limpkin, Aramus pictus" | "European gallinule, Porphyrio porphyrio" | "American coot, marsh hen, mud hen, water hen, Fulica americana" | "bustard" | "ruddy turnstone, Arenaria interpres" | "red-backed sandpiper, dunlin, Erolia alpina" | "redshank, Tringa totanus" | "dowitcher" | "oystercatcher, oyster catcher" | "pelican" | "king penguin, Aptenodytes patagonica" | "albatross, mollymawk" | "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus" | "killer whale, killer, orca, grampus, sea wolf, Orcinus orca" | "dugong, Dugong dugon" | "sea lion" | "Chihuahua" | "Japanese spaniel" | "Maltese dog, Maltese terrier, Maltese" | "Pekinese, Pekingese, Peke" | "Shih-Tzu" | "Blenheim spaniel" | "papillon" | "toy terrier" | "Rhodesian ridgeback" | "Afghan hound, Afghan" | "basset, basset hound" | "beagle" | "bloodhound, sleuthhound" | "bluetick" | "black-and-tan coonhound" | "Walker hound, Walker foxhound" | "English foxhound" | "redbone" | "borzoi, Russian wolfhound" | "Irish wolfhound" | "Italian greyhound" | "whippet" | "Ibizan hound, Ibizan Podenco" | "Norwegian elkhound, elkhound" | "otterhound, otter hound" | "Saluki, gazelle hound" | "Scottish deerhound, deerhound" | "Weimaraner" | "Staffordshire bullterrier, Staffordshire bull terrier" | "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier" | "Bedlington terrier" | "Border terrier" | "Kerry blue terrier" | "Irish terrier" | "Norfolk terrier" | "Norwich terrier" | "Yorkshire terrier" | "wire-haired fox terrier" | "Lakeland terrier" | "Sealyham terrier, Sealyham" | "Airedale, Airedale terrier" | "cairn, cairn terrier" | "Australian terrier" | "Dandie Dinmont, Dandie Dinmont terrier" | "Boston bull, Boston terrier" | "miniature schnauzer" | "giant schnauzer" | "standard schnauzer" | "Scotch terrier, Scottish terrier, Scottie" | "Tibetan terrier, chrysanthemum dog" | "silky terrier, Sydney silky" | "soft-coated wheaten terrier" | "West Highland white terrier" | "Lhasa, Lhasa apso" | "flat-coated retriever" | "curly-coated retriever" | "golden retriever" | "Labrador retriever" | "Chesapeake Bay retriever" | "German short-haired pointer" | "vizsla, Hungarian pointer" | "English setter" | "Irish setter, red setter" | "Gordon setter" | "Brittany spaniel" | "clumber, clumber spaniel" | "English springer, English springer spaniel" | "Welsh springer spaniel" | "cocker spaniel, English cocker spaniel, cocker" | "Sussex spaniel" | "Irish water spaniel" | "kuvasz" | "schipperke" | "groenendael" | "malinois" | "briard" | "kelpie" | "komondor" | "Old English sheepdog, bobtail" | "Shetland sheepdog, Shetland sheep dog, Shetland" | "collie" | "Border collie" | "Bouvier des Flandres, Bouviers des Flandres" | "Rottweiler" | "German shepherd, German shepherd dog, German police dog, alsatian" | "Doberman, Doberman pinscher" | "miniature pinscher" | "Greater Swiss Mountain dog" | "Bernese mountain dog" | "Appenzeller" | "EntleBucher" | "boxer" | "bull mastiff" | "Tibetan mastiff" | "French bulldog" | "Great Dane" | "Saint Bernard, St Bernard" | "Eskimo dog, husky" | "malamute, malemute, Alaskan malamute" | "Siberian husky" | "dalmatian, coach dog, carriage dog" | "affenpinscher, monkey pinscher, monkey dog" | "basenji" | "pug, pug-dog" | "Leonberg" | "Newfoundland, Newfoundland dog" | "Great Pyrenees" | "Samoyed, Samoyede" | "Pomeranian" | "chow, chow chow" | "keeshond" | "Brabancon griffon" | "Pembroke, Pembroke Welsh corgi" | "Cardigan, Cardigan Welsh corgi" | "toy poodle" | "miniature poodle" | "standard poodle" | "Mexican hairless" | "timber wolf, grey wolf, gray wolf, Canis lupus" | "white wolf, Arctic wolf, Canis lupus tundrarum" | "red wolf, maned wolf, Canis rufus, Canis niger" | "coyote, prairie wolf, brush wolf, Canis latrans" | "dingo, warrigal, warragal, Canis dingo" | "dhole, Cuon alpinus" | "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus" | "hyena, hyaena" | "red fox, Vulpes vulpes" | "kit fox, Vulpes macrotis" | "Arctic fox, white fox, Alopex lagopus" | "grey fox, gray fox, Urocyon cinereoargenteus" | "tabby, tabby cat" | "tiger cat" | "Persian cat" | "Siamese cat, Siamese" | "Egyptian cat" | "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor" | "lynx, catamount" | "leopard, Panthera pardus" | "snow leopard, ounce, Panthera uncia" | "jaguar, panther, Panthera onca, Felis onca" | "lion, king of beasts, Panthera leo" | "tiger, Panthera tigris" | "cheetah, chetah, Acinonyx jubatus" | "brown bear, bruin, Ursus arctos" | "American black bear, black bear, Ursus americanus, Euarctos americanus" | "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus" | "sloth bear, Melursus ursinus, Ursus ursinus" | "mongoose" | "meerkat, mierkat" | "tiger beetle" | "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle" | "ground beetle, carabid beetle" | "long-horned beetle, longicorn, longicorn beetle" | "leaf beetle, chrysomelid" | "dung beetle" | "rhinoceros beetle" | "weevil" | "fly" | "bee" | "ant, emmet, pismire" | "grasshopper, hopper" | "cricket" | "walking stick, walkingstick, stick insect" | "cockroach, roach" | "focused mantis, mantid" | "cicada, cicala" | "leafhopper" | "lacewing, lacewing fly" | "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk" | "damselfly" | "admiral" | "ringlet, ringlet butterfly" | "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus" | "cabbage butterfly" | "sulphur butterfly, sulfur butterfly" | "lycaenid, lycaenid butterfly" | "starfish, sea star" | "sea urchin" | "sea cucumber, holothurian" | "wood rabbit, cottontail, cottontail rabbit" | "hare" | "Angora, Angora rabbit" | "hamster" | "porcupine, hedgehog" | "fox squirrel, eastern fox squirrel, Sciurus niger" | "marmot" | "beaver" | "guinea pig, Cavia cobaya" | "sorrel" | "zebra" | "hog, pig, grunter, squealer, Sus scrofa" | "wild boar, boar, Sus scrofa" | "warthog" | "hippopotamus, hippo, river horse, Hippopotamus amphibius" | "ox" | "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis" | "bison" | "ram, tup" | "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis" | "ibex, Capra ibex" | "hartebeest" | "impala, Aepyceros melampus" | "gazelle" | "Arabian camel, dromedary, Camelus dromedarius" | "llama" | "weasel" | "mink" | "polecat, fitch, foulmart, foumart, Mustela putorius" | "black-footed ferret, ferret, Mustela nigripes" | "otter" | "skunk, polecat, wood pussy" | "badger" | "armadillo" | "three-toed sloth, ai, Bradypus tridactylus" | "orangutan, orang, orangutang, Pongo pygmaeus" | "gorilla, Gorilla gorilla" | "chimpanzee, chimp, Pan troglodytes" | "gibbon, Hylobates lar" | "siamang, Hylobates syndactylus, Symphalangus syndactylus" | "guenon, guenon monkey" | "patas, hussar monkey, Erythrocebus patas" | "baboon" | "macaque" | "langur" | "colobus, colobus monkey" | "proboscis monkey, Nasalis larvatus" | "marmoset" | "capuchin, ringtail, Cebus capucinus" | "howler monkey, howler" | "titi, titi monkey" | "spider monkey, Ateles geoffroyi" | "squirrel monkey, Saimiri sciureus" | "Madagascar cat, ring-tailed lemur, Lemur catta" | "indri, indris, Indri indri, Indri brevicaudatus" | "Indian elephant, Elephas maximus" | "African elephant, Loxodonta africana" | "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens" | "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca" | "barracouta, snoek" | "raw eel, eel" | "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch" | "rock beauty, Holocanthus tricolor" | "anemone fish" | "sturgeon" | "gar, garfish, garpike, billfish, Lepisosteus osseus" | "lionfish" | "puffer, pufferfish, blowfish, globefish" | "abacus" | "abaya" | "academic gown, academic robe, judge's robe" | "accordion, piano accordion, squeeze box" | "acoustic guitar" | "aircraft carrier, carrier, flattop, attack aircraft carrier" | "airliner" | "airship, dirigible" | "altar" | "ambulance" | "amphibian, amphibious vehicle" | "analog clock" | "apiary, bee house" | "apron" | "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin" | "assault rifle, assault gun" | "backpack, back pack, knapsack, packsack, rucksack, haversack" | "bakery, bakeshop, bakehouse" | "balance beam, beam" | "balloon" | "ballpoint, ballpoint pen, ballpen, Biro" | "Band Aid" | "banjo" | "bannister, banister, balustrade, balusters, handrail" | "barbell" | "barber chair" | "barbershop" | "barn" | "barometer" | "barrel, cask" | "barrow, garden cart, lawn cart, wheelbarrow" | "baseball" | "basketball" | "bassinet" | "bassoon" | "bathing cap, swimming cap" | "bath towel" | "bathtub, bathing tub, bath, tub" | "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon" | "beacon, lighthouse, beacon light, pharos" | "beaker" | "bearskin, busby, shako" | "beer bottle" | "beer glass" | "bell cote, bell cot" | "bib" | "bicycle-built-for-two, tandem bicycle, tandem" | "bikini, two-piece" | "binder, ring-binder" | "binoculars, field glasses, opera glasses" | "birdhouse" | "boathouse" | "bobsled, bobsleigh, bob" | "bolo tie, bolo, bola tie, bola" | "bonnet, poke bonnet" | "bookcase" | "bookshop, bookstore, bookstall" | "bottlecap" | "bow" | "bow tie, bow-tie, bowtie" | "brass, memorial tablet, plaque" | "brassiere, bra, bandeau" | "breakwater, groin, groyne, mole, bulwark, seawall, jetty" | "breastplate, aegis, egis" | "broom" | "bucket, pail" | "buckle" | "bulletproof vest" | "bullet train, bullet" | "butcher shop, meat market" | "cab, hack, taxi, taxicab" | "caldron, cauldron" | "candle, taper, wax light" | "cannon" | "canoe" | "can opener, tin opener" | "cardigan" | "car mirror" | "carousel, carrousel, merry-go-round, roundabout, whirligig" | "carpenter's kit, tool kit" | "carton" | "car wheel" | "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM" | "cassette" | "cassette player" | "castle" | "catamaran" | "CD player" | "cello, violoncello" | "cellular telephone, cellular phone, cellphone, cell, mobile phone" | "chain" | "chainlink fence" | "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour" | "chain saw, chainsaw" | "chest" | "chiffonier, commode" | "chime, bell, gong" | "china cabinet, china closet" | "Christmas stocking" | "church, church building" | "cinema, movie theater, movie theatre, movie house, picture palace" | "cleaver, meat cleaver, chopper" | "cliff dwelling" | "cloak" | "clog, geta, patten, sabot" | "cocktail shaker" | "coffee mug" | "coffeepot" | "coil, spiral, volute, whorl, helix" | "combination lock" | "computer keyboard, keypad" | "confectionery, confectionery store, candy store" | "container ship, containership, container vessel" | "convertible" | "corkscrew, bottle screw" | "cornet, horn, trumpet, trump" | "cowboy boot" | "cowboy hat, ten-gallon hat" | "cradle" | "crash helmet" | "crate" | "crib, cot" | "Crock Pot" | "croquet ball" | "crutch" | "cuirass" | "dam, dike, dyke" | "desk" | "desktop computer" | "dial telephone, dial phone" | "diaper, nappy, napkin" | "digital clock" | "digital watch" | "dining table, board" | "dishrag, dishcloth" | "dishwasher, dish washer, dishwashing machine" | "disk brake, disc brake" | "dock, dockage, docking facility" | "dogsled, dog sled, dog sleigh" | "dome" | "doormat, welcome mat" | "drilling platform, offshore rig" | "drum, membranophone, tympan" | "drumstick" | "dumbbell" | "Dutch oven" | "electric fan, blower" | "electric guitar" | "electric locomotive" | "entertainment center" | "envelope" | "espresso maker" | "face powder" | "feather boa, boa" | "file, file cabinet, filing cabinet" | "fireboat" | "fire engine, fire truck" | "fire screen, fireguard" | "flagpole, flagstaff" | "flute, transverse flute" | "folding chair" | "football helmet" | "forklift" | "fountain" | "fountain pen" | "four-poster" | "freight car" | "French horn, horn" | "frying pan, frypan, skillet" | "fur coat" | "garbage truck, dustcart" | "gasmask, respirator, gas helmet" | "gas pump, gasoline pump, petrol pump, island dispenser" | "goblet" | "go-kart" | "golf ball" | "golfcart, golf cart" | "gondola" | "gong, tam-tam" | "gown" | "grand piano, grand" | "greenhouse, nursery, glasshouse" | "grille, radiator grille" | "grocery store, grocery, food market, market" | "guillotine" | "hair slide" | "hair spray" | "half track" | "hammer" | "hamper" | "hand blower, blow dryer, blow drier, hair dryer, hair drier" | "hand-held computer, hand-held microcomputer" | "handkerchief, hankie, hanky, hankey" | "hard disc, hard disk, fixed disk" | "harmonica, mouth organ, harp, mouth harp" | "harp" | "harvester, reaper" | "hatchet" | "holster" | "home theater, home theatre" | "honeycomb" | "hook, claw" | "hoopskirt, crinoline" | "horizontal bar, high bar" | "horse cart, horse-cart" | "hourglass" | "iPod" | "iron, smoothing iron" | "jack-o'-lantern" | "jean, blue jean, denim" | "jeep, landrover" | "jersey, T-shirt, tee shirt" | "jigsaw puzzle" | "jinrikisha, ricksha, rickshaw" | "joystick" | "kimono" | "knee pad" | "knot" | "lab coat, laboratory coat" | "ladle" | "lampshade, lamp shade" | "laptop, laptop computer" | "lawn mower, mower" | "lens cap, lens cover" | "letter opener, paper knife, paperknife" | "library" | "lifeboat" | "lighter, light, igniter, ignitor" | "limousine, limo" | "liner, ocean liner" | "lipstick, lip rouge" | "Loafer" | "lotion" | "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system" | "loupe, jeweler's loupe" | "lumbermill, sawmill" | "magnetic compass" | "mailbag, postbag" | "mailbox, letter box" | "maillot" | "maillot, tank suit" | "manhole cover" | "maraca" | "marimba, xylophone" | "mask" | "matchstick" | "maypole" | "maze, labyrinth" | "measuring cup" | "medicine chest, medicine cabinet" | "megalith, megalithic structure" | "microphone, mike" | "microwave, microwave oven" | "military uniform" | "milk can" | "minibus" | "miniskirt, mini" | "minivan" | "missile" | "mitten" | "mixing bowl" | "mobile home, manufactured home" | "Model T" | "modem" | "monastery" | "monitor" | "moped" | "mortar" | "mortarboard" | "mosque" | "mosquito net" | "motor scooter, scooter" | "mountain bike, all-terrain bike, off-roader" | "mountain tent" | "mouse, computer mouse" | "mousetrap" | "moving van" | "muzzle" | "nail" | "neck brace" | "necklace" | "nipple" | "notebook, notebook computer" | "obelisk" | "oboe, hautboy, hautbois" | "ocarina, sweet potato" | "odometer, hodometer, mileometer, milometer" | "oil filter" | "organ, pipe organ" | "oscilloscope, scope, cathode-ray oscilloscope, CRO" | "overskirt" | "oxcart" | "oxygen mask" | "packet" | "paddle, boat paddle" | "paddlewheel, paddle wheel" | "padlock" | "paintbrush" | "pajama, pyjama, pj's, jammies" | "palace" | "panpipe, pandean pipe, syrinx" | "paper towel" | "parachute, chute" | "parallel bars, bars" | "park bench" | "parking meter" | "passenger car, coach, carriage" | "patio, terrace" | "pay-phone, pay-station" | "pedestal, plinth, footstall" | "pencil box, pencil case" | "pencil sharpener" | "perfume, essence" | "Petri dish" | "photocopier" | "pick, plectrum, plectron" | "pickelhaube" | "picket fence, paling" | "pickup, pickup truck" | "pier" | "piggy bank, penny bank" | "pill bottle" | "pillow" | "ping-pong ball" | "pinwheel" | "pirate, pirate ship" | "pitcher, ewer" | "plane, carpenter's plane, woodworking plane" | "planetarium" | "plastic bag" | "plate rack" | "plow, plough" | "plunger, plumber's helper" | "Polaroid camera, Polaroid Land camera" | "pole" | "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria" | "poncho" | "pool table, billiard table, snooker table" | "pop bottle, soda bottle" | "pot, flowerpot" | "potter's wheel" | "power drill" | "prayer rug, prayer mat" | "printer" | "prison, prison house" | "projectile, missile" | "projector" | "puck, hockey puck" | "punching bag, punch bag, punching ball, punchball" | "purse" | "quill, quill pen" | "quilt, comforter, comfort, puff" | "racer, race car, racing car" | "racket, racquet" | "radiator" | "radio, wireless" | "radio telescope, radio reflector" | "rain barrel" | "recreational vehicle, RV, R.V." | "reel" | "reflex camera" | "refrigerator, icebox" | "remote control, remote" | "restaurant, eating house, eating place, eatery" | "revolver, six-gun, six-shooter" | "rifle" | "rocking chair, rocker" | "rotisserie" | "rubber eraser, rubber, pencil eraser" | "rugby ball" | "rule, ruler" | "running shoe" | "safe" | "safety pin" | "saltshaker, salt shaker" | "sandal" | "sarong" | "sax, saxophone" | "scabbard" | "scale, weighing machine" | "school bus" | "schooner" | "scoreboard" | "screen, CRT screen" | "screw" | "screwdriver" | "seat belt, seatbelt" | "sewing machine" | "shield, buckler" | "shoe shop, shoe-shop, shoe store" | "shoji" | "shopping basket" | "shopping cart" | "shovel" | "shower cap" | "shower curtain" | "ski" | "ski mask" | "sleeping bag" | "slide rule, slipstick" | "sliding door" | "slot, one-armed bandit" | "snorkel" | "snowmobile" | "snowplow, snowplough" | "soap dispenser" | "soccer ball" | "sock" | "solar dish, solar collector, solar furnace" | "sombrero" | "soup bowl" | "space bar" | "space heater" | "space shuttle" | "spatula" | "speedboat" | "spider web, spider's web" | "spindle" | "sports car, sport car" | "spotlight, spot" | "stage" | "steam locomotive" | "steel arch bridge" | "steel drum" | "stethoscope" | "stole" | "stone wall" | "stopwatch, stop watch" | "stove" | "strainer" | "streetcar, tram, tramcar, trolley, trolley car" | "stretcher" | "studio couch, day bed" | "stupa, tope" | "submarine, pigboat, sub, U-boat" | "suit, suit of clothes" | "sundial" | "sunglass" | "sunglasses, dark glasses, shades" | "sunscreen, sunblock, sun blocker" | "suspension bridge" | "swab, swob, mop" | "sweatshirt" | "swimming trunks, bathing trunks" | "swing" | "switch, electric switch, electrical switch" | "syringe" | "table lamp" | "tank, army tank, armored combat vehicle, armoured combat vehicle" | "tape player" | "teapot" | "teddy, teddy bear" | "television, television system" | "tennis ball" | "thatch, thatched roof" | "theater curtain, theatre curtain" | "thimble" | "thresher, thrasher, threshing machine" | "throne" | "tile roof" | "toaster" | "tobacco shop, tobacconist shop, tobacconist" | "toilet seat" | "torch" | "totem pole" | "tow truck, tow car, wrecker" | "toyshop" | "tractor" | "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi" | "tray" | "trench coat" | "tricycle, trike, velocipede" | "trimaran" | "tripod" | "triumphal arch" | "trolleybus, trolley coach, trackless trolley" | "trombone" | "tub, vat" | "turnstile" | "typewriter keyboard" | "umbrella" | "unicycle, monocycle" | "upright, upright piano" | "vacuum, vacuum cleaner" | "vase" | "vault" | "velvet" | "vending machine" | "vestment" | "viaduct" | "violin, fiddle" | "volleyball" | "waffle iron" | "wall clock" | "wallet, billfold, notecase, pocketbook" | "wardrobe, closet, press" | "warplane, military plane" | "washbasin, handbasin, washbowl, lavabo, wash-hand basin" | "washer, automatic washer, washing machine" | "water bottle" | "water jug" | "water tower" | "whiskey jug" | "whistle" | "wig" | "window screen" | "window shade" | "Windsor tie" | "wine bottle" | "wing" | "wok" | "wooden spoon" | "wool, woolen, woollen" | "worm fence, snake fence, snake-rail fence, Virginia fence" | "wreck" | "yawl" | "yurt" | "web site, website, internet site, site" | "comic book" | "crossword puzzle, crossword" | "street sign" | "traffic light, traffic signal, stoplight" | "book jacket, dust cover, dust jacket, dust wrapper" | "menu" | "plate" | "guacamole" | "consomme" | "hot pot, hotpot" | "trifle" | "ice cream, icecream" | "ice lolly, lolly, lollipop, popsicle" | "French loaf" | "bagel, beigel" | "pretzel" | "cheeseburger" | "hotdog, hot dog, red hot" | "mashed potato" | "head cabbage" | "broccoli" | "cauliflower" | "zucchini, courgette" | "spaghetti squash" | "acorn squash" | "butternut squash" | "cucumber, cuke" | "artichoke, globe artichoke" | "bell pepper" | "cardoon" | "mushroom" | "Granny Smith" | "strawberry" | "orange" | "lemon" | "fig" | "pineapple, ananas" | "banana" | "jackfruit, jak, jack" | "custard apple" | "pomegranate" | "hay" | "carbonara" | "chocolate sauce, chocolate syrup" | "dough" | "meat loaf, meatloaf" | "pizza, pizza pie" | "potpie" | "burrito" | "red wine" | "espresso" | "cup" | "eggnog" | "alp" | "bubble" | "cliff, drop, drop-off" | "coral reef" | "geyser" | "lakeside, lakeside road, lakeshore" | "promontory, headland, head, foreland" | "sandbar, sand bar" | "seashore, coast, seacoast, sea-coast" | "valley, vale" | "volcano" | "ballplayer, baseball player" | "groom, bridegroom" | "scuba diver" | "rapeseed" | "daisy" | "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum" | "corn" | "acorn" | "hip, rose hip, rosehip" | "buckeye, horse chestnut, conker" | "coral fungus" | "agaric" | "gyromitra" | "stinkhorn, carrion fungus" | "earthstar" | "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa" | "bolete" | "ear, spike, capitulum" | "toilet tissue, toilet paper, bathroom tissue"> = EFFICIENTNET_V2_S_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: ClassifierModel<"tench, Tinca tinca" | "goldfish, Carassius auratus" | "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias" | "tiger shark, Galeocerdo cuvieri" | "hammerhead, hammerhead shark" | "electric ray, crampfish, numbfish, torpedo" | "stingray" | "cock" | "hen" | "ostrich, Struthio camelus" | "brambling, Fringilla montifringilla" | "goldfinch, Carduelis carduelis" | "house finch, linnet, Carpodacus mexicanus" | "junco, snowbird" | "indigo bunting, indigo finch, indigo bird, Passerina cyanea" | "robin, American robin, Turdus migratorius" | "bulbul" | "jay" | "magpie" | "chickadee" | "water ouzel, dipper" | "kite" | "bald eagle, American eagle, Haliaeetus leucocephalus" | "vulture" | "great grey owl, great gray owl, Strix nebulosa" | "European fire salamander, Salamandra salamandra" | "common newt, Triturus vulgaris" | "eft" | "spotted salamander, Ambystoma maculatum" | "axolotl, mud puppy, Ambystoma mexicanum" | "bullfrog, Rana catesbeiana" | "tree frog, tree-frog" | "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui" | "loggerhead, loggerhead turtle, Caretta caretta" | "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea" | "mud turtle" | "terrapin" | "box turtle, box tortoise" | "banded gecko" | "common iguana, iguana, Iguana iguana" | "American chameleon, anole, Anolis carolenensis" | "whiptail, whiptail lizard" | "agama" | "frilled lizard, Chlamydosaurus kingi" | "alligator lizard" | "Gila monster, Heloderma suspectum" | "green lizard, Lacerta viridis" | "African chameleon, Chamaeleo chamaeleon" | "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis" | "African crocodile, Nile crocodile, Crocodylus niloticus" | "American alligator, Alligator mississipiensis" | "triceratops" | "thunder snake, worm snake, Carphophis amoenus" | "ringneck snake, ring-necked snake, ring snake" | "hognose snake, puff adder, sand viper" | "green snake, grass snake" | "king snake, kingsnake" | "garter snake, grass snake" | "water snake" | "vine snake" | "night snake, Hypsiglena torquata" | "boa constrictor, Constrictor constrictor" | "rock python, rock snake, Python sebae" | "Indian cobra, Naja naja" | "green mamba" | "sea snake" | "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus" | "diamondback, diamondback rattlesnake, Crotalus adamanteus" | "sidewinder, horned rattlesnake, Crotalus cerastes" | "trilobite" | "harvestman, daddy longlegs, Phalangium opilio" | "scorpion" | "black and gold garden spider, Argiope aurantia" | "barn spider, Araneus cavaticus" | "garden spider, Aranea diademata" | "black widow, Latrodectus mactans" | "tarantula" | "wolf spider, hunting spider" | "tick" | "centipede" | "black grouse" | "ptarmigan" | "ruffed grouse, partridge, Bonasa umbellus" | "prairie chicken, prairie grouse, prairie fowl" | "peacock" | "quail" | "partridge" | "African grey, African gray, Psittacus erithacus" | "macaw" | "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita" | "lorikeet" | "coucal" | "bee eater" | "hornbill" | "hummingbird" | "jacamar" | "toucan" | "drake" | "red-breasted merganser, Mergus serrator" | "goose" | "black swan, Cygnus atratus" | "tusker" | "echidna, spiny anteater, anteater" | "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus" | "wallaby, brush kangaroo" | "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus" | "wombat" | "jellyfish" | "sea anemone, anemone" | "brain coral" | "flatworm, platyhelminth" | "nematode, nematode worm, roundworm" | "conch" | "snail" | "slug" | "sea slug, nudibranch" | "chiton, coat-of-mail shell, sea cradle, polyplacophore" | "chambered nautilus, pearly nautilus, nautilus" | "Dungeness crab, Cancer magister" | "rock crab, Cancer irroratus" | "fiddler crab" | "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica" | "American lobster, Northern lobster, Maine lobster, Homarus americanus" | "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish" | "crayfish, crawfish, crawdad, crawdaddy" | "hermit crab" | "isopod" | "white stork, Ciconia ciconia" | "black stork, Ciconia nigra" | "spoonbill" | "flamingo" | "little blue heron, Egretta caerulea" | "American egret, great white heron, Egretta albus" | "bittern" | "crane" | "limpkin, Aramus pictus" | "European gallinule, Porphyrio porphyrio" | "American coot, marsh hen, mud hen, water hen, Fulica americana" | "bustard" | "ruddy turnstone, Arenaria interpres" | "red-backed sandpiper, dunlin, Erolia alpina" | "redshank, Tringa totanus" | "dowitcher" | "oystercatcher, oyster catcher" | "pelican" | "king penguin, Aptenodytes patagonica" | "albatross, mollymawk" | "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus" | "killer whale, killer, orca, grampus, sea wolf, Orcinus orca" | "dugong, Dugong dugon" | "sea lion" | "Chihuahua" | "Japanese spaniel" | "Maltese dog, Maltese terrier, Maltese" | "Pekinese, Pekingese, Peke" | "Shih-Tzu" | "Blenheim spaniel" | "papillon" | "toy terrier" | "Rhodesian ridgeback" | "Afghan hound, Afghan" | "basset, basset hound" | "beagle" | "bloodhound, sleuthhound" | "bluetick" | "black-and-tan coonhound" | "Walker hound, Walker foxhound" | "English foxhound" | "redbone" | "borzoi, Russian wolfhound" | "Irish wolfhound" | "Italian greyhound" | "whippet" | "Ibizan hound, Ibizan Podenco" | "Norwegian elkhound, elkhound" | "otterhound, otter hound" | "Saluki, gazelle hound" | "Scottish deerhound, deerhound" | "Weimaraner" | "Staffordshire bullterrier, Staffordshire bull terrier" | "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier" | "Bedlington terrier" | "Border terrier" | "Kerry blue terrier" | "Irish terrier" | "Norfolk terrier" | "Norwich terrier" | "Yorkshire terrier" | "wire-haired fox terrier" | "Lakeland terrier" | "Sealyham terrier, Sealyham" | "Airedale, Airedale terrier" | "cairn, cairn terrier" | "Australian terrier" | "Dandie Dinmont, Dandie Dinmont terrier" | "Boston bull, Boston terrier" | "miniature schnauzer" | "giant schnauzer" | "standard schnauzer" | "Scotch terrier, Scottish terrier, Scottie" | "Tibetan terrier, chrysanthemum dog" | "silky terrier, Sydney silky" | "soft-coated wheaten terrier" | "West Highland white terrier" | "Lhasa, Lhasa apso" | "flat-coated retriever" | "curly-coated retriever" | "golden retriever" | "Labrador retriever" | "Chesapeake Bay retriever" | "German short-haired pointer" | "vizsla, Hungarian pointer" | "English setter" | "Irish setter, red setter" | "Gordon setter" | "Brittany spaniel" | "clumber, clumber spaniel" | "English springer, English springer spaniel" | "Welsh springer spaniel" | "cocker spaniel, English cocker spaniel, cocker" | "Sussex spaniel" | "Irish water spaniel" | "kuvasz" | "schipperke" | "groenendael" | "malinois" | "briard" | "kelpie" | "komondor" | "Old English sheepdog, bobtail" | "Shetland sheepdog, Shetland sheep dog, Shetland" | "collie" | "Border collie" | "Bouvier des Flandres, Bouviers des Flandres" | "Rottweiler" | "German shepherd, German shepherd dog, German police dog, alsatian" | "Doberman, Doberman pinscher" | "miniature pinscher" | "Greater Swiss Mountain dog" | "Bernese mountain dog" | "Appenzeller" | "EntleBucher" | "boxer" | "bull mastiff" | "Tibetan mastiff" | "French bulldog" | "Great Dane" | "Saint Bernard, St Bernard" | "Eskimo dog, husky" | "malamute, malemute, Alaskan malamute" | "Siberian husky" | "dalmatian, coach dog, carriage dog" | "affenpinscher, monkey pinscher, monkey dog" | "basenji" | "pug, pug-dog" | "Leonberg" | "Newfoundland, Newfoundland dog" | "Great Pyrenees" | "Samoyed, Samoyede" | "Pomeranian" | "chow, chow chow" | "keeshond" | "Brabancon griffon" | "Pembroke, Pembroke Welsh corgi" | "Cardigan, Cardigan Welsh corgi" | "toy poodle" | "miniature poodle" | "standard poodle" | "Mexican hairless" | "timber wolf, grey wolf, gray wolf, Canis lupus" | "white wolf, Arctic wolf, Canis lupus tundrarum" | "red wolf, maned wolf, Canis rufus, Canis niger" | "coyote, prairie wolf, brush wolf, Canis latrans" | "dingo, warrigal, warragal, Canis dingo" | "dhole, Cuon alpinus" | "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus" | "hyena, hyaena" | "red fox, Vulpes vulpes" | "kit fox, Vulpes macrotis" | "Arctic fox, white fox, Alopex lagopus" | "grey fox, gray fox, Urocyon cinereoargenteus" | "tabby, tabby cat" | "tiger cat" | "Persian cat" | "Siamese cat, Siamese" | "Egyptian cat" | "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor" | "lynx, catamount" | "leopard, Panthera pardus" | "snow leopard, ounce, Panthera uncia" | "jaguar, panther, Panthera onca, Felis onca" | "lion, king of beasts, Panthera leo" | "tiger, Panthera tigris" | "cheetah, chetah, Acinonyx jubatus" | "brown bear, bruin, Ursus arctos" | "American black bear, black bear, Ursus americanus, Euarctos americanus" | "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus" | "sloth bear, Melursus ursinus, Ursus ursinus" | "mongoose" | "meerkat, mierkat" | "tiger beetle" | "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle" | "ground beetle, carabid beetle" | "long-horned beetle, longicorn, longicorn beetle" | "leaf beetle, chrysomelid" | "dung beetle" | "rhinoceros beetle" | "weevil" | "fly" | "bee" | "ant, emmet, pismire" | "grasshopper, hopper" | "cricket" | "walking stick, walkingstick, stick insect" | "cockroach, roach" | "focused mantis, mantid" | "cicada, cicala" | "leafhopper" | "lacewing, lacewing fly" | "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk" | "damselfly" | "admiral" | "ringlet, ringlet butterfly" | "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus" | "cabbage butterfly" | "sulphur butterfly, sulfur butterfly" | "lycaenid, lycaenid butterfly" | "starfish, sea star" | "sea urchin" | "sea cucumber, holothurian" | "wood rabbit, cottontail, cottontail rabbit" | "hare" | "Angora, Angora rabbit" | "hamster" | "porcupine, hedgehog" | "fox squirrel, eastern fox squirrel, Sciurus niger" | "marmot" | "beaver" | "guinea pig, Cavia cobaya" | "sorrel" | "zebra" | "hog, pig, grunter, squealer, Sus scrofa" | "wild boar, boar, Sus scrofa" | "warthog" | "hippopotamus, hippo, river horse, Hippopotamus amphibius" | "ox" | "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis" | "bison" | "ram, tup" | "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis" | "ibex, Capra ibex" | "hartebeest" | "impala, Aepyceros melampus" | "gazelle" | "Arabian camel, dromedary, Camelus dromedarius" | "llama" | "weasel" | "mink" | "polecat, fitch, foulmart, foumart, Mustela putorius" | "black-footed ferret, ferret, Mustela nigripes" | "otter" | "skunk, polecat, wood pussy" | "badger" | "armadillo" | "three-toed sloth, ai, Bradypus tridactylus" | "orangutan, orang, orangutang, Pongo pygmaeus" | "gorilla, Gorilla gorilla" | "chimpanzee, chimp, Pan troglodytes" | "gibbon, Hylobates lar" | "siamang, Hylobates syndactylus, Symphalangus syndactylus" | "guenon, guenon monkey" | "patas, hussar monkey, Erythrocebus patas" | "baboon" | "macaque" | "langur" | "colobus, colobus monkey" | "proboscis monkey, Nasalis larvatus" | "marmoset" | "capuchin, ringtail, Cebus capucinus" | "howler monkey, howler" | "titi, titi monkey" | "spider monkey, Ateles geoffroyi" | "squirrel monkey, Saimiri sciureus" | "Madagascar cat, ring-tailed lemur, Lemur catta" | "indri, indris, Indri indri, Indri brevicaudatus" | "Indian elephant, Elephas maximus" | "African elephant, Loxodonta africana" | "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens" | "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca" | "barracouta, snoek" | "raw eel, eel" | "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch" | "rock beauty, Holocanthus tricolor" | "anemone fish" | "sturgeon" | "gar, garfish, garpike, billfish, Lepisosteus osseus" | "lionfish" | "puffer, pufferfish, blowfish, globefish" | "abacus" | "abaya" | "academic gown, academic robe, judge's robe" | "accordion, piano accordion, squeeze box" | "acoustic guitar" | "aircraft carrier, carrier, flattop, attack aircraft carrier" | "airliner" | "airship, dirigible" | "altar" | "ambulance" | "amphibian, amphibious vehicle" | "analog clock" | "apiary, bee house" | "apron" | "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin" | "assault rifle, assault gun" | "backpack, back pack, knapsack, packsack, rucksack, haversack" | "bakery, bakeshop, bakehouse" | "balance beam, beam" | "balloon" | "ballpoint, ballpoint pen, ballpen, Biro" | "Band Aid" | "banjo" | "bannister, banister, balustrade, balusters, handrail" | "barbell" | "barber chair" | "barbershop" | "barn" | "barometer" | "barrel, cask" | "barrow, garden cart, lawn cart, wheelbarrow" | "baseball" | "basketball" | "bassinet" | "bassoon" | "bathing cap, swimming cap" | "bath towel" | "bathtub, bathing tub, bath, tub" | "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon" | "beacon, lighthouse, beacon light, pharos" | "beaker" | "bearskin, busby, shako" | "beer bottle" | "beer glass" | "bell cote, bell cot" | "bib" | "bicycle-built-for-two, tandem bicycle, tandem" | "bikini, two-piece" | "binder, ring-binder" | "binoculars, field glasses, opera glasses" | "birdhouse" | "boathouse" | "bobsled, bobsleigh, bob" | "bolo tie, bolo, bola tie, bola" | "bonnet, poke bonnet" | "bookcase" | "bookshop, bookstore, bookstall" | "bottlecap" | "bow" | "bow tie, bow-tie, bowtie" | "brass, memorial tablet, plaque" | "brassiere, bra, bandeau" | "breakwater, groin, groyne, mole, bulwark, seawall, jetty" | "breastplate, aegis, egis" | "broom" | "bucket, pail" | "buckle" | "bulletproof vest" | "bullet train, bullet" | "butcher shop, meat market" | "cab, hack, taxi, taxicab" | "caldron, cauldron" | "candle, taper, wax light" | "cannon" | "canoe" | "can opener, tin opener" | "cardigan" | "car mirror" | "carousel, carrousel, merry-go-round, roundabout, whirligig" | "carpenter's kit, tool kit" | "carton" | "car wheel" | "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM" | "cassette" | "cassette player" | "castle" | "catamaran" | "CD player" | "cello, violoncello" | "cellular telephone, cellular phone, cellphone, cell, mobile phone" | "chain" | "chainlink fence" | "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour" | "chain saw, chainsaw" | "chest" | "chiffonier, commode" | "chime, bell, gong" | "china cabinet, china closet" | "Christmas stocking" | "church, church building" | "cinema, movie theater, movie theatre, movie house, picture palace" | "cleaver, meat cleaver, chopper" | "cliff dwelling" | "cloak" | "clog, geta, patten, sabot" | "cocktail shaker" | "coffee mug" | "coffeepot" | "coil, spiral, volute, whorl, helix" | "combination lock" | "computer keyboard, keypad" | "confectionery, confectionery store, candy store" | "container ship, containership, container vessel" | "convertible" | "corkscrew, bottle screw" | "cornet, horn, trumpet, trump" | "cowboy boot" | "cowboy hat, ten-gallon hat" | "cradle" | "crash helmet" | "crate" | "crib, cot" | "Crock Pot" | "croquet ball" | "crutch" | "cuirass" | "dam, dike, dyke" | "desk" | "desktop computer" | "dial telephone, dial phone" | "diaper, nappy, napkin" | "digital clock" | "digital watch" | "dining table, board" | "dishrag, dishcloth" | "dishwasher, dish washer, dishwashing machine" | "disk brake, disc brake" | "dock, dockage, docking facility" | "dogsled, dog sled, dog sleigh" | "dome" | "doormat, welcome mat" | "drilling platform, offshore rig" | "drum, membranophone, tympan" | "drumstick" | "dumbbell" | "Dutch oven" | "electric fan, blower" | "electric guitar" | "electric locomotive" | "entertainment center" | "envelope" | "espresso maker" | "face powder" | "feather boa, boa" | "file, file cabinet, filing cabinet" | "fireboat" | "fire engine, fire truck" | "fire screen, fireguard" | "flagpole, flagstaff" | "flute, transverse flute" | "folding chair" | "football helmet" | "forklift" | "fountain" | "fountain pen" | "four-poster" | "freight car" | "French horn, horn" | "frying pan, frypan, skillet" | "fur coat" | "garbage truck, dustcart" | "gasmask, respirator, gas helmet" | "gas pump, gasoline pump, petrol pump, island dispenser" | "goblet" | "go-kart" | "golf ball" | "golfcart, golf cart" | "gondola" | "gong, tam-tam" | "gown" | "grand piano, grand" | "greenhouse, nursery, glasshouse" | "grille, radiator grille" | "grocery store, grocery, food market, market" | "guillotine" | "hair slide" | "hair spray" | "half track" | "hammer" | "hamper" | "hand blower, blow dryer, blow drier, hair dryer, hair drier" | "hand-held computer, hand-held microcomputer" | "handkerchief, hankie, hanky, hankey" | "hard disc, hard disk, fixed disk" | "harmonica, mouth organ, harp, mouth harp" | "harp" | "harvester, reaper" | "hatchet" | "holster" | "home theater, home theatre" | "honeycomb" | "hook, claw" | "hoopskirt, crinoline" | "horizontal bar, high bar" | "horse cart, horse-cart" | "hourglass" | "iPod" | "iron, smoothing iron" | "jack-o'-lantern" | "jean, blue jean, denim" | "jeep, landrover" | "jersey, T-shirt, tee shirt" | "jigsaw puzzle" | "jinrikisha, ricksha, rickshaw" | "joystick" | "kimono" | "knee pad" | "knot" | "lab coat, laboratory coat" | "ladle" | "lampshade, lamp shade" | "laptop, laptop computer" | "lawn mower, mower" | "lens cap, lens cover" | "letter opener, paper knife, paperknife" | "library" | "lifeboat" | "lighter, light, igniter, ignitor" | "limousine, limo" | "liner, ocean liner" | "lipstick, lip rouge" | "Loafer" | "lotion" | "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system" | "loupe, jeweler's loupe" | "lumbermill, sawmill" | "magnetic compass" | "mailbag, postbag" | "mailbox, letter box" | "maillot" | "maillot, tank suit" | "manhole cover" | "maraca" | "marimba, xylophone" | "mask" | "matchstick" | "maypole" | "maze, labyrinth" | "measuring cup" | "medicine chest, medicine cabinet" | "megalith, megalithic structure" | "microphone, mike" | "microwave, microwave oven" | "military uniform" | "milk can" | "minibus" | "miniskirt, mini" | "minivan" | "missile" | "mitten" | "mixing bowl" | "mobile home, manufactured home" | "Model T" | "modem" | "monastery" | "monitor" | "moped" | "mortar" | "mortarboard" | "mosque" | "mosquito net" | "motor scooter, scooter" | "mountain bike, all-terrain bike, off-roader" | "mountain tent" | "mouse, computer mouse" | "mousetrap" | "moving van" | "muzzle" | "nail" | "neck brace" | "necklace" | "nipple" | "notebook, notebook computer" | "obelisk" | "oboe, hautboy, hautbois" | "ocarina, sweet potato" | "odometer, hodometer, mileometer, milometer" | "oil filter" | "organ, pipe organ" | "oscilloscope, scope, cathode-ray oscilloscope, CRO" | "overskirt" | "oxcart" | "oxygen mask" | "packet" | "paddle, boat paddle" | "paddlewheel, paddle wheel" | "padlock" | "paintbrush" | "pajama, pyjama, pj's, jammies" | "palace" | "panpipe, pandean pipe, syrinx" | "paper towel" | "parachute, chute" | "parallel bars, bars" | "park bench" | "parking meter" | "passenger car, coach, carriage" | "patio, terrace" | "pay-phone, pay-station" | "pedestal, plinth, footstall" | "pencil box, pencil case" | "pencil sharpener" | "perfume, essence" | "Petri dish" | "photocopier" | "pick, plectrum, plectron" | "pickelhaube" | "picket fence, paling" | "pickup, pickup truck" | "pier" | "piggy bank, penny bank" | "pill bottle" | "pillow" | "ping-pong ball" | "pinwheel" | "pirate, pirate ship" | "pitcher, ewer" | "plane, carpenter's plane, woodworking plane" | "planetarium" | "plastic bag" | "plate rack" | "plow, plough" | "plunger, plumber's helper" | "Polaroid camera, Polaroid Land camera" | "pole" | "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria" | "poncho" | "pool table, billiard table, snooker table" | "pop bottle, soda bottle" | "pot, flowerpot" | "potter's wheel" | "power drill" | "prayer rug, prayer mat" | "printer" | "prison, prison house" | "projectile, missile" | "projector" | "puck, hockey puck" | "punching bag, punch bag, punching ball, punchball" | "purse" | "quill, quill pen" | "quilt, comforter, comfort, puff" | "racer, race car, racing car" | "racket, racquet" | "radiator" | "radio, wireless" | "radio telescope, radio reflector" | "rain barrel" | "recreational vehicle, RV, R.V." | "reel" | "reflex camera" | "refrigerator, icebox" | "remote control, remote" | "restaurant, eating house, eating place, eatery" | "revolver, six-gun, six-shooter" | "rifle" | "rocking chair, rocker" | "rotisserie" | "rubber eraser, rubber, pencil eraser" | "rugby ball" | "rule, ruler" | "running shoe" | "safe" | "safety pin" | "saltshaker, salt shaker" | "sandal" | "sarong" | "sax, saxophone" | "scabbard" | "scale, weighing machine" | "school bus" | "schooner" | "scoreboard" | "screen, CRT screen" | "screw" | "screwdriver" | "seat belt, seatbelt" | "sewing machine" | "shield, buckler" | "shoe shop, shoe-shop, shoe store" | "shoji" | "shopping basket" | "shopping cart" | "shovel" | "shower cap" | "shower curtain" | "ski" | "ski mask" | "sleeping bag" | "slide rule, slipstick" | "sliding door" | "slot, one-armed bandit" | "snorkel" | "snowmobile" | "snowplow, snowplough" | "soap dispenser" | "soccer ball" | "sock" | "solar dish, solar collector, solar furnace" | "sombrero" | "soup bowl" | "space bar" | "space heater" | "space shuttle" | "spatula" | "speedboat" | "spider web, spider's web" | "spindle" | "sports car, sport car" | "spotlight, spot" | "stage" | "steam locomotive" | "steel arch bridge" | "steel drum" | "stethoscope" | "stole" | "stone wall" | "stopwatch, stop watch" | "stove" | "strainer" | "streetcar, tram, tramcar, trolley, trolley car" | "stretcher" | "studio couch, day bed" | "stupa, tope" | "submarine, pigboat, sub, U-boat" | "suit, suit of clothes" | "sundial" | "sunglass" | "sunglasses, dark glasses, shades" | "sunscreen, sunblock, sun blocker" | "suspension bridge" | "swab, swob, mop" | "sweatshirt" | "swimming trunks, bathing trunks" | "swing" | "switch, electric switch, electrical switch" | "syringe" | "table lamp" | "tank, army tank, armored combat vehicle, armoured combat vehicle" | "tape player" | "teapot" | "teddy, teddy bear" | "television, television system" | "tennis ball" | "thatch, thatched roof" | "theater curtain, theatre curtain" | "thimble" | "thresher, thrasher, threshing machine" | "throne" | "tile roof" | "toaster" | "tobacco shop, tobacconist shop, tobacconist" | "toilet seat" | "torch" | "totem pole" | "tow truck, tow car, wrecker" | "toyshop" | "tractor" | "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi" | "tray" | "trench coat" | "tricycle, trike, velocipede" | "trimaran" | "tripod" | "triumphal arch" | "trolleybus, trolley coach, trackless trolley" | "trombone" | "tub, vat" | "turnstile" | "typewriter keyboard" | "umbrella" | "unicycle, monocycle" | "upright, upright piano" | "vacuum, vacuum cleaner" | "vase" | "vault" | "velvet" | "vending machine" | "vestment" | "viaduct" | "violin, fiddle" | "volleyball" | "waffle iron" | "wall clock" | "wallet, billfold, notecase, pocketbook" | "wardrobe, closet, press" | "warplane, military plane" | "washbasin, handbasin, washbowl, lavabo, wash-hand basin" | "washer, automatic washer, washing machine" | "water bottle" | "water jug" | "water tower" | "whiskey jug" | "whistle" | "wig" | "window screen" | "window shade" | "Windsor tie" | "wine bottle" | "wing" | "wok" | "wooden spoon" | "wool, woolen, woollen" | "worm fence, snake fence, snake-rail fence, Virginia fence" | "wreck" | "yawl" | "yurt" | "web site, website, internet site, site" | "comic book" | "crossword puzzle, crossword" | "street sign" | "traffic light, traffic signal, stoplight" | "book jacket, dust cover, dust jacket, dust wrapper" | "menu" | "plate" | "guacamole" | "consomme" | "hot pot, hotpot" | "trifle" | "ice cream, icecream" | "ice lolly, lolly, lollipop, popsicle" | "French loaf" | "bagel, beigel" | "pretzel" | "cheeseburger" | "hotdog, hot dog, red hot" | "mashed potato" | "head cabbage" | "broccoli" | "cauliflower" | "zucchini, courgette" | "spaghetti squash" | "acorn squash" | "butternut squash" | "cucumber, cuke" | "artichoke, globe artichoke" | "bell pepper" | "cardoon" | "mushroom" | "Granny Smith" | "strawberry" | "orange" | "lemon" | "fig" | "pineapple, ananas" | "banana" | "jackfruit, jak, jack" | "custard apple" | "pomegranate" | "hay" | "carbonara" | "chocolate sauce, chocolate syrup" | "dough" | "meat loaf, meatloaf" | "pizza, pizza pie" | "potpie" | "burrito" | "red wine" | "espresso" | "cup" | "eggnog" | "alp" | "bubble" | "cliff, drop, drop-off" | "coral reef" | "geyser" | "lakeside, lakeside road, lakeshore" | "promontory, headland, head, foreland" | "sandbar, sand bar" | "seashore, coast, seacoast, sea-coast" | "valley, vale" | "volcano" | "ballplayer, baseball player" | "groom, bridegroom" | "scuba diver" | "rapeseed" | "daisy" | "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum" | "corn" | "acorn" | "hip, rose hip, rosehip" | "buckeye, horse chestnut, conker" | "coral fungus" | "agaric" | "gyromitra" | "stinkhorn, carrion fungus" | "earthstar" | "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa" | "bolete" | "ear, spike, capitulum" | "toilet tissue, toilet paper, bathroom tissue"> = EFFICIENTNET_V2_S_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: ClassifierModel<"tench, Tinca tinca" | "goldfish, Carassius auratus" | "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias" | "tiger shark, Galeocerdo cuvieri" | "hammerhead, hammerhead shark" | "electric ray, crampfish, numbfish, torpedo" | "stingray" | "cock" | "hen" | "ostrich, Struthio camelus" | "brambling, Fringilla montifringilla" | "goldfinch, Carduelis carduelis" | "house finch, linnet, Carpodacus mexicanus" | "junco, snowbird" | "indigo bunting, indigo finch, indigo bird, Passerina cyanea" | "robin, American robin, Turdus migratorius" | "bulbul" | "jay" | "magpie" | "chickadee" | "water ouzel, dipper" | "kite" | "bald eagle, American eagle, Haliaeetus leucocephalus" | "vulture" | "great grey owl, great gray owl, Strix nebulosa" | "European fire salamander, Salamandra salamandra" | "common newt, Triturus vulgaris" | "eft" | "spotted salamander, Ambystoma maculatum" | "axolotl, mud puppy, Ambystoma mexicanum" | "bullfrog, Rana catesbeiana" | "tree frog, tree-frog" | "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui" | "loggerhead, loggerhead turtle, Caretta caretta" | "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea" | "mud turtle" | "terrapin" | "box turtle, box tortoise" | "banded gecko" | "common iguana, iguana, Iguana iguana" | "American chameleon, anole, Anolis carolenensis" | "whiptail, whiptail lizard" | "agama" | "frilled lizard, Chlamydosaurus kingi" | "alligator lizard" | "Gila monster, Heloderma suspectum" | "green lizard, Lacerta viridis" | "African chameleon, Chamaeleo chamaeleon" | "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis" | "African crocodile, Nile crocodile, Crocodylus niloticus" | "American alligator, Alligator mississipiensis" | "triceratops" | "thunder snake, worm snake, Carphophis amoenus" | "ringneck snake, ring-necked snake, ring snake" | "hognose snake, puff adder, sand viper" | "green snake, grass snake" | "king snake, kingsnake" | "garter snake, grass snake" | "water snake" | "vine snake" | "night snake, Hypsiglena torquata" | "boa constrictor, Constrictor constrictor" | "rock python, rock snake, Python sebae" | "Indian cobra, Naja naja" | "green mamba" | "sea snake" | "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus" | "diamondback, diamondback rattlesnake, Crotalus adamanteus" | "sidewinder, horned rattlesnake, Crotalus cerastes" | "trilobite" | "harvestman, daddy longlegs, Phalangium opilio" | "scorpion" | "black and gold garden spider, Argiope aurantia" | "barn spider, Araneus cavaticus" | "garden spider, Aranea diademata" | "black widow, Latrodectus mactans" | "tarantula" | "wolf spider, hunting spider" | "tick" | "centipede" | "black grouse" | "ptarmigan" | "ruffed grouse, partridge, Bonasa umbellus" | "prairie chicken, prairie grouse, prairie fowl" | "peacock" | "quail" | "partridge" | "African grey, African gray, Psittacus erithacus" | "macaw" | "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita" | "lorikeet" | "coucal" | "bee eater" | "hornbill" | "hummingbird" | "jacamar" | "toucan" | "drake" | "red-breasted merganser, Mergus serrator" | "goose" | "black swan, Cygnus atratus" | "tusker" | "echidna, spiny anteater, anteater" | "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus" | "wallaby, brush kangaroo" | "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus" | "wombat" | "jellyfish" | "sea anemone, anemone" | "brain coral" | "flatworm, platyhelminth" | "nematode, nematode worm, roundworm" | "conch" | "snail" | "slug" | "sea slug, nudibranch" | "chiton, coat-of-mail shell, sea cradle, polyplacophore" | "chambered nautilus, pearly nautilus, nautilus" | "Dungeness crab, Cancer magister" | "rock crab, Cancer irroratus" | "fiddler crab" | "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica" | "American lobster, Northern lobster, Maine lobster, Homarus americanus" | "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish" | "crayfish, crawfish, crawdad, crawdaddy" | "hermit crab" | "isopod" | "white stork, Ciconia ciconia" | "black stork, Ciconia nigra" | "spoonbill" | "flamingo" | "little blue heron, Egretta caerulea" | "American egret, great white heron, Egretta albus" | "bittern" | "crane" | "limpkin, Aramus pictus" | "European gallinule, Porphyrio porphyrio" | "American coot, marsh hen, mud hen, water hen, Fulica americana" | "bustard" | "ruddy turnstone, Arenaria interpres" | "red-backed sandpiper, dunlin, Erolia alpina" | "redshank, Tringa totanus" | "dowitcher" | "oystercatcher, oyster catcher" | "pelican" | "king penguin, Aptenodytes patagonica" | "albatross, mollymawk" | "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus" | "killer whale, killer, orca, grampus, sea wolf, Orcinus orca" | "dugong, Dugong dugon" | "sea lion" | "Chihuahua" | "Japanese spaniel" | "Maltese dog, Maltese terrier, Maltese" | "Pekinese, Pekingese, Peke" | "Shih-Tzu" | "Blenheim spaniel" | "papillon" | "toy terrier" | "Rhodesian ridgeback" | "Afghan hound, Afghan" | "basset, basset hound" | "beagle" | "bloodhound, sleuthhound" | "bluetick" | "black-and-tan coonhound" | "Walker hound, Walker foxhound" | "English foxhound" | "redbone" | "borzoi, Russian wolfhound" | "Irish wolfhound" | "Italian greyhound" | "whippet" | "Ibizan hound, Ibizan Podenco" | "Norwegian elkhound, elkhound" | "otterhound, otter hound" | "Saluki, gazelle hound" | "Scottish deerhound, deerhound" | "Weimaraner" | "Staffordshire bullterrier, Staffordshire bull terrier" | "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier" | "Bedlington terrier" | "Border terrier" | "Kerry blue terrier" | "Irish terrier" | "Norfolk terrier" | "Norwich terrier" | "Yorkshire terrier" | "wire-haired fox terrier" | "Lakeland terrier" | "Sealyham terrier, Sealyham" | "Airedale, Airedale terrier" | "cairn, cairn terrier" | "Australian terrier" | "Dandie Dinmont, Dandie Dinmont terrier" | "Boston bull, Boston terrier" | "miniature schnauzer" | "giant schnauzer" | "standard schnauzer" | "Scotch terrier, Scottish terrier, Scottie" | "Tibetan terrier, chrysanthemum dog" | "silky terrier, Sydney silky" | "soft-coated wheaten terrier" | "West Highland white terrier" | "Lhasa, Lhasa apso" | "flat-coated retriever" | "curly-coated retriever" | "golden retriever" | "Labrador retriever" | "Chesapeake Bay retriever" | "German short-haired pointer" | "vizsla, Hungarian pointer" | "English setter" | "Irish setter, red setter" | "Gordon setter" | "Brittany spaniel" | "clumber, clumber spaniel" | "English springer, English springer spaniel" | "Welsh springer spaniel" | "cocker spaniel, English cocker spaniel, cocker" | "Sussex spaniel" | "Irish water spaniel" | "kuvasz" | "schipperke" | "groenendael" | "malinois" | "briard" | "kelpie" | "komondor" | "Old English sheepdog, bobtail" | "Shetland sheepdog, Shetland sheep dog, Shetland" | "collie" | "Border collie" | "Bouvier des Flandres, Bouviers des Flandres" | "Rottweiler" | "German shepherd, German shepherd dog, German police dog, alsatian" | "Doberman, Doberman pinscher" | "miniature pinscher" | "Greater Swiss Mountain dog" | "Bernese mountain dog" | "Appenzeller" | "EntleBucher" | "boxer" | "bull mastiff" | "Tibetan mastiff" | "French bulldog" | "Great Dane" | "Saint Bernard, St Bernard" | "Eskimo dog, husky" | "malamute, malemute, Alaskan malamute" | "Siberian husky" | "dalmatian, coach dog, carriage dog" | "affenpinscher, monkey pinscher, monkey dog" | "basenji" | "pug, pug-dog" | "Leonberg" | "Newfoundland, Newfoundland dog" | "Great Pyrenees" | "Samoyed, Samoyede" | "Pomeranian" | "chow, chow chow" | "keeshond" | "Brabancon griffon" | "Pembroke, Pembroke Welsh corgi" | "Cardigan, Cardigan Welsh corgi" | "toy poodle" | "miniature poodle" | "standard poodle" | "Mexican hairless" | "timber wolf, grey wolf, gray wolf, Canis lupus" | "white wolf, Arctic wolf, Canis lupus tundrarum" | "red wolf, maned wolf, Canis rufus, Canis niger" | "coyote, prairie wolf, brush wolf, Canis latrans" | "dingo, warrigal, warragal, Canis dingo" | "dhole, Cuon alpinus" | "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus" | "hyena, hyaena" | "red fox, Vulpes vulpes" | "kit fox, Vulpes macrotis" | "Arctic fox, white fox, Alopex lagopus" | "grey fox, gray fox, Urocyon cinereoargenteus" | "tabby, tabby cat" | "tiger cat" | "Persian cat" | "Siamese cat, Siamese" | "Egyptian cat" | "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor" | "lynx, catamount" | "leopard, Panthera pardus" | "snow leopard, ounce, Panthera uncia" | "jaguar, panther, Panthera onca, Felis onca" | "lion, king of beasts, Panthera leo" | "tiger, Panthera tigris" | "cheetah, chetah, Acinonyx jubatus" | "brown bear, bruin, Ursus arctos" | "American black bear, black bear, Ursus americanus, Euarctos americanus" | "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus" | "sloth bear, Melursus ursinus, Ursus ursinus" | "mongoose" | "meerkat, mierkat" | "tiger beetle" | "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle" | "ground beetle, carabid beetle" | "long-horned beetle, longicorn, longicorn beetle" | "leaf beetle, chrysomelid" | "dung beetle" | "rhinoceros beetle" | "weevil" | "fly" | "bee" | "ant, emmet, pismire" | "grasshopper, hopper" | "cricket" | "walking stick, walkingstick, stick insect" | "cockroach, roach" | "focused mantis, mantid" | "cicada, cicala" | "leafhopper" | "lacewing, lacewing fly" | "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk" | "damselfly" | "admiral" | "ringlet, ringlet butterfly" | "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus" | "cabbage butterfly" | "sulphur butterfly, sulfur butterfly" | "lycaenid, lycaenid butterfly" | "starfish, sea star" | "sea urchin" | "sea cucumber, holothurian" | "wood rabbit, cottontail, cottontail rabbit" | "hare" | "Angora, Angora rabbit" | "hamster" | "porcupine, hedgehog" | "fox squirrel, eastern fox squirrel, Sciurus niger" | "marmot" | "beaver" | "guinea pig, Cavia cobaya" | "sorrel" | "zebra" | "hog, pig, grunter, squealer, Sus scrofa" | "wild boar, boar, Sus scrofa" | "warthog" | "hippopotamus, hippo, river horse, Hippopotamus amphibius" | "ox" | "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis" | "bison" | "ram, tup" | "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis" | "ibex, Capra ibex" | "hartebeest" | "impala, Aepyceros melampus" | "gazelle" | "Arabian camel, dromedary, Camelus dromedarius" | "llama" | "weasel" | "mink" | "polecat, fitch, foulmart, foumart, Mustela putorius" | "black-footed ferret, ferret, Mustela nigripes" | "otter" | "skunk, polecat, wood pussy" | "badger" | "armadillo" | "three-toed sloth, ai, Bradypus tridactylus" | "orangutan, orang, orangutang, Pongo pygmaeus" | "gorilla, Gorilla gorilla" | "chimpanzee, chimp, Pan troglodytes" | "gibbon, Hylobates lar" | "siamang, Hylobates syndactylus, Symphalangus syndactylus" | "guenon, guenon monkey" | "patas, hussar monkey, Erythrocebus patas" | "baboon" | "macaque" | "langur" | "colobus, colobus monkey" | "proboscis monkey, Nasalis larvatus" | "marmoset" | "capuchin, ringtail, Cebus capucinus" | "howler monkey, howler" | "titi, titi monkey" | "spider monkey, Ateles geoffroyi" | "squirrel monkey, Saimiri sciureus" | "Madagascar cat, ring-tailed lemur, Lemur catta" | "indri, indris, Indri indri, Indri brevicaudatus" | "Indian elephant, Elephas maximus" | "African elephant, Loxodonta africana" | "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens" | "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca" | "barracouta, snoek" | "raw eel, eel" | "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch" | "rock beauty, Holocanthus tricolor" | "anemone fish" | "sturgeon" | "gar, garfish, garpike, billfish, Lepisosteus osseus" | "lionfish" | "puffer, pufferfish, blowfish, globefish" | "abacus" | "abaya" | "academic gown, academic robe, judge's robe" | "accordion, piano accordion, squeeze box" | "acoustic guitar" | "aircraft carrier, carrier, flattop, attack aircraft carrier" | "airliner" | "airship, dirigible" | "altar" | "ambulance" | "amphibian, amphibious vehicle" | "analog clock" | "apiary, bee house" | "apron" | "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin" | "assault rifle, assault gun" | "backpack, back pack, knapsack, packsack, rucksack, haversack" | "bakery, bakeshop, bakehouse" | "balance beam, beam" | "balloon" | "ballpoint, ballpoint pen, ballpen, Biro" | "Band Aid" | "banjo" | "bannister, banister, balustrade, balusters, handrail" | "barbell" | "barber chair" | "barbershop" | "barn" | "barometer" | "barrel, cask" | "barrow, garden cart, lawn cart, wheelbarrow" | "baseball" | "basketball" | "bassinet" | "bassoon" | "bathing cap, swimming cap" | "bath towel" | "bathtub, bathing tub, bath, tub" | "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon" | "beacon, lighthouse, beacon light, pharos" | "beaker" | "bearskin, busby, shako" | "beer bottle" | "beer glass" | "bell cote, bell cot" | "bib" | "bicycle-built-for-two, tandem bicycle, tandem" | "bikini, two-piece" | "binder, ring-binder" | "binoculars, field glasses, opera glasses" | "birdhouse" | "boathouse" | "bobsled, bobsleigh, bob" | "bolo tie, bolo, bola tie, bola" | "bonnet, poke bonnet" | "bookcase" | "bookshop, bookstore, bookstall" | "bottlecap" | "bow" | "bow tie, bow-tie, bowtie" | "brass, memorial tablet, plaque" | "brassiere, bra, bandeau" | "breakwater, groin, groyne, mole, bulwark, seawall, jetty" | "breastplate, aegis, egis" | "broom" | "bucket, pail" | "buckle" | "bulletproof vest" | "bullet train, bullet" | "butcher shop, meat market" | "cab, hack, taxi, taxicab" | "caldron, cauldron" | "candle, taper, wax light" | "cannon" | "canoe" | "can opener, tin opener" | "cardigan" | "car mirror" | "carousel, carrousel, merry-go-round, roundabout, whirligig" | "carpenter's kit, tool kit" | "carton" | "car wheel" | "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM" | "cassette" | "cassette player" | "castle" | "catamaran" | "CD player" | "cello, violoncello" | "cellular telephone, cellular phone, cellphone, cell, mobile phone" | "chain" | "chainlink fence" | "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour" | "chain saw, chainsaw" | "chest" | "chiffonier, commode" | "chime, bell, gong" | "china cabinet, china closet" | "Christmas stocking" | "church, church building" | "cinema, movie theater, movie theatre, movie house, picture palace" | "cleaver, meat cleaver, chopper" | "cliff dwelling" | "cloak" | "clog, geta, patten, sabot" | "cocktail shaker" | "coffee mug" | "coffeepot" | "coil, spiral, volute, whorl, helix" | "combination lock" | "computer keyboard, keypad" | "confectionery, confectionery store, candy store" | "container ship, containership, container vessel" | "convertible" | "corkscrew, bottle screw" | "cornet, horn, trumpet, trump" | "cowboy boot" | "cowboy hat, ten-gallon hat" | "cradle" | "crash helmet" | "crate" | "crib, cot" | "Crock Pot" | "croquet ball" | "crutch" | "cuirass" | "dam, dike, dyke" | "desk" | "desktop computer" | "dial telephone, dial phone" | "diaper, nappy, napkin" | "digital clock" | "digital watch" | "dining table, board" | "dishrag, dishcloth" | "dishwasher, dish washer, dishwashing machine" | "disk brake, disc brake" | "dock, dockage, docking facility" | "dogsled, dog sled, dog sleigh" | "dome" | "doormat, welcome mat" | "drilling platform, offshore rig" | "drum, membranophone, tympan" | "drumstick" | "dumbbell" | "Dutch oven" | "electric fan, blower" | "electric guitar" | "electric locomotive" | "entertainment center" | "envelope" | "espresso maker" | "face powder" | "feather boa, boa" | "file, file cabinet, filing cabinet" | "fireboat" | "fire engine, fire truck" | "fire screen, fireguard" | "flagpole, flagstaff" | "flute, transverse flute" | "folding chair" | "football helmet" | "forklift" | "fountain" | "fountain pen" | "four-poster" | "freight car" | "French horn, horn" | "frying pan, frypan, skillet" | "fur coat" | "garbage truck, dustcart" | "gasmask, respirator, gas helmet" | "gas pump, gasoline pump, petrol pump, island dispenser" | "goblet" | "go-kart" | "golf ball" | "golfcart, golf cart" | "gondola" | "gong, tam-tam" | "gown" | "grand piano, grand" | "greenhouse, nursery, glasshouse" | "grille, radiator grille" | "grocery store, grocery, food market, market" | "guillotine" | "hair slide" | "hair spray" | "half track" | "hammer" | "hamper" | "hand blower, blow dryer, blow drier, hair dryer, hair drier" | "hand-held computer, hand-held microcomputer" | "handkerchief, hankie, hanky, hankey" | "hard disc, hard disk, fixed disk" | "harmonica, mouth organ, harp, mouth harp" | "harp" | "harvester, reaper" | "hatchet" | "holster" | "home theater, home theatre" | "honeycomb" | "hook, claw" | "hoopskirt, crinoline" | "horizontal bar, high bar" | "horse cart, horse-cart" | "hourglass" | "iPod" | "iron, smoothing iron" | "jack-o'-lantern" | "jean, blue jean, denim" | "jeep, landrover" | "jersey, T-shirt, tee shirt" | "jigsaw puzzle" | "jinrikisha, ricksha, rickshaw" | "joystick" | "kimono" | "knee pad" | "knot" | "lab coat, laboratory coat" | "ladle" | "lampshade, lamp shade" | "laptop, laptop computer" | "lawn mower, mower" | "lens cap, lens cover" | "letter opener, paper knife, paperknife" | "library" | "lifeboat" | "lighter, light, igniter, ignitor" | "limousine, limo" | "liner, ocean liner" | "lipstick, lip rouge" | "Loafer" | "lotion" | "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system" | "loupe, jeweler's loupe" | "lumbermill, sawmill" | "magnetic compass" | "mailbag, postbag" | "mailbox, letter box" | "maillot" | "maillot, tank suit" | "manhole cover" | "maraca" | "marimba, xylophone" | "mask" | "matchstick" | "maypole" | "maze, labyrinth" | "measuring cup" | "medicine chest, medicine cabinet" | "megalith, megalithic structure" | "microphone, mike" | "microwave, microwave oven" | "military uniform" | "milk can" | "minibus" | "miniskirt, mini" | "minivan" | "missile" | "mitten" | "mixing bowl" | "mobile home, manufactured home" | "Model T" | "modem" | "monastery" | "monitor" | "moped" | "mortar" | "mortarboard" | "mosque" | "mosquito net" | "motor scooter, scooter" | "mountain bike, all-terrain bike, off-roader" | "mountain tent" | "mouse, computer mouse" | "mousetrap" | "moving van" | "muzzle" | "nail" | "neck brace" | "necklace" | "nipple" | "notebook, notebook computer" | "obelisk" | "oboe, hautboy, hautbois" | "ocarina, sweet potato" | "odometer, hodometer, mileometer, milometer" | "oil filter" | "organ, pipe organ" | "oscilloscope, scope, cathode-ray oscilloscope, CRO" | "overskirt" | "oxcart" | "oxygen mask" | "packet" | "paddle, boat paddle" | "paddlewheel, paddle wheel" | "padlock" | "paintbrush" | "pajama, pyjama, pj's, jammies" | "palace" | "panpipe, pandean pipe, syrinx" | "paper towel" | "parachute, chute" | "parallel bars, bars" | "park bench" | "parking meter" | "passenger car, coach, carriage" | "patio, terrace" | "pay-phone, pay-station" | "pedestal, plinth, footstall" | "pencil box, pencil case" | "pencil sharpener" | "perfume, essence" | "Petri dish" | "photocopier" | "pick, plectrum, plectron" | "pickelhaube" | "picket fence, paling" | "pickup, pickup truck" | "pier" | "piggy bank, penny bank" | "pill bottle" | "pillow" | "ping-pong ball" | "pinwheel" | "pirate, pirate ship" | "pitcher, ewer" | "plane, carpenter's plane, woodworking plane" | "planetarium" | "plastic bag" | "plate rack" | "plow, plough" | "plunger, plumber's helper" | "Polaroid camera, Polaroid Land camera" | "pole" | "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria" | "poncho" | "pool table, billiard table, snooker table" | "pop bottle, soda bottle" | "pot, flowerpot" | "potter's wheel" | "power drill" | "prayer rug, prayer mat" | "printer" | "prison, prison house" | "projectile, missile" | "projector" | "puck, hockey puck" | "punching bag, punch bag, punching ball, punchball" | "purse" | "quill, quill pen" | "quilt, comforter, comfort, puff" | "racer, race car, racing car" | "racket, racquet" | "radiator" | "radio, wireless" | "radio telescope, radio reflector" | "rain barrel" | "recreational vehicle, RV, R.V." | "reel" | "reflex camera" | "refrigerator, icebox" | "remote control, remote" | "restaurant, eating house, eating place, eatery" | "revolver, six-gun, six-shooter" | "rifle" | "rocking chair, rocker" | "rotisserie" | "rubber eraser, rubber, pencil eraser" | "rugby ball" | "rule, ruler" | "running shoe" | "safe" | "safety pin" | "saltshaker, salt shaker" | "sandal" | "sarong" | "sax, saxophone" | "scabbard" | "scale, weighing machine" | "school bus" | "schooner" | "scoreboard" | "screen, CRT screen" | "screw" | "screwdriver" | "seat belt, seatbelt" | "sewing machine" | "shield, buckler" | "shoe shop, shoe-shop, shoe store" | "shoji" | "shopping basket" | "shopping cart" | "shovel" | "shower cap" | "shower curtain" | "ski" | "ski mask" | "sleeping bag" | "slide rule, slipstick" | "sliding door" | "slot, one-armed bandit" | "snorkel" | "snowmobile" | "snowplow, snowplough" | "soap dispenser" | "soccer ball" | "sock" | "solar dish, solar collector, solar furnace" | "sombrero" | "soup bowl" | "space bar" | "space heater" | "space shuttle" | "spatula" | "speedboat" | "spider web, spider's web" | "spindle" | "sports car, sport car" | "spotlight, spot" | "stage" | "steam locomotive" | "steel arch bridge" | "steel drum" | "stethoscope" | "stole" | "stone wall" | "stopwatch, stop watch" | "stove" | "strainer" | "streetcar, tram, tramcar, trolley, trolley car" | "stretcher" | "studio couch, day bed" | "stupa, tope" | "submarine, pigboat, sub, U-boat" | "suit, suit of clothes" | "sundial" | "sunglass" | "sunglasses, dark glasses, shades" | "sunscreen, sunblock, sun blocker" | "suspension bridge" | "swab, swob, mop" | "sweatshirt" | "swimming trunks, bathing trunks" | "swing" | "switch, electric switch, electrical switch" | "syringe" | "table lamp" | "tank, army tank, armored combat vehicle, armoured combat vehicle" | "tape player" | "teapot" | "teddy, teddy bear" | "television, television system" | "tennis ball" | "thatch, thatched roof" | "theater curtain, theatre curtain" | "thimble" | "thresher, thrasher, threshing machine" | "throne" | "tile roof" | "toaster" | "tobacco shop, tobacconist shop, tobacconist" | "toilet seat" | "torch" | "totem pole" | "tow truck, tow car, wrecker" | "toyshop" | "tractor" | "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi" | "tray" | "trench coat" | "tricycle, trike, velocipede" | "trimaran" | "tripod" | "triumphal arch" | "trolleybus, trolley coach, trackless trolley" | "trombone" | "tub, vat" | "turnstile" | "typewriter keyboard" | "umbrella" | "unicycle, monocycle" | "upright, upright piano" | "vacuum, vacuum cleaner" | "vase" | "vault" | "velvet" | "vending machine" | "vestment" | "viaduct" | "violin, fiddle" | "volleyball" | "waffle iron" | "wall clock" | "wallet, billfold, notecase, pocketbook" | "wardrobe, closet, press" | "warplane, military plane" | "washbasin, handbasin, washbowl, lavabo, wash-hand basin" | "washer, automatic washer, washing machine" | "water bottle" | "water jug" | "water tower" | "whiskey jug" | "whistle" | "wig" | "window screen" | "window shade" | "Windsor tie" | "wine bottle" | "wing" | "wok" | "wooden spoon" | "wool, woolen, woollen" | "worm fence, snake fence, snake-rail fence, Virginia fence" | "wreck" | "yawl" | "yurt" | "web site, website, internet site, site" | "comic book" | "crossword puzzle, crossword" | "street sign" | "traffic light, traffic signal, stoplight" | "book jacket, dust cover, dust jacket, dust wrapper" | "menu" | "plate" | "guacamole" | "consomme" | "hot pot, hotpot" | "trifle" | "ice cream, icecream" | "ice lolly, lolly, lollipop, popsicle" | "French loaf" | "bagel, beigel" | "pretzel" | "cheeseburger" | "hotdog, hot dog, red hot" | "mashed potato" | "head cabbage" | "broccoli" | "cauliflower" | "zucchini, courgette" | "spaghetti squash" | "acorn squash" | "butternut squash" | "cucumber, cuke" | "artichoke, globe artichoke" | "bell pepper" | "cardoon" | "mushroom" | "Granny Smith" | "strawberry" | "orange" | "lemon" | "fig" | "pineapple, ananas" | "banana" | "jackfruit, jak, jack" | "custard apple" | "pomegranate" | "hay" | "carbonara" | "chocolate sauce, chocolate syrup" | "dough" | "meat loaf, meatloaf" | "pizza, pizza pie" | "potpie" | "burrito" | "red wine" | "espresso" | "cup" | "eggnog" | "alp" | "bubble" | "cliff, drop, drop-off" | "coral reef" | "geyser" | "lakeside, lakeside road, lakeshore" | "promontory, headland, head, foreland" | "sandbar, sand bar" | "seashore, coast, seacoast, sea-coast" | "valley, vale" | "volcano" | "ballplayer, baseball player" | "groom, bridegroom" | "scuba diver" | "rapeseed" | "daisy" | "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum" | "corn" | "acorn" | "hip, rose hip, rosehip" | "buckeye, horse chestnut, conker" | "coral fungus" | "agaric" | "gyromitra" | "stinkhorn, carrion fungus" | "earthstar" | "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa" | "bolete" | "ear, spike, capitulum" | "toilet tissue, toilet paper, bathroom tissue">

imageEmbeddings

imageEmbeddings: object

Image feature extraction and vision embedding models.

imageEmbeddings.CLIP_VIT_BASE_PATCH32

CLIP_VIT_BASE_PATCH32: object & object

CLIP vision encoder (ViT-B/32) mapping images into a 512-dimensional shared text-image space. Used for zero-shot visual classification and cross-modal image search.

Type Declaration

COREML_FP16

COREML_FP16: ImageEmbedderModel = CLIP_VIT_BASE_PATCH32_IMAGE_COREML_FP16

MLX_INT8

MLX_INT8: ImageEmbedderModel = CLIP_VIT_BASE_PATCH32_IMAGE_MLX_INT8

VULKAN_FP16

VULKAN_FP16: ImageEmbedderModel = CLIP_VIT_BASE_PATCH32_IMAGE_VULKAN_FP16

XNNPACK_FP32

XNNPACK_FP32: ImageEmbedderModel = CLIP_VIT_BASE_PATCH32_IMAGE_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ImageEmbedderModel

instanceSegmentation

instanceSegmentation: object

Instance segmentation models predicting both bounding boxes and fine-grained pixel masks per object instance.

instanceSegmentation.FASTSAM

FASTSAM: object

Fast Segment Anything Model (FastSAM) for promptable or global object instance mask segmentation. Available in Small (S) and Extra Large (X) variants.

instanceSegmentation.FASTSAM.S

S: object & object

FastSAM Small - lightweight instance segmenter for mobile.

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "object"> = FASTSAM_S_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "object"> = FASTSAM_S_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "object">

instanceSegmentation.FASTSAM.X

X: object & object

FastSAM Extra Large - high-accuracy instance segmenter.

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "object"> = FASTSAM_X_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "object"> = FASTSAM_X_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "object">

instanceSegmentation.RFDETR_NANO

RFDETR_NANO: object & object

RF-DETR (Roboflow Detection Transformer) Nano instance segmentation model predicting COCO class masks and bounding boxes (see COCO_CLASSES).

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = RFDETR_NANO_SEG_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = RFDETR_NANO_SEG_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

instanceSegmentation.YOLO26

YOLO26: object & object

YOLO26 instance segmentation models predicting COCO class instance masks and bounding boxes (see COCO_CLASSES_YOLO). Available across multiple sizes (NANO, SMALL, MEDIUM, LARGE, XLARGE) and resolutions (384x384, 512x512, 640x640).

Type Declaration

LARGE

LARGE: object & object

Large scale YOLO26 instance segmentation model. High accuracy instance segmentation variant for demanding visual pipelines.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_SEG_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_SEG_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_SEG_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_SEG_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_SEG_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_SEG_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

MEDIUM

MEDIUM: object & object

Medium scale YOLO26 instance segmentation model. Higher mask boundary precision for complex multi-object scenes.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_SEG_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_SEG_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_SEG_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_SEG_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_SEG_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_SEG_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

NANO

NANO: object & object

Nano scale YOLO26 instance segmentation model. High speed, ultra low latency mask generation.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_SEG_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_SEG_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_SEG_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_SEG_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_SEG_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_SEG_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SMALL

SMALL: object & object

Small scale YOLO26 instance segmentation model. Balanced latency and mask accuracy.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_SEG_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_SEG_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_SEG_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_SEG_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_SEG_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_SEG_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

XLARGE

XLARGE: object & object

Extra Large scale YOLO26 instance segmentation model. Maximum instance segmentation and mask delineation performance.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_SEG_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_SEG_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_SEG_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_SEG_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_SEG_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_SEG_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: InstanceSegmenterModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

keypointDetection

keypointDetection: object

Keypoint and pose detection models that estimate facial landmarks or human body skeletal keypoints.

keypointDetection.BLAZEFACE

BLAZEFACE: object & object

MediaPipe BlazeFace lightweight face detection and 6-point facial landmark locator (eyes, nose, mouth, ears, see BLAZEFACE_LANDMARKS).

Type Declaration

XNNPACK_FP32

XNNPACK_FP32: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "noseTip" | "mouthCenter" | "leftEar" | "rightEar"> = BLAZEFACE_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "noseTip" | "mouthCenter" | "leftEar" | "rightEar">

keypointDetection.RFDETR_KEYPOINT

RFDETR_KEYPOINT: object & object

RF-DETR (Roboflow Detection Transformer) pose keypoint detector predicting 17 COCO body keypoints (see COCO_LANDMARKS).

Type Declaration

COREML_FP16

COREML_FP16: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = RFDETR_KEYPOINT_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = RFDETR_KEYPOINT_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle">

keypointDetection.YOLO26_POSE

YOLO26_POSE: object & object

YOLO26 human pose estimation model predicting 17 COCO body keypoints (see COCO_LANDMARKS). Available across 384x384, 512x512, and 640x640 resolutions.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = YOLO26_POSE_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = YOLO26_POSE_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = YOLO26_POSE_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = YOLO26_POSE_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = YOLO26_POSE_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle"> = YOLO26_POSE_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle">

Type Declaration

DEFAULT

readonly DEFAULT: KeypointDetectorModel<"xyxy", "leftEye" | "rightEye" | "leftEar" | "rightEar" | "nose" | "leftShoulder" | "rightShoulder" | "leftElbow" | "rightElbow" | "leftWrist" | "rightWrist" | "leftHip" | "rightHip" | "leftKnee" | "rightKnee" | "leftAnkle" | "rightAnkle">

llm

llm: object

Generative Large Language Models (LLMs) for instruction following, chat, text generation, and reasoning.

llm.BIELIK_V3_1_5B

BIELIK_V3_1_5B: object & object

Bielik v3 1.5B bilingual Polish & English language model, developed by SpeakLeash. Fine-tuned on curated Polish corpora and instruction datasets for native Polish cultural nuance, grammar accuracy, idioms, and high-fidelity bidirectional Polish-English translation.

Type Declaration

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = BIELIK_V3_1_5B_XNNPACK_8DA4W

XNNPACK_FP16

XNNPACK_FP16: LLMModel = BIELIK_V3_1_5B_XNNPACK_FP16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.GEMMA4_E2B

GEMMA4_E2B: object & object

Google Gemma 4 E2B generative language model. Built on Google's Gemini research and architecture innovations, offering high-fidelity instruction following, creative text generation, and reasoning efficiency optimized for mobile deployment.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = GEMMA4_E2B_MLX_INT4

VULKAN_8DA4W

VULKAN_8DA4W: LLMModel = GEMMA4_E2B_VULKAN_8DA4W

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = GEMMA4_E2B_XNNPACK_8DA4W

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.HAMMER2_1_0_5B

HAMMER2_1_0_5B: object & object

Hammer 2.1 0.5B specialized function-calling model. Fine-tuned specifically for agentic tool use, structured JSON extraction, and single/multi-tool invocation with ultra-low latency for real-time mobile tool calling flows.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = HAMMER2_1_0_5B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = HAMMER2_1_0_5B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = HAMMER2_1_0_5B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.HAMMER2_1_1_5B

HAMMER2_1_1_5B: object & object

Hammer 2.1 1.5B function-calling language model. Optimized for multi-tool agentic workflows, API parameter schema validation, and structured JSON output generation on edge devices.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = HAMMER2_1_1_5B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = HAMMER2_1_1_5B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = HAMMER2_1_1_5B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.HAMMER2_1_3B

HAMMER2_1_3B: object & object

Hammer 2.1 3B high-capacity function-calling model. Provides top-tier tool selection precision, multi-turn tool calling, error recovery, and strict compliance with complex TypeScript/JSON schema specifications in autonomous mobile agent pipelines.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = HAMMER2_1_3B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = HAMMER2_1_3B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = HAMMER2_1_3B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.LFM2_5_1_2B

LFM2_5_1_2B: object & object

Liquid AI LFM 2.5 1.2B general-purpose hybrid language model. Built on the Liquid Foundation Model architecture for low memory bandwidth usage, and high-throughput token generation. Delivers strong general-purpose reasoning, instruction following, and fast multi-turn conversational chat on mobile devices.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = LFM2_5_1_2B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = LFM2_5_1_2B_XNNPACK_8DA4W

XNNPACK_FP16

XNNPACK_FP16: LLMModel = LFM2_5_1_2B_XNNPACK_FP16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.LFM2_5_350M

LFM2_5_350M: object & object

Liquid AI LFM 2.5 350M ultra-compact hybrid language model. Optimized for minimal memory footprint and sub-second first-token response times. Ideal for lightweight text completion, fast intent classification, query routing, and low-latency chat on resource-constrained edge hardware.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = LFM2_5_350M_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = LFM2_5_350M_XNNPACK_8DA4W

XNNPACK_FP16

XNNPACK_FP16: LLMModel = LFM2_5_350M_XNNPACK_FP16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.LFM2_5_VL_1_6B

LFM2_5_VL_1_6B: object & object

Liquid AI LFM 2.5 VL 1.6B high-capacity vision-language model. Provides fine-grained visual scene understanding, document/chart interpretation, detailed image captioning, and multi-turn visual dialogue with higher precision and reasoning fidelity than the 450M variant.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = LFM2_5_VL_1_6B_MLX_INT4

MLX_INT8

MLX_INT8: LLMModel = LFM2_5_VL_1_6B_MLX_INT8

VULKAN_8DA4W

VULKAN_8DA4W: LLMModel = LFM2_5_VL_1_6B_VULKAN_8DA4W

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = LFM2_5_VL_1_6B_XNNPACK_8DA4W

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.LFM2_5_VL_450M

LFM2_5_VL_450M: object & object

Liquid AI LFM 2.5 VL 450M lightweight multimodal vision-language model. Combines Liquid hybrid language modeling with visual token embeddings for real-time on-device visual question answering (VQA), image description, UI element inspection, and low-latency multimodal conversational agents.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = LFM2_5_VL_450M_MLX_INT4

VULKAN_8DA4W

VULKAN_8DA4W: LLMModel = LFM2_5_VL_450M_VULKAN_8DA4W

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = LFM2_5_VL_450M_XNNPACK_8DA4W

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.LLAMA3_2_1B

LLAMA3_2_1B: object & object

Meta Llama 3.2 1B lightweight instruction-tuned multilingual model. Features Grouped-Query Attention (GQA) and SpinQuant quantization for compact memory utilization and high throughput. Well suited for on-device text summarization, prompt rewriting, and lightweight conversational assistance.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = LLAMA3_2_1B_MLX_INT4

XNNPACK_BF16

XNNPACK_BF16: LLMModel = LLAMA3_2_1B_BF16

XNNPACK_SPINQUANT

XNNPACK_SPINQUANT: LLMModel = LLAMA3_2_1B_SPINQUANT

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.LLAMA3_2_3B

LLAMA3_2_3B: object & object

Meta Llama 3.2 3B instruction-tuned multilingual language model. Delivers strong instruction adherence, multi-turn reasoning, and high-quality content creation across 8+ core languages while maintaining a compact on-device memory profile.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = LLAMA3_2_3B_MLX_INT4

XNNPACK_BF16

XNNPACK_BF16: LLMModel = LLAMA3_2_3B_BF16

XNNPACK_SPINQUANT

XNNPACK_SPINQUANT: LLMModel = LLAMA3_2_3B_SPINQUANT

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.PHI4_MINI

PHI4_MINI: object & object

Microsoft Phi-4 Mini 3.8B high-density reasoning model. Trained on synthetic textbook-grade datasets for state-of-the-art on-device STEM problem solving, complex mathematical reasoning, multi-step code synthesis, and structured analytical tasks.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = PHI4_MINI_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = PHI4_MINI_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = PHI4_MINI_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.QWEN2_5_0_5B

QWEN2_5_0_5B: object & object

Alibaba Qwen 2.5 0.5B ultra-lightweight multilingual model. Trained on 18T tokens supporting 29+ languages; optimized for near-instant response times, basic instruction following, multilingual translation, and lightweight conversational assistants on mobile devices.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = QWEN2_5_0_5B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = QWEN2_5_0_5B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = QWEN2_5_0_5B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.QWEN2_5_1_5B

QWEN2_5_1_5B: object & object

Alibaba Qwen 2.5 1.5B multilingual instruction model. Combines broad multilingual comprehension across 29+ languages with strong coding and math capabilities, well suited for interactive chat, summarization, and cross-lingual translation.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = QWEN2_5_1_5B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = QWEN2_5_1_5B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = QWEN2_5_1_5B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.QWEN2_5_3B

QWEN2_5_3B: object & object

Alibaba Qwen 2.5 3B high-capability multilingual model. Delivers strong reasoning, coding, mathematics, and multilingual fluency across 29+ languages for in-depth text generation and complex multi-turn dialogue.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = QWEN2_5_3B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = QWEN2_5_3B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = QWEN2_5_3B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.QWEN3_0_6B

QWEN3_0_6B: object & object

Alibaba Qwen 3 0.6B next-generation compact language model. Features updated architectural optimizations for reduced latency, enhanced multilingual token representation, and efficient conversational turn-taking on mobile devices.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = QWEN3_0_6B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = QWEN3_0_6B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = QWEN3_0_6B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.QWEN3_1_7B

QWEN3_1_7B: object & object

Alibaba Qwen 3 1.7B next-generation multilingual language model. Balances high reasoning capability, general knowledge retrieval, coding proficiency, and conversational fluidity across multiple languages.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = QWEN3_1_7B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = QWEN3_1_7B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = QWEN3_1_7B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.QWEN3_4B

QWEN3_4B: object & object

Alibaba Qwen 3 4B high-capacity generative model. Delivers advanced multi-step reasoning, comprehensive world knowledge, complex coding capabilities, and top-tier multilingual performance for demanding on-device AI applications.

Type Declaration

MLX_INT4

MLX_INT4: LLMModel = QWEN3_4B_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: LLMModel = QWEN3_4B_XNNPACK_8DA4W

XNNPACK_BF16

XNNPACK_BF16: LLMModel = QWEN3_4B_XNNPACK_BF16

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.SMOLLM2_1_7B

SMOLLM2_1_7B: object & object

Hugging Face SmolLM2 1.7B language model trained on curated educational, synthetic, and web data. Delivers competitive reasoning, creative text generation, and general knowledge Q&A performance approaching larger 2B-3B models while maintaining fast on-device inference.

Type Declaration

MLX_INT8

MLX_INT8: LLMModel = SMOLLM2_1_7B_MLX_INT8

XNNPACK_8DA8W

XNNPACK_8DA8W: LLMModel = SMOLLM2_1_7B_8DA8W

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.SMOLLM2_135M

SMOLLM2_135M: object & object

Hugging Face SmolLM2 135M ultra-compact language model. Engineered for micro-memory footprints, instant token generation, text classification, and background processing on low-power devices.

Type Declaration

MLX_INT8

MLX_INT8: LLMModel = SMOLLM2_135M_MLX_INT8

XNNPACK_8DA8W

XNNPACK_8DA8W: LLMModel = SMOLLM2_135M_8DA8W

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

llm.SMOLLM2_360M

SMOLLM2_360M: object & object

Hugging Face SmolLM2 360M compact instruction-tuned model. Provides a practical balance between fast mobile generation speed and conversational coherence, ideal for lightweight on-device assistants, text simplification, and structured data extraction.

Type Declaration

MLX_INT8

MLX_INT8: LLMModel = SMOLLM2_360M_MLX_INT8

XNNPACK_8DA8W

XNNPACK_8DA8W: LLMModel = SMOLLM2_360M_8DA8W

Type Declaration

DEFAULT

readonly DEFAULT: LLMModel

objectDetection

objectDetection: object

Object detection models that identify object locations and bounding boxes.

objectDetection.RFDETR_NANO

RFDETR_NANO: object & object

RF-DETR (Roboflow Detection Transformer) Nano variant trained on COCO (see COCO_CLASSES). Modern end-to-end DINOv2-based transformer object detector.

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = RFDETR_NANO_DETECTOR_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = RFDETR_NANO_DETECTOR_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

objectDetection.SSDLITE320_MOBILENET_V3_LARGE

SSDLITE320_MOBILENET_V3_LARGE: object & object

SSDLite object detector with MobileNetV3-Large backbone trained on COCO (see COCO_CLASSES) at 320x320 resolution. Fast, lightweight detector suited for real-time mobile applications.

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = SSDLITE320_MOBILENET_V3_LARGE_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = SSDLITE320_MOBILENET_V3_LARGE_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "background" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "N/A" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

objectDetection.YOLO26

YOLO26: object & object

Ultralytics YOLO26 real-time object detection models trained on COCO (80 classes, see COCO_CLASSES_YOLO). Available across multiple scale sizes (NANO, SMALL, MEDIUM, LARGE, XLARGE) and resolutions (384x384, 512x512, 640x640).

Type Declaration

LARGE

LARGE: object & object

Large scale YOLO26 object detection model. High accuracy model variant.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_LARGE_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

MEDIUM

MEDIUM: object & object

Medium scale YOLO26 object detection model. Higher precision for complex scenes.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_MEDIUM_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

NANO

NANO: object & object

Nano scale YOLO26 object detection model. High speed, ultra low latency.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_NANO_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SMALL

SMALL: object & object

Small scale YOLO26 object detection model. Balanced latency and accuracy.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_SMALL_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

XLARGE

XLARGE: object & object

Extra Large scale YOLO26 object detection model. Maximum detection performance.

Type Declaration

SIZE_384

SIZE_384: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_384_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_384_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_512

SIZE_512: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_512_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_512_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

SIZE_640

SIZE_640: object & object

Type Declaration

COREML_FP16

COREML_FP16: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_640_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush"> = YOLO26_XLARGE_640_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

Type Declaration

DEFAULT

readonly DEFAULT: ObjectDetectorModel<"xyxy", "kite" | "zebra" | "parking meter" | "toaster" | "umbrella" | "vase" | "broccoli" | "orange" | "banana" | "cup" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "dog" | "horse" | "person" | "sheep" | "train" | "motorcycle" | "airplane" | "truck" | "traffic light" | "fire hydrant" | "stop sign" | "bench" | "elephant" | "bear" | "giraffe" | "backpack" | "handbag" | "tie" | "suitcase" | "frisbee" | "skis" | "snowboard" | "sports ball" | "baseball bat" | "baseball glove" | "skateboard" | "surfboard" | "tennis racket" | "wine glass" | "fork" | "knife" | "spoon" | "bowl" | "apple" | "sandwich" | "carrot" | "hot dog" | "pizza" | "donut" | "cake" | "couch" | "potted plant" | "bed" | "dining table" | "toilet" | "tv" | "laptop" | "mouse" | "remote" | "keyboard" | "cell phone" | "microwave" | "oven" | "sink" | "refrigerator" | "book" | "clock" | "scissors" | "teddy bear" | "hair drier" | "toothbrush">

ocr

ocr: object

Optical Character Recognition models — a text detector paired with a text recognizer, run as one two-stage pipeline.

ocr.PADDLE

PADDLE: object

PP-OCRv6 small — DBNet detector plus an SVTR recognizer, one model for every language.

On Android, VULKAN is the faster choice.

ocr.PADDLE.PPOCRV6_SMALL

PPOCRV6_SMALL: object & object

PP-OCRv6 Small multilingual OCR model.

Type Declaration

COREML

COREML: PaddleOcrModel = PPOCRV6_SMALL_COREML_INT8

VULKAN

VULKAN: PaddleOcrModel = PPOCRV6_SMALL_VULKAN_FP16

XNNPACK

XNNPACK: PaddleOcrModel = PPOCRV6_SMALL_XNNPACK_INT8

XNNPACK_FP32

XNNPACK_FP32: PaddleOcrModel = PPOCRV6_SMALL_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: PaddleOcrModel

privacyFilter

privacyFilter: object

Models that find and label personally identifiable information (PII) — names, emails, phone numbers, addresses, and the like — in free text, so it can be redacted or handled with care.

privacyFilter.NEMOTRON

NEMOTRON: object & object

Nemotron-based detector covering 55 fine-grained PII types. Larger label space for stricter compliance-oriented redaction.

Type Declaration

MLX_INT8

MLX_INT8: PrivacyFilterModel<"O" | "B-account_number" | "I-account_number" | "E-account_number" | "S-account_number" | "B-language" | "B-age" | "B-api_key" | "B-bank_routing_number" | "B-biometric_identifier" | "B-blood_type" | "B-certificate_license_number" | "B-city" | "B-company_name" | "B-coordinate" | "B-country" | "B-county" | "B-credit_debit_card" | "B-customer_id" | "B-cvv" | "B-date" | "B-date_of_birth" | "B-date_time" | "B-device_identifier" | "B-education_level" | "B-email" | "B-employee_id" | "B-employment_status" | "B-fax_number" | "B-first_name" | "B-gender" | "B-health_plan_beneficiary_number" | "B-http_cookie" | "B-ipv4" | "B-ipv6" | "B-last_name" | "B-license_plate" | "B-mac_address" | "B-medical_record_number" | "B-national_id" | "B-occupation" | "B-password" | "B-phone_number" | "B-pin" | "B-political_view" | "B-postcode" | "B-race_ethnicity" | "B-religious_belief" | "B-sexuality" | "B-ssn" | "B-state" | "B-street_address" | "B-swift_bic" | "B-tax_id" | "B-time" | "B-unique_id" | "B-url" | "B-user_name" | "B-vehicle_identifier" | "I-language" | "I-age" | "I-api_key" | "I-bank_routing_number" | "I-biometric_identifier" | "I-blood_type" | "I-certificate_license_number" | "I-city" | "I-company_name" | "I-coordinate" | "I-country" | "I-county" | "I-credit_debit_card" | "I-customer_id" | "I-cvv" | "I-date" | "I-date_of_birth" | "I-date_time" | "I-device_identifier" | "I-education_level" | "I-email" | "I-employee_id" | "I-employment_status" | "I-fax_number" | "I-first_name" | "I-gender" | "I-health_plan_beneficiary_number" | "I-http_cookie" | "I-ipv4" | "I-ipv6" | "I-last_name" | "I-license_plate" | "I-mac_address" | "I-medical_record_number" | "I-national_id" | "I-occupation" | "I-password" | "I-phone_number" | "I-pin" | "I-political_view" | "I-postcode" | "I-race_ethnicity" | "I-religious_belief" | "I-sexuality" | "I-ssn" | "I-state" | "I-street_address" | "I-swift_bic" | "I-tax_id" | "I-time" | "I-unique_id" | "I-url" | "I-user_name" | "I-vehicle_identifier" | "E-language" | "E-age" | "E-api_key" | "E-bank_routing_number" | "E-biometric_identifier" | "E-blood_type" | "E-certificate_license_number" | "E-city" | "E-company_name" | "E-coordinate" | "E-country" | "E-county" | "E-credit_debit_card" | "E-customer_id" | "E-cvv" | "E-date" | "E-date_of_birth" | "E-date_time" | "E-device_identifier" | "E-education_level" | "E-email" | "E-employee_id" | "E-employment_status" | "E-fax_number" | "E-first_name" | "E-gender" | "E-health_plan_beneficiary_number" | "E-http_cookie" | "E-ipv4" | "E-ipv6" | "E-last_name" | "E-license_plate" | "E-mac_address" | "E-medical_record_number" | "E-national_id" | "E-occupation" | "E-password" | "E-phone_number" | "E-pin" | "E-political_view" | "E-postcode" | "E-race_ethnicity" | "E-religious_belief" | "E-sexuality" | "E-ssn" | "E-state" | "E-street_address" | "E-swift_bic" | "E-tax_id" | "E-time" | "E-unique_id" | "E-url" | "E-user_name" | "E-vehicle_identifier" | "S-language" | "S-age" | "S-api_key" | "S-bank_routing_number" | "S-biometric_identifier" | "S-blood_type" | "S-certificate_license_number" | "S-city" | "S-company_name" | "S-coordinate" | "S-country" | "S-county" | "S-credit_debit_card" | "S-customer_id" | "S-cvv" | "S-date" | "S-date_of_birth" | "S-date_time" | "S-device_identifier" | "S-education_level" | "S-email" | "S-employee_id" | "S-employment_status" | "S-fax_number" | "S-first_name" | "S-gender" | "S-health_plan_beneficiary_number" | "S-http_cookie" | "S-ipv4" | "S-ipv6" | "S-last_name" | "S-license_plate" | "S-mac_address" | "S-medical_record_number" | "S-national_id" | "S-occupation" | "S-password" | "S-phone_number" | "S-pin" | "S-political_view" | "S-postcode" | "S-race_ethnicity" | "S-religious_belief" | "S-sexuality" | "S-ssn" | "S-state" | "S-street_address" | "S-swift_bic" | "S-tax_id" | "S-time" | "S-unique_id" | "S-url" | "S-user_name" | "S-vehicle_identifier"> = PRIVACY_FILTER_NEMOTRON_MLX_INT8

XNNPACK_8DA4W

XNNPACK_8DA4W: PrivacyFilterModel<"O" | "B-account_number" | "I-account_number" | "E-account_number" | "S-account_number" | "B-language" | "B-age" | "B-api_key" | "B-bank_routing_number" | "B-biometric_identifier" | "B-blood_type" | "B-certificate_license_number" | "B-city" | "B-company_name" | "B-coordinate" | "B-country" | "B-county" | "B-credit_debit_card" | "B-customer_id" | "B-cvv" | "B-date" | "B-date_of_birth" | "B-date_time" | "B-device_identifier" | "B-education_level" | "B-email" | "B-employee_id" | "B-employment_status" | "B-fax_number" | "B-first_name" | "B-gender" | "B-health_plan_beneficiary_number" | "B-http_cookie" | "B-ipv4" | "B-ipv6" | "B-last_name" | "B-license_plate" | "B-mac_address" | "B-medical_record_number" | "B-national_id" | "B-occupation" | "B-password" | "B-phone_number" | "B-pin" | "B-political_view" | "B-postcode" | "B-race_ethnicity" | "B-religious_belief" | "B-sexuality" | "B-ssn" | "B-state" | "B-street_address" | "B-swift_bic" | "B-tax_id" | "B-time" | "B-unique_id" | "B-url" | "B-user_name" | "B-vehicle_identifier" | "I-language" | "I-age" | "I-api_key" | "I-bank_routing_number" | "I-biometric_identifier" | "I-blood_type" | "I-certificate_license_number" | "I-city" | "I-company_name" | "I-coordinate" | "I-country" | "I-county" | "I-credit_debit_card" | "I-customer_id" | "I-cvv" | "I-date" | "I-date_of_birth" | "I-date_time" | "I-device_identifier" | "I-education_level" | "I-email" | "I-employee_id" | "I-employment_status" | "I-fax_number" | "I-first_name" | "I-gender" | "I-health_plan_beneficiary_number" | "I-http_cookie" | "I-ipv4" | "I-ipv6" | "I-last_name" | "I-license_plate" | "I-mac_address" | "I-medical_record_number" | "I-national_id" | "I-occupation" | "I-password" | "I-phone_number" | "I-pin" | "I-political_view" | "I-postcode" | "I-race_ethnicity" | "I-religious_belief" | "I-sexuality" | "I-ssn" | "I-state" | "I-street_address" | "I-swift_bic" | "I-tax_id" | "I-time" | "I-unique_id" | "I-url" | "I-user_name" | "I-vehicle_identifier" | "E-language" | "E-age" | "E-api_key" | "E-bank_routing_number" | "E-biometric_identifier" | "E-blood_type" | "E-certificate_license_number" | "E-city" | "E-company_name" | "E-coordinate" | "E-country" | "E-county" | "E-credit_debit_card" | "E-customer_id" | "E-cvv" | "E-date" | "E-date_of_birth" | "E-date_time" | "E-device_identifier" | "E-education_level" | "E-email" | "E-employee_id" | "E-employment_status" | "E-fax_number" | "E-first_name" | "E-gender" | "E-health_plan_beneficiary_number" | "E-http_cookie" | "E-ipv4" | "E-ipv6" | "E-last_name" | "E-license_plate" | "E-mac_address" | "E-medical_record_number" | "E-national_id" | "E-occupation" | "E-password" | "E-phone_number" | "E-pin" | "E-political_view" | "E-postcode" | "E-race_ethnicity" | "E-religious_belief" | "E-sexuality" | "E-ssn" | "E-state" | "E-street_address" | "E-swift_bic" | "E-tax_id" | "E-time" | "E-unique_id" | "E-url" | "E-user_name" | "E-vehicle_identifier" | "S-language" | "S-age" | "S-api_key" | "S-bank_routing_number" | "S-biometric_identifier" | "S-blood_type" | "S-certificate_license_number" | "S-city" | "S-company_name" | "S-coordinate" | "S-country" | "S-county" | "S-credit_debit_card" | "S-customer_id" | "S-cvv" | "S-date" | "S-date_of_birth" | "S-date_time" | "S-device_identifier" | "S-education_level" | "S-email" | "S-employee_id" | "S-employment_status" | "S-fax_number" | "S-first_name" | "S-gender" | "S-health_plan_beneficiary_number" | "S-http_cookie" | "S-ipv4" | "S-ipv6" | "S-last_name" | "S-license_plate" | "S-mac_address" | "S-medical_record_number" | "S-national_id" | "S-occupation" | "S-password" | "S-phone_number" | "S-pin" | "S-political_view" | "S-postcode" | "S-race_ethnicity" | "S-religious_belief" | "S-sexuality" | "S-ssn" | "S-state" | "S-street_address" | "S-swift_bic" | "S-tax_id" | "S-time" | "S-unique_id" | "S-url" | "S-user_name" | "S-vehicle_identifier"> = PRIVACY_FILTER_NEMOTRON_XNNPACK_8DA4W

Type Declaration

DEFAULT

readonly DEFAULT: PrivacyFilterModel<"O" | "B-account_number" | "I-account_number" | "E-account_number" | "S-account_number" | "B-language" | "B-age" | "B-api_key" | "B-bank_routing_number" | "B-biometric_identifier" | "B-blood_type" | "B-certificate_license_number" | "B-city" | "B-company_name" | "B-coordinate" | "B-country" | "B-county" | "B-credit_debit_card" | "B-customer_id" | "B-cvv" | "B-date" | "B-date_of_birth" | "B-date_time" | "B-device_identifier" | "B-education_level" | "B-email" | "B-employee_id" | "B-employment_status" | "B-fax_number" | "B-first_name" | "B-gender" | "B-health_plan_beneficiary_number" | "B-http_cookie" | "B-ipv4" | "B-ipv6" | "B-last_name" | "B-license_plate" | "B-mac_address" | "B-medical_record_number" | "B-national_id" | "B-occupation" | "B-password" | "B-phone_number" | "B-pin" | "B-political_view" | "B-postcode" | "B-race_ethnicity" | "B-religious_belief" | "B-sexuality" | "B-ssn" | "B-state" | "B-street_address" | "B-swift_bic" | "B-tax_id" | "B-time" | "B-unique_id" | "B-url" | "B-user_name" | "B-vehicle_identifier" | "I-language" | "I-age" | "I-api_key" | "I-bank_routing_number" | "I-biometric_identifier" | "I-blood_type" | "I-certificate_license_number" | "I-city" | "I-company_name" | "I-coordinate" | "I-country" | "I-county" | "I-credit_debit_card" | "I-customer_id" | "I-cvv" | "I-date" | "I-date_of_birth" | "I-date_time" | "I-device_identifier" | "I-education_level" | "I-email" | "I-employee_id" | "I-employment_status" | "I-fax_number" | "I-first_name" | "I-gender" | "I-health_plan_beneficiary_number" | "I-http_cookie" | "I-ipv4" | "I-ipv6" | "I-last_name" | "I-license_plate" | "I-mac_address" | "I-medical_record_number" | "I-national_id" | "I-occupation" | "I-password" | "I-phone_number" | "I-pin" | "I-political_view" | "I-postcode" | "I-race_ethnicity" | "I-religious_belief" | "I-sexuality" | "I-ssn" | "I-state" | "I-street_address" | "I-swift_bic" | "I-tax_id" | "I-time" | "I-unique_id" | "I-url" | "I-user_name" | "I-vehicle_identifier" | "E-language" | "E-age" | "E-api_key" | "E-bank_routing_number" | "E-biometric_identifier" | "E-blood_type" | "E-certificate_license_number" | "E-city" | "E-company_name" | "E-coordinate" | "E-country" | "E-county" | "E-credit_debit_card" | "E-customer_id" | "E-cvv" | "E-date" | "E-date_of_birth" | "E-date_time" | "E-device_identifier" | "E-education_level" | "E-email" | "E-employee_id" | "E-employment_status" | "E-fax_number" | "E-first_name" | "E-gender" | "E-health_plan_beneficiary_number" | "E-http_cookie" | "E-ipv4" | "E-ipv6" | "E-last_name" | "E-license_plate" | "E-mac_address" | "E-medical_record_number" | "E-national_id" | "E-occupation" | "E-password" | "E-phone_number" | "E-pin" | "E-political_view" | "E-postcode" | "E-race_ethnicity" | "E-religious_belief" | "E-sexuality" | "E-ssn" | "E-state" | "E-street_address" | "E-swift_bic" | "E-tax_id" | "E-time" | "E-unique_id" | "E-url" | "E-user_name" | "E-vehicle_identifier" | "S-language" | "S-age" | "S-api_key" | "S-bank_routing_number" | "S-biometric_identifier" | "S-blood_type" | "S-certificate_license_number" | "S-city" | "S-company_name" | "S-coordinate" | "S-country" | "S-county" | "S-credit_debit_card" | "S-customer_id" | "S-cvv" | "S-date" | "S-date_of_birth" | "S-date_time" | "S-device_identifier" | "S-education_level" | "S-email" | "S-employee_id" | "S-employment_status" | "S-fax_number" | "S-first_name" | "S-gender" | "S-health_plan_beneficiary_number" | "S-http_cookie" | "S-ipv4" | "S-ipv6" | "S-last_name" | "S-license_plate" | "S-mac_address" | "S-medical_record_number" | "S-national_id" | "S-occupation" | "S-password" | "S-phone_number" | "S-pin" | "S-political_view" | "S-postcode" | "S-race_ethnicity" | "S-religious_belief" | "S-sexuality" | "S-ssn" | "S-state" | "S-street_address" | "S-swift_bic" | "S-tax_id" | "S-time" | "S-unique_id" | "S-url" | "S-user_name" | "S-vehicle_identifier">

privacyFilter.OPENAI

OPENAI: object & object

OpenAI-style detector covering 8 common PII types (name, email, phone, address, and similar). Compact label space, best for general redaction.

Type Declaration

MLX_INT4

MLX_INT4: PrivacyFilterModel<"O" | "B-account_number" | "B-private_address" | "B-private_date" | "B-private_email" | "B-private_person" | "B-private_phone" | "B-private_url" | "B-secret" | "I-account_number" | "I-private_address" | "I-private_date" | "I-private_email" | "I-private_person" | "I-private_phone" | "I-private_url" | "I-secret" | "E-account_number" | "E-private_address" | "E-private_date" | "E-private_email" | "E-private_person" | "E-private_phone" | "E-private_url" | "E-secret" | "S-account_number" | "S-private_address" | "S-private_date" | "S-private_email" | "S-private_person" | "S-private_phone" | "S-private_url" | "S-secret"> = PRIVACY_FILTER_OPENAI_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: PrivacyFilterModel<"O" | "B-account_number" | "B-private_address" | "B-private_date" | "B-private_email" | "B-private_person" | "B-private_phone" | "B-private_url" | "B-secret" | "I-account_number" | "I-private_address" | "I-private_date" | "I-private_email" | "I-private_person" | "I-private_phone" | "I-private_url" | "I-secret" | "E-account_number" | "E-private_address" | "E-private_date" | "E-private_email" | "E-private_person" | "E-private_phone" | "E-private_url" | "E-secret" | "S-account_number" | "S-private_address" | "S-private_date" | "S-private_email" | "S-private_person" | "S-private_phone" | "S-private_url" | "S-secret"> = PRIVACY_FILTER_OPENAI_XNNPACK_8DA4W

Type Declaration

DEFAULT

readonly DEFAULT: PrivacyFilterModel<"O" | "B-account_number" | "B-private_address" | "B-private_date" | "B-private_email" | "B-private_person" | "B-private_phone" | "B-private_url" | "B-secret" | "I-account_number" | "I-private_address" | "I-private_date" | "I-private_email" | "I-private_person" | "I-private_phone" | "I-private_url" | "I-secret" | "E-account_number" | "E-private_address" | "E-private_date" | "E-private_email" | "E-private_person" | "E-private_phone" | "E-private_url" | "E-secret" | "S-account_number" | "S-private_address" | "S-private_date" | "S-private_email" | "S-private_person" | "S-private_phone" | "S-private_url" | "S-secret">

semanticSegmentation

semanticSegmentation: object

Semantic segmentation models that classify each pixel into target object or background classes.

semanticSegmentation.DEEPLAB_V3_MOBILENET_V3_LARGE

DEEPLAB_V3_MOBILENET_V3_LARGE: object & object

DeepLabV3 semantic segmentation model with MobileNetV3-Large backbone (21 classes, see PASCAL_VOC_LABELS). Combines DeepLabV3 feature extraction quality with a lightweight mobile backbone.

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_MOBILENET_V3_LARGE_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_MOBILENET_V3_LARGE_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_MOBILENET_V3_LARGE_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor">

semanticSegmentation.DEEPLAB_V3_RESNET101

DEEPLAB_V3_RESNET101: object & object

DeepLabV3 semantic segmentation model with ResNet-101 backbone (21 classes, see PASCAL_VOC_LABELS). High-capacity backbone for maximum segmentation detail and boundary accuracy.

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_RESNET101_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_RESNET101_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_RESNET101_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor">

semanticSegmentation.DEEPLAB_V3_RESNET50

DEEPLAB_V3_RESNET50: object & object

DeepLabV3 semantic segmentation model with ResNet-50 backbone (21 classes, see PASCAL_VOC_LABELS). High-accuracy segmentation utilizing atrous spatial pyramid pooling.

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_RESNET50_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_RESNET50_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = DEEPLAB_V3_RESNET50_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor">

semanticSegmentation.FCN_RESNET101

FCN_RESNET101: object & object

Fully Convolutional Network (FCN) semantic segmentation model with ResNet-101 backbone (21 classes, see PASCAL_VOC_LABELS).

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = FCN_RESNET101_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = FCN_RESNET101_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = FCN_RESNET101_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor">

semanticSegmentation.FCN_RESNET50

FCN_RESNET50: object & object

Fully Convolutional Network (FCN) semantic segmentation model with ResNet-50 backbone (21 classes, see PASCAL_VOC_LABELS).

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = FCN_RESNET50_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = FCN_RESNET50_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = FCN_RESNET50_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor">

semanticSegmentation.LRASPP_MOBILENET_V3_LARGE

LRASPP_MOBILENET_V3_LARGE: object & object

Lite R-ASPP semantic segmentation model with MobileNetV3-Large backbone (21 classes, see PASCAL_VOC_LABELS). Optimized for low-latency, real-time pixel-level segmentation on mobile devices.

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = LRASPP_MOBILENET_V3_LARGE_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = LRASPP_MOBILENET_V3_LARGE_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor"> = LRASPP_MOBILENET_V3_LARGE_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "aeroplane" | "bicycle" | "bird" | "boat" | "bottle" | "bus" | "car" | "cat" | "chair" | "cow" | "diningtable" | "dog" | "horse" | "motorbike" | "person" | "pottedplant" | "sheep" | "sofa" | "train" | "tvmonitor">

semanticSegmentation.SELFIE_SEGMENTATION

SELFIE_SEGMENTATION: object & object

Lightweight portrait selfie segmentation model for real-time person vs background separation. Categorizes pixels into background and person. Ideal for background blur and replacement effects.

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "person"> = SELFIE_SEGMENTATION_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "person"> = SELFIE_SEGMENTATION_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "person">

semanticSegmentation.SELFIE_SEGMENTATION_LANDSCAPE

SELFIE_SEGMENTATION_LANDSCAPE: object & object

MediaPipe Selfie Segmentation, landscape orientation. A separate 256x144 checkpoint rather than a resize of the portrait model.

Type Declaration

COREML_FP16

COREML_FP16: SemanticSegmenterModel<"background" | "person"> = SELFIE_SEGMENTATION_LANDSCAPE_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SemanticSegmenterModel<"background" | "person"> = SELFIE_SEGMENTATION_LANDSCAPE_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: SemanticSegmenterModel<"background" | "person">

speechToText

speechToText: object

Automatic Speech Recognition (ASR) / Speech-to-Text models.

speechToText.WHISPER

WHISPER: object

OpenAI Whisper automatic speech recognition model family with integrated Voice Activity Detection. Includes multilingual and English-only (EN) variants across model sizes (TINY, BASE, SMALL).

speechToText.WHISPER.BASE

BASE: object & object

Multilingual Whisper Base model. Higher accuracy across supported languages.

Type Declaration

COREML_FP16

COREML_FP16: WhisperSttModel = WHISPER_BASE_COREML_FP16

MLX_BF16

MLX_BF16: WhisperSttModel = WHISPER_BASE_MLX_BF16

MLX_INT8

MLX_INT8: WhisperSttModel = WHISPER_BASE_MLX_INT8

VULKAN_FP16

VULKAN_FP16: WhisperSttModel = WHISPER_BASE_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: WhisperSttModel = WHISPER_BASE_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: WhisperSttModel = WHISPER_BASE_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: WhisperSttModel

speechToText.WHISPER.EN

EN: object

English-only optimized Whisper models (TINY, BASE, SMALL).

speechToText.WHISPER.EN.BASE

BASE: object & object

English-only Whisper Base model. High accuracy English speech recognition.

Type Declaration

COREML_FP16

COREML_FP16: WhisperSttModel<"en"> = WHISPER_BASE_EN_COREML_FP16

MLX_BF16

MLX_BF16: WhisperSttModel<"en"> = WHISPER_BASE_EN_MLX_BF16

MLX_INT8

MLX_INT8: WhisperSttModel<"en"> = WHISPER_BASE_EN_MLX_INT8

VULKAN_FP16

VULKAN_FP16: WhisperSttModel<"en"> = WHISPER_BASE_EN_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: WhisperSttModel<"en"> = WHISPER_BASE_EN_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: WhisperSttModel<"en"> = WHISPER_BASE_EN_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: WhisperSttModel<"en"> = WHISPER_BASE_EN_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: WhisperSttModel<"en">

speechToText.WHISPER.EN.SMALL

SMALL: object & object

English-only Whisper Small model. Superior accuracy for English transcription.

Type Declaration

COREML_FP16

COREML_FP16: WhisperSttModel<"en"> = WHISPER_SMALL_EN_COREML_FP16

MLX_INT8

MLX_INT8: WhisperSttModel<"en"> = WHISPER_SMALL_EN_MLX_INT8

VULKAN_FP16

VULKAN_FP16: WhisperSttModel<"en"> = WHISPER_SMALL_EN_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: WhisperSttModel<"en"> = WHISPER_SMALL_EN_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: WhisperSttModel<"en"> = WHISPER_SMALL_EN_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: WhisperSttModel<"en"> = WHISPER_SMALL_EN_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: WhisperSttModel<"en">

speechToText.WHISPER.EN.TINY

TINY: object & object

English-only Whisper Tiny model. Fast and compact for English STT.

Type Declaration

COREML_FP16

COREML_FP16: WhisperSttModel<"en"> = WHISPER_TINY_EN_COREML_FP16

MLX_BF16

MLX_BF16: WhisperSttModel<"en"> = WHISPER_TINY_EN_MLX_BF16

MLX_INT8

MLX_INT8: WhisperSttModel<"en"> = WHISPER_TINY_EN_MLX_INT8

VULKAN_FP16

VULKAN_FP16: WhisperSttModel<"en"> = WHISPER_TINY_EN_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: WhisperSttModel<"en"> = WHISPER_TINY_EN_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: WhisperSttModel<"en"> = WHISPER_TINY_EN_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: WhisperSttModel<"en"> = WHISPER_TINY_EN_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: WhisperSttModel<"en">

speechToText.WHISPER.SMALL

SMALL: object & object

Multilingual Whisper Small model. Best accuracy for complex multi-language audio.

Type Declaration

COREML_FP16

COREML_FP16: WhisperSttModel = WHISPER_SMALL_COREML_FP16

MLX_INT8

MLX_INT8: WhisperSttModel = WHISPER_SMALL_MLX_INT8

VULKAN_FP16

VULKAN_FP16: WhisperSttModel = WHISPER_SMALL_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: WhisperSttModel = WHISPER_SMALL_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: WhisperSttModel = WHISPER_SMALL_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: WhisperSttModel

speechToText.WHISPER.TINY

TINY: object & object

Multilingual Whisper Tiny model. Supporting 99+ languages. High speed speech recognition.

Type Declaration

COREML_FP16

COREML_FP16: WhisperSttModel = WHISPER_TINY_COREML_FP16

MLX_BF16

MLX_BF16: WhisperSttModel = WHISPER_TINY_MLX_BF16

MLX_INT8

MLX_INT8: WhisperSttModel = WHISPER_TINY_MLX_INT8

VULKAN_FP16

VULKAN_FP16: WhisperSttModel = WHISPER_TINY_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: WhisperSttModel = WHISPER_TINY_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: WhisperSttModel = WHISPER_TINY_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: WhisperSttModel

styleTransfer

styleTransfer: object

Artistic style transfer models that re-style input images according to artwork patterns.

styleTransfer.CANDY

CANDY: object & object

Fast neural style transfer model generating a vibrant, artistic "Candy" style effect.

Type Declaration

COREML_FP16

COREML_FP16: StyleTransferModel = STYLE_TRANSFER_CANDY_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: StyleTransferModel = STYLE_TRANSFER_CANDY_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: StyleTransferModel = STYLE_TRANSFER_CANDY_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: StyleTransferModel

styleTransfer.MOSAIC

MOSAIC: object & object

Fast neural style transfer model applying a classic tile mosaic artistic pattern.

Type Declaration

COREML_FP16

COREML_FP16: StyleTransferModel = STYLE_TRANSFER_MOSAIC_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: StyleTransferModel = STYLE_TRANSFER_MOSAIC_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: StyleTransferModel = STYLE_TRANSFER_MOSAIC_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: StyleTransferModel

styleTransfer.RAIN_PRINCESS

RAIN_PRINCESS: object & object

Fast neural style transfer model applying a painterly "Rain Princess" oil painting aesthetic.

Type Declaration

COREML_FP16

COREML_FP16: StyleTransferModel = STYLE_TRANSFER_RAIN_PRINCESS_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: StyleTransferModel = STYLE_TRANSFER_RAIN_PRINCESS_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: StyleTransferModel = STYLE_TRANSFER_RAIN_PRINCESS_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: StyleTransferModel

styleTransfer.UDNIE

UDNIE: object & object

Fast neural style transfer model applying Francis Picabia's "Udnie" abstract art style.

Type Declaration

COREML_FP16

COREML_FP16: StyleTransferModel = STYLE_TRANSFER_UDNIE_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: StyleTransferModel = STYLE_TRANSFER_UDNIE_XNNPACK_FP32

XNNPACK_INT8

XNNPACK_INT8: StyleTransferModel = STYLE_TRANSFER_UDNIE_XNNPACK_INT8

Type Declaration

DEFAULT

readonly DEFAULT: StyleTransferModel

textEmbeddings

textEmbeddings: object

Text embedding models mapping sentences and documents into dense vector representations for semantic search and RAG.

textEmbeddings.ALL_MINILM_L6_V2

ALL_MINILM_L6_V2: object & object

Compact 384-dimensional sentence transformer mapping text to a dense vector space. Optimized for fast, general-purpose semantic search, sentence similarity, and clustering.

Type Declaration

COREML_FP16

COREML_FP16: TextEmbedderModel = ALL_MINILM_L6_V2_COREML_FP16

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = ALL_MINILM_L6_V2_VULKAN_FP16

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = ALL_MINILM_L6_V2_EMBEDDINGS

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.ALL_MPNET_BASE_V2

ALL_MPNET_BASE_V2: object & object

High-quality 768-dimensional sentence transformer model based on MPNet. Provides higher quality semantic embeddings compared to MiniLM.

Type Declaration

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = ALL_MPNET_BASE_V2_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: TextEmbedderModel = ALL_MPNET_BASE_V2_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = ALL_MPNET_BASE_V2_EMBEDDINGS

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.CLIP_VIT_BASE_PATCH32_TEXT

CLIP_VIT_BASE_PATCH32_TEXT: object & object

CLIP text encoder (ViT-B/32) mapping text queries into a 512-dimensional joint text-image embedding space. Used in combination with imageEmbeddings.CLIP_VIT_BASE_PATCH32 for zero-shot text-to-image search.

Type Declaration

COREML_FP16

COREML_FP16: TextEmbedderModel = CLIP_VIT_BASE_PATCH32_TEXT_COREML_FP16

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = CLIP_VIT_BASE_PATCH32_TEXT_VULKAN_FP16

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = CLIP_VIT_BASE_PATCH32_TEXT_EMBEDDINGS

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.DISTILUSE_BASE_MULTILINGUAL_CASED_V2

DISTILUSE_BASE_MULTILINGUAL_CASED_V2: object & object

Multilingual sentence transformer supporting 50+ languages, based on distilled Universal Sentence Encoder (512-dim output).

Type Declaration

COREML_FP16

COREML_FP16: TextEmbedderModel = DISTILUSE_BASE_MULTILINGUAL_CASED_V2_COREML_FP16

MLX_INT8

MLX_INT8: TextEmbedderModel = DISTILUSE_BASE_MULTILINGUAL_CASED_V2_MLX_INT8

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = DISTILUSE_BASE_MULTILINGUAL_CASED_V2_VULKAN_FP16

XNNPACK_8DA4W

XNNPACK_8DA4W: TextEmbedderModel = DISTILUSE_BASE_MULTILINGUAL_CASED_V2_EMBEDDINGS

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = DISTILUSE_BASE_MULTILINGUAL_CASED_V2_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.LFM2_5_EMBEDDING_350M

LFM2_5_EMBEDDING_350M: object & object

Liquid AI LFM 2.5 350M parameter embedding model for asymmetric search and retrieval tasks. Prompts queries with query: (the default) and passages with document: via TextEmbedder.embed.

Type Declaration

MLX_INT4

MLX_INT4: TextEmbedderModel = LFM2_5_EMBEDDING_350M_MLX_INT4

XNNPACK_8DA4W

XNNPACK_8DA4W: TextEmbedderModel = LFM2_5_EMBEDDING_350M_EMBEDDINGS

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.MULTI_QA_MINILM_L6_COS_V1

MULTI_QA_MINILM_L6_COS_V1: object & object

384-dimensional sentence transformer fine-tuned specifically for semantic QA matching using cosine similarity.

Type Declaration

COREML_FP16

COREML_FP16: TextEmbedderModel = MULTI_QA_MINILM_L6_COS_V1_COREML_FP16

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = MULTI_QA_MINILM_L6_COS_V1_VULKAN_FP16

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = MULTI_QA_MINILM_L6_COS_V1_EMBEDDINGS

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.MULTI_QA_MPNET_BASE_DOT_V1

MULTI_QA_MPNET_BASE_DOT_V1: object & object

768-dimensional sentence transformer fine-tuned specifically for question-answering matching using dot product distance.

Type Declaration

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = MULTI_QA_MPNET_BASE_DOT_V1_VULKAN_FP16

VULKAN_INT8

VULKAN_INT8: TextEmbedderModel = MULTI_QA_MPNET_BASE_DOT_V1_VULKAN_INT8

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = MULTI_QA_MPNET_BASE_DOT_V1_EMBEDDINGS

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textEmbeddings.PARAPHRASE_MULTILINGUAL_MINILM_L12_V2

PARAPHRASE_MULTILINGUAL_MINILM_L12_V2: object & object

384-dimensional sentence transformer supporting 50+ languages for cross-lingual semantic similarity.

Type Declaration

COREML_FP16

COREML_FP16: TextEmbedderModel = PARAPHRASE_MULTILINGUAL_MINILM_L12_V2_COREML_FP16

VULKAN_FP16

VULKAN_FP16: TextEmbedderModel = PARAPHRASE_MULTILINGUAL_MINILM_L12_V2_VULKAN_FP16

XNNPACK_8DA4W

XNNPACK_8DA4W: TextEmbedderModel = PARAPHRASE_MULTILINGUAL_MINILM_L12_V2_EMBEDDINGS

XNNPACK_FP32

XNNPACK_FP32: TextEmbedderModel = PARAPHRASE_MULTILINGUAL_MINILM_L12_V2_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: TextEmbedderModel

textToImage

textToImage: object

Generative text-to-image synthesis models.

textToImage.SDXS_512_DREAMSHAPER

SDXS_512_DREAMSHAPER: object & object

Ultra-fast SDXS (Stable Diffusion eXtreme Speed) 512x512 text-to-image generation model based on DreamShaper. Generates high-quality images from text prompts in real time.

Type Declaration

COREML_FP16

COREML_FP16: SdxsTextToImageModel = SDXS_512_DREAMSHAPER_COREML_FP16

XNNPACK_FP32

XNNPACK_FP32: SdxsTextToImageModel = SDXS_512_DREAMSHAPER_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: SdxsTextToImageModel

textToSpeech

textToSpeech: object

Text-to-Speech (TTS) models that synthesize audio waveforms from input text.

textToSpeech.KOKORO

KOKORO: object

Kokoro — a lightweight phoneme-driven Text-to-Speech model. Each language entry bundles the matching model weights, grapheme-to-phoneme assets and the voices available for that language, nested per backend.

textToSpeech.KOKORO.DE

DE: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"df_anna"> = KOKORO_DE_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"df_anna"> = KOKORO_DE_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"df_anna">

textToSpeech.KOKORO.EN_GB

EN_GB: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"bf_emma" | "bm_daniel"> = KOKORO_EN_GB_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"bf_emma" | "bm_daniel"> = KOKORO_EN_GB_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"bf_emma" | "bm_daniel">

textToSpeech.KOKORO.EN_US

EN_US: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"af_heart" | "af_river" | "af_sarah" | "am_adam" | "am_michael" | "am_santa"> = KOKORO_EN_US_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"af_heart" | "af_river" | "af_sarah" | "am_adam" | "am_michael" | "am_santa"> = KOKORO_EN_US_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"af_heart" | "af_river" | "af_sarah" | "am_adam" | "am_michael" | "am_santa">

textToSpeech.KOKORO.ES

ES: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"ef_dora" | "em_alex"> = KOKORO_ES_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"ef_dora" | "em_alex"> = KOKORO_ES_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"ef_dora" | "em_alex">

textToSpeech.KOKORO.FR

FR: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"ff_siwis"> = KOKORO_FR_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"ff_siwis"> = KOKORO_FR_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"ff_siwis">

textToSpeech.KOKORO.HI

HI: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"hf_alpha" | "hm_omega" | "hm_psi"> = KOKORO_HI_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"hf_alpha" | "hm_omega" | "hm_psi"> = KOKORO_HI_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"hf_alpha" | "hm_omega" | "hm_psi">

textToSpeech.KOKORO.IT

IT: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"if_sara" | "im_nicola"> = KOKORO_IT_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"if_sara" | "im_nicola"> = KOKORO_IT_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"if_sara" | "im_nicola">

textToSpeech.KOKORO.PL

PL: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"pm_mateusz"> = KOKORO_PL_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"pm_mateusz"> = KOKORO_PL_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"pm_mateusz">

textToSpeech.KOKORO.PT

PT: object & object

Type Declaration

COREML_FP32

COREML_FP32: KokoroTtsModel<"pf_dora" | "pm_santa"> = KOKORO_PT_COREML_FP32

XNNPACK_FP32

XNNPACK_FP32: KokoroTtsModel<"pf_dora" | "pm_santa"> = KOKORO_PT_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: KokoroTtsModel<"pf_dora" | "pm_santa">

textToSpeech.SUPERTONIC

SUPERTONIC: object & object

Supertonic 3 multilingual flow-matching Text-to-Speech model. Delivers natural, highly expressive speech synthesis with configurable speaker voice presets (see SUPERTONIC_DEFAULT_VOICE_NAMES).

Type Declaration

MLX_FP32

MLX_FP32: SupertonicTtsModel<"F1" | "F2" | "F3" | "F4" | "F5" | "M1" | "M2" | "M3" | "M4" | "M5"> = SUPERTONIC_3_MLX_FP32

VULKAN_FP16

VULKAN_FP16: SupertonicTtsModel<"F1" | "F2" | "F3" | "F4" | "F5" | "M1" | "M2" | "M3" | "M4" | "M5"> = SUPERTONIC_3_VULKAN_FP16

XNNPACK_FP32

XNNPACK_FP32: SupertonicTtsModel<"F1" | "F2" | "F3" | "F4" | "F5" | "M1" | "M2" | "M3" | "M4" | "M5"> = SUPERTONIC_3_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: SupertonicTtsModel<"F1" | "F2" | "F3" | "F4" | "F5" | "M1" | "M2" | "M3" | "M4" | "M5">

tokenizer

tokenizer: object

Standalone text tokenizers for preprocessing strings into token ID arrays.

tokenizer.ALL_MINILM_L6_V2

ALL_MINILM_L6_V2: string = ALL_MINILM_L6_V2_TOKENIZER

WordPiece tokenizer URL for the all-MiniLM-L6-v2 embedding model.

voiceActivityDetection

voiceActivityDetection: object

Voice Activity Detection (VAD) models detecting speech vs non-speech intervals in real-time audio streams.

voiceActivityDetection.FSMN_VAD

FSMN_VAD: object & object

Feedforward Sequential Memory Network (FSMN) Voice Activity Detection model. Extremely lightweight model evaluating continuous speech probability chunks for live mic streaming and STT preprocessing.

Type Declaration

XNNPACK_FP32

XNNPACK_FP32: FsmnVadModel = FSMN_VAD_XNNPACK_FP32

Type Declaration

DEFAULT

readonly DEFAULT: FsmnVadModel

Type Declaration​

classification​

classification.EFFICIENTNET_V2_S​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

XNNPACK_INT8​

Type Declaration​

DEFAULT​

imageEmbeddings​

imageEmbeddings.CLIP_VIT_BASE_PATCH32​

Type Declaration​

COREML_FP16​

MLX_INT8​

VULKAN_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

instanceSegmentation​

instanceSegmentation.FASTSAM​

instanceSegmentation.FASTSAM.S​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

instanceSegmentation.FASTSAM.X​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

instanceSegmentation.RFDETR_NANO​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

instanceSegmentation.YOLO26​

Type Declaration​

LARGE​

Type Declaration​

SIZE_384​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

SIZE_512​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

SIZE_640​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

Type Declaration​

DEFAULT​

MEDIUM​

Type Declaration​

SIZE_384​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

SIZE_512​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration​

DEFAULT​

SIZE_640​

Type Declaration​

COREML_FP16​

XNNPACK_FP32​

Type Declaration

classification

classification.EFFICIENTNET_V2_S

Type Declaration

COREML_FP16

XNNPACK_FP32

XNNPACK_INT8

Type Declaration

DEFAULT

imageEmbeddings

imageEmbeddings.CLIP_VIT_BASE_PATCH32

Type Declaration

COREML_FP16

MLX_INT8

VULKAN_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

instanceSegmentation

instanceSegmentation.FASTSAM

instanceSegmentation.FASTSAM.S

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

instanceSegmentation.FASTSAM.X

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

instanceSegmentation.RFDETR_NANO

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

instanceSegmentation.YOLO26

Type Declaration

LARGE

Type Declaration

SIZE_384

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

SIZE_512

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

SIZE_640

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

Type Declaration

DEFAULT

MEDIUM

Type Declaration

SIZE_384

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

SIZE_512

Type Declaration

COREML_FP16

XNNPACK_FP32

Type Declaration

DEFAULT

SIZE_640

Type Declaration

COREML_FP16

XNNPACK_FP32