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{ "bbox": [ [ 71.27, 48.63, 93.02, 114.36000000000001 ], [ 216.41, 35.43, 44.97999999999999, 26.509999999999998 ] ], "categories": [ 1072, 536 ], "category_names": [ "tennis_racket", "headband" ] }
[ { "label": "tennis_racket", "category": 1072, "points": [ [ 153.17, 161.82 ], [ 155.12, 162.41 ], [ 157.66, 162.99 ], [ 162.15, 162.6 ], [ 164.29, 161.82 ], [...
{ "bbox": [ [ 136.84, 157.82, 15.849999999999994, 16.730000000000018 ], [ 448, 172.77, 18.819999999999993, 9.879999999999995 ], [ 201.44, 131.85, 18.930000000000007, 27.75 ], [ 282.49, 219.53, 139.539...
[ { "label": "control", "category": 288, "points": [ [ 136.84, 167.63 ], [ 139.74, 162.35 ], [ 140.99, 159.46 ], [ 142.75, 157.95 ], [ 145.27, 157.82 ], [ ...
{ "bbox": [ [ 476.33, 337.43, 27.75999999999999, 21.019999999999982 ], [ 507.07, 331.3, 35.98999999999995, 24.039999999999964 ] ], "categories": [ 1185, 1185 ], "category_names": [ "windshield_wiper", "windshield_wiper" ] }
[ { "label": "windshield_wiper", "category": 1185, "points": [ [ 476.33, 357.06 ], [ 476.73, 356.01 ], [ 480.04, 354.39 ], [ 483.52, 351.6 ], [ 493.08, 342.42 ], ...
{ "bbox": [ [ 302.97, 192.97, 137.34999999999997, 111.55999999999997 ] ], "categories": [ 1108 ], "category_names": [ "train_(railroad_vehicle)" ] }
[ { "label": "train_(railroad_vehicle)", "category": 1108, "points": [ [ 305.69, 279.66 ], [ 303.79, 273.71 ], [ 302.97, 262.52 ], [ 303.79, 247.79 ], [ 303.07, 223.29 ...
{ "bbox": [ [ 360.32, 148.19, 86.16000000000003, 113.55000000000001 ], [ 228.44, 65.58, 62.00999999999999, 108.39999999999999 ], [ 182.44, 70.75, 58.849999999999994, 64.19999999999999 ], [ 358.35, 319.66, ...
[ { "label": "backpack", "category": 44, "points": [ [ 368.84, 260.68 ], [ 362.07, 254.54 ], [ 360.32, 252.41 ], [ 360.44, 249.9 ], [ 361.57, 246.14 ], [ ...
{ "bbox": [ [ 365.35, 298.18, 35.91999999999996, 39.69 ] ], "categories": [ 1110 ], "category_names": [ "trash_can" ] }
[ { "label": "trash_can", "category": 1110, "points": [ [ 366.25, 323.93 ], [ 366.07, 313.73 ], [ 365.53, 307.48 ], [ 370.71, 307.12 ], [ 375.9, 307.3 ], [ ...
{ "bbox": [ [ 204.21, 275.38, 39, 29.430000000000007 ], [ 358.92, 44.08, 19.189999999999998, 15.670000000000002 ], [ 143.6, 278, 53.31999999999999, 40.19 ], [ 343.38, 43.39, 13.340000000000032, ...
[ { "label": "ski_boot", "category": 958, "points": [ [ 238.96, 275.38 ], [ 240.03, 278.11 ], [ 239.72, 282.21 ], [ 240.48, 285.7 ], [ 241.39, 289.49 ], [ ...
{ "bbox": [ [ 549.43, 164.08, 90.57000000000005, 122.94999999999996 ], [ 558.54, 218.71, 81.46000000000004, 209.29 ], [ 24.7, 123.47, 212.24, 246.76000000000002 ], [ 424.32, 125.09, 142.73999999999995...
[ { "label": "sheep", "category": 936, "points": [ [ 618.71, 193.22 ], [ 626.01, 197.78 ], [ 636.95, 192.31 ], [ 640, 218.73 ], [ 618.71, 230.57 ], [ 60...
{ "bbox": [ [ 354.38, 214.58, 20.170000000000016, 19.839999999999975 ], [ 120.69, 261.94, 4.969999999999999, 9.149999999999977 ], [ 91.67, 227.95, 8.079999999999998, 18.80000000000001 ], [ 242.86, 31.64, ...
[ { "label": "bird", "category": 104, "points": [ [ 354.91, 217.24 ], [ 356.76, 216.83 ], [ 358.8, 214.58 ], [ 359.52, 217.04 ], [ 362.18, 217.96 ], [ 3...
{ "bbox": [ [ 469.25, 210, 25.899999999999977, 31.919999999999987 ], [ 50.17, 177.86, 6.479999999999997, 9.529999999999973 ], [ 205.43, 202.68, 12.969999999999999, 12.969999999999999 ], [ 485.79, 197.56, ...
[ { "label": "domestic_ass", "category": 372, "points": [ [ 474.3, 240.78 ], [ 475.44, 241.92 ], [ 476.25, 241.6 ], [ 476.25, 240.29 ], [ 476.58, 238.83 ], [ ...
{ "bbox": [ [ 141.35, 252.28, 309.21000000000004, 224.95000000000002 ], [ 363.62, 0, 229.04999999999995, 224.21 ], [ 383.54, 326.89, 255.09999999999997, 152.48000000000002 ], [ 257.68, 51.32, 169.14, ...
[ { "label": "broccoli", "category": 158, "points": [ [ 255.14, 477.23 ], [ 198.13, 432.1 ], [ 164.9, 407.36 ], [ 149.93, 389.82 ], [ 154.63, 380.53 ], [ ...
{ "bbox": [ [ 41.88, 194.58, 7.159999999999997, 9.019999999999982 ], [ 72.1, 194.87, 8.100000000000009, 9.859999999999985 ], [ 22.2, 117.7, 288.03000000000003, 128.51 ], [ 277.08, 146.57, 116.66000000...
[ { "label": "headlight", "category": 538, "points": [ [ 46.37, 203.37 ], [ 47.67, 202.45 ], [ 48.66, 201 ], [ 49.04, 199.17 ], [ 48.66, 196.81 ], [ 47....
{ "bbox": [ [ 0, 122.98, 18.59, 78.96 ], [ 14.83, 5.29, 310.61, 183.54000000000002 ], [ 0, 0, 640, 470.48 ], [ 359.03, 1.99, 157.79000000000008, 65.41000000000001 ] ], "categories": [ ...
[ { "label": "fork", "category": 457, "points": [ [ 18.59, 131.92 ], [ 0, 122.98 ], [ 0, 201.94 ], [ 11.1, 177.95 ], [ 14.46, 163.32 ], [ 18.42, ...
{ "bbox": [ [ 277.02, 77.71, 46.81999999999999, 21.03 ], [ 222.22, 59.18, 48.41, 31.520000000000003 ], [ 255.25, 42.23, 165.88, 153.87 ], [ 147.73, 15.8, 146.42, 167.32 ] ], "categories"...
[ { "label": "dog_collar", "category": 367, "points": [ [ 288.32, 78.8 ], [ 287.88, 81.21 ], [ 287.99, 83.51 ], [ 287.99, 85.71 ], [ 289.08, 87.79 ], [ ...
{ "bbox": [ [ 0, 113.49, 228.2, 278.9 ], [ 50.31, 305.86, 346.49, 227.05999999999995 ] ], "categories": [ 1201, 1201 ], "category_names": [ "zebra", "zebra" ] }
[ { "label": "zebra", "category": 1201, "points": [ [ 0, 135.75 ], [ 4.67, 137.1 ], [ 14.7, 140.5 ], [ 20.82, 141.69 ], [ 26.43, 143.39 ], [ 32.72, ...
{ "bbox": [ [ 93.73, 244.9, 394.15, 178.51000000000002 ] ], "categories": [ 461 ], "category_names": [ "frisbee" ] }
[ { "label": "frisbee", "category": 461, "points": [ [ 93.73, 423.41 ], [ 487.88, 422.62 ], [ 481.54, 386.92 ], [ 468.85, 340.9 ], [ 456.95, 307.58 ], [ ...
{ "bbox": [ [ 0, 234.4, 355.18, 112.37999999999997 ] ], "categories": [ 13 ], "category_names": [ "airplane" ] }
[ { "label": "airplane", "category": 13, "points": [ [ 235.33, 330.71 ], [ 236.83, 332.7 ], [ 242.76, 333.67 ], [ 256.01, 334.08 ], [ 260.01, 333.53 ], [ ...
{ "bbox": [ [ 463.9, 174.16, 2.4700000000000273, 11.950000000000017 ], [ 469.08, 173.68, 1.400000000000034, 10.030000000000001 ], [ 410.03, 182.82, 4.190000000000055, 28.47 ], [ 435.36, 178.95, 3.0699...
[ { "label": "parking_meter", "category": 756, "points": [ [ 466.08, 186.11 ], [ 465.7, 180.13 ], [ 466.08, 179.37 ], [ 466.37, 176.81 ], [ 465.8, 175.01 ], [ ...
{ "bbox": [ [ 12.32, 121.51, 34.54, 108.36999999999999 ], [ 532.84, 50.51, 34.26999999999998, 165.4 ], [ 181.89, 96.96, 33.10000000000002, 133.40000000000003 ] ], "categories": [ 1021, 1021, 1021 ], "ca...
[ { "label": "streetlight", "category": 1021, "points": [ [ 12.75, 121.51 ], [ 15.87, 121.51 ], [ 18.77, 123.74 ], [ 24.12, 126.42 ], [ 28.36, 128.2 ], [ ...
{ "bbox": [ [ 433.83, 201.37, 12.189999999999998, 3.5600000000000023 ], [ 439.48, 193.53, 12.740000000000009, 6.189999999999998 ], [ 405.54, 186.27, 13.269999999999982, 8.359999999999985 ], [ 538.12, 186.82...
[ { "label": "boat", "category": 122, "points": [ [ 433.83, 201.37 ], [ 434.65, 203.97 ], [ 436.43, 204.38 ], [ 444.65, 204.93 ], [ 446.02, 203.01 ], [ ...
{ "bbox": [ [ 244.97, 65.25, 33.190000000000026, 22.340000000000003 ], [ 298.45, 159.23, 93.25999999999999, 151.79 ] ], "categories": [ 461, 940 ], "category_names": [ "frisbee", "shirt" ] }
[ { "label": "frisbee", "category": 461, "points": [ [ 250.46, 74.24 ], [ 247.71, 76.61 ], [ 246.22, 79.23 ], [ 245.34, 81.6 ], [ 244.97, 83.22 ], [ 245...
{ "bbox": [ [ 193.68, 112.38, 13.47999999999999, 6.570000000000007 ], [ 39.12, 91.36, 31.089999999999996, 10.840000000000003 ], [ 23.08, 119.54, 69.60000000000001, 115.46999999999998 ], [ 189.22, 124.62, ...
[ { "label": "sunglasses", "category": 1030, "points": [ [ 193.68, 117.14 ], [ 199.27, 114.68 ], [ 202.23, 114.35 ], [ 205.52, 113.36 ], [ 207.16, 112.38 ], [ ...
{ "bbox": [ [ 162.99, 233.62, 113.15999999999997, 52.44999999999999 ], [ 28.14, 259.81, 110.72000000000001, 58.79000000000002 ], [ 273.92, 288.83, 78.83999999999997, 72.46000000000004 ], [ 376.86, 258.09, ...
[ { "label": "ham", "category": 518, "points": [ [ 164.43, 272.78 ], [ 167.3, 274.4 ], [ 169.46, 274.93 ], [ 175.39, 274.22 ], [ 193.89, 274.57 ], [ 199...
{ "bbox": [ [ 82.06, 343.73, 50.03999999999999, 31.060000000000002 ], [ 478.38, 217.92, 9.939999999999998, 16.880000000000024 ], [ 478.75, 196.82, 9.110000000000014, 15.79000000000002 ], [ 319.36, 181.69, ...
[ { "label": "license_plate", "category": 631, "points": [ [ 90.48, 344.49 ], [ 126.9, 355.21 ], [ 129.81, 355.82 ], [ 131.49, 356.28 ], [ 132.1, 357.5 ], [ ...
{ "bbox": [ [ 132.5, 474.53, 51.21000000000001, 49.32000000000005 ], [ 261.13, 421.19, 47.44999999999999, 40.80000000000001 ], [ 243.98, 473.04, 62.01000000000002, 91.55000000000001 ], [ 149.34, 515.08, ...
[ { "label": "helmet", "category": 545, "points": [ [ 150.21, 516.85 ], [ 153.37, 515.61 ], [ 155.96, 515.84 ], [ 158.78, 517.31 ], [ 160.92, 517.64 ], [ ...
{ "bbox": [ [ 148.73, 254.07, 41.59, 16.629999999999995 ], [ 278.26, 231.45, 10.150000000000034, 3.469999999999999 ], [ 310.24, 229.82, 12.560000000000002, 4.490000000000009 ], [ 360.67, 289.42, 10.62...
[ { "label": "umbrella", "category": 1128, "points": [ [ 176.4, 264.76 ], [ 181.32, 265.44 ], [ 186.41, 265.95 ], [ 190.32, 265.95 ], [ 186.07, 260.86 ], [ ...
{ "bbox": [ [ 318.84, 34.31, 304.12000000000006, 391.69 ] ], "categories": [ 366 ], "category_names": [ "dog" ] }
[ { "label": "dog", "category": 366, "points": [ [ 387.23, 342.07 ], [ 391.72, 342.07 ], [ 390.34, 342.58 ], [ 387.23, 342.58 ], [ 386.19, 348.11 ], [ 3...
{ "bbox": [ [ 424.63, 0, 1.740000000000009, 7.37 ], [ 265.82, 6.16, 2.980000000000018, 7.390000000000001 ], [ 76.06, 34.06, 3.1700000000000017, 5.960000000000001 ], [ 241.55, 8.53, 3.75, 7.75000...
[ { "label": "pole", "category": 820, "points": [ [ 425.25, 3.03 ], [ 424.88, 5.63 ], [ 424.63, 6.75 ], [ 425.13, 7.12 ], [ 426.37, 7.37 ], [ 425.87, ...
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LVIS-20k

19,808 images and 244,700 annotations across 1,203 classes, annotated as polygon and bounding box.

View on Pictograph · Pictograph Research · Creative Commons Attribution 4.0

About

LVIS-20k is a computer-vision dataset curated and annotated on Pictograph. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.

At a glance

Metric Value
Images 19,808
Annotations 244,700
Classes 1,203
Annotation types polygon, bounding box
Splits train

Dataset structure

This dataset uses the Hugging Face imagefolder layout: each split directory holds the images plus a metadata.jsonl that links every image to its annotations by file_name.

Field Description
file_name Path to the image within the split directory.
objects.bbox Bounding boxes as [x, y, width, height] (pixels).
objects.categories Integer class index per box (matches the class list below).
objects.category_names Human class name per box.
segmentation Polygon rings as [[x, y], ...] with class label.

Use it

from datasets import load_dataset

ds = load_dataset("pictograph/lvis-20k")
print(ds)

Prefer a full annotation editor, one-click fork, multi-format export, and one-click training? Open this dataset on Pictograph.

Classes

Class index matches objects.categories in metadata.jsonl.

All 1,203 classes, in index order: Band_Aid, Bible, CD_player, Christmas_tree, Dixie_cup, Ferris_wheel, French_toast, Lego, Rollerblade, Sharpie, Tabasco_sauce, aerosol_can, air_conditioner, airplane, alarm_clock, alcohol, alligator, almond, ambulance, amplifier, anklet, antenna, apple, applesauce, apricot, apron, aquarium, arctic_(type_of_shoe), armband, armchair, armoire, armor, army_tank, artichoke, ashtray, asparagus, atomizer, automatic_washer, avocado, award, awning, ax, baboon, baby_buggy, backpack, bagel, bagpipe, baguet, bait, ball, ballet_skirt, balloon, bamboo, banana, bandage, bandanna, banjo, banner, barbell, barge, barrel, barrette, barrow, baseball, baseball_base, baseball_bat, baseball_cap, baseball_glove, basket, basketball, basketball_backboard, bass_horn, bat_(animal), bath_mat, bath_towel, bathrobe, bathtub, batter_(food), battery, beachball, bead, bean_curd, beanbag, beanie, bear, bed, bedpan, bedspread, beef_(food), beeper, beer_bottle, beer_can, beetle, bell, bell_pepper, belt, belt_buckle, bench, beret, bib, bicycle, billboard, binder, binoculars, bird, birdbath, birdcage, birdfeeder, birdhouse, birthday_cake, birthday_card, black_sheep, blackberry, blackboard, blanket, blazer, blender, blimp, blinder_(for_horses), blinker, blouse, blueberry, boat, bob, bobbin, bobby_pin, boiled_egg, bolo_tie, bolt, bonnet, book, bookcase, booklet, bookmark, boom_microphone, boot, bottle, bottle_cap, bottle_opener, bouquet, bow-tie, bow_(decorative_ribbons), bow_(weapon), bowl, bowler_hat, bowling_ball, box, boxing_glove, bracelet, brake_light, brass_plaque, brassiere, bread, bread-bin, breechcloth, bridal_gown, briefcase, broach, broccoli, broom, brownie, brussels_sprouts, bubble_gum, bucket, bull, bulldog, bulldozer, bullet_train, bulletin_board, bulletproof_vest, bullhorn, bun, bunk_bed, buoy, burrito, bus_(vehicle), business_card, butter, butterfly, button, cab_(taxi), cabana, cabin_car, cabinet, cake, calculator, calendar, calf, camcorder, camel, camera, camera_lens, camper_(vehicle), can, can_opener, candle, candle_holder, candy_bar, candy_cane, canister, canoe, cantaloup, canteen, cap_(headwear), cape, cappuccino, car_(automobile), car_battery, card, cardigan, cargo_ship, carnation, carrot, cart, carton, cash_register, casserole, cassette, cast, cat, cauliflower, cayenne_(spice), celery, cellular_telephone, chain_mail, chair, chaise_longue, chalice, chandelier, chap, checkbook, checkerboard, cherry, chessboard, chicken_(animal), chickpea, chili_(vegetable), chime, chinaware, chocolate_bar, chocolate_cake, chocolate_milk, chocolate_mousse, choker, chopping_board, chopstick, cider, cigar_box, cigarette, cigarette_case, cincture, cistern, clarinet, clasp, cleansing_agent, cleat_(for_securing_rope), clementine, clip, clipboard, clippers_(for_plants), cloak, clock, clock_tower, clothes_hamper, clothespin, clutch_bag, coaster, coat, coat_hanger, coatrack, cock, cockroach, cocoa_(beverage), coconut, coffee_maker, coffee_table, coffeepot, coil, coin, colander, coleslaw, coloring_material, combination_lock, comic_book, compass, computer_keyboard, condiment, cone, control, convertible_(automobile), cooker, cookie, cooking_utensil, cooler_(for_food), cork_(bottle_plug), corkboard, corkscrew, cornbread, cornet, cornice, cornmeal, corset, costume, cougar, cover, coverall, cow, cowbell, cowboy_hat, crab_(animal), crabmeat, cracker, crape, crate, crawfish, crayon, cream_pitcher, crescent_roll, crib, crisp_(potato_chip), crock_pot, crossbar, crouton, crow, crowbar, crown, crucifix, cruise_ship, crumb, crutch, cub_(animal), cube, cucumber, cufflink, cup, cupboard, cupcake, curling_iron, curtain, cushion, cylinder, cymbal, dagger, dalmatian, dartboard, date_(fruit), deadbolt, deck_chair, deer, dental_floss, desk, detergent, diaper, diary, die, dinghy, dining_table, dirt_bike, dish, dish_antenna, dishrag, dishtowel, dishwasher, dishwasher_detergent, dispenser, diving_board, dog, dog_collar, doll, dollar, dollhouse, dolphin, domestic_ass, doorknob, doormat, doughnut, dove, dragonfly, drawer, dress, dress_hat, dress_suit, dresser, drill, drone, dropper, drum_(musical_instrument), drumstick, duck, duckling, duct_tape, duffel_bag, dumbbell, dumpster, dustpan, eagle, earphone, earplug, earring, easel, eclair, edible_corn, eel, egg, egg_roll, egg_yolk, eggbeater, eggplant, electric_chair, elephant, elevator_car, elk, envelope, eraser, escargot, eyepatch, falcon, fan, faucet, fedora, ferret, ferry, fig_(fruit), fighter_jet, figurine, file_(tool), file_cabinet, fire_alarm, fire_engine, fire_extinguisher, fire_hose, fireplace, fireplug, first-aid_kit, fish, fish_(food), fishbowl, fishing_rod, flag, flagpole, flamingo, flannel, flap, flash, flashlight, fleece, flip-flop_(sandal), flipper_(footwear), flower_arrangement, flowerpot, flute_glass, foal, folding_chair, food_processor, football_(American), football_helmet, footstool, fork, forklift, freight_car, freshener, frisbee, frog, fruit_juice, frying_pan, fudge, fume_hood, funnel, futon, gag, gameboard, garbage, garbage_truck, garden_hose, gargle, gargoyle, garlic, gasmask, gazelle, gelatin, gemstone, generator, giant_panda, gift_wrap, ginger, giraffe, glass_(drink_container), globe, glove, goat, goggles, goldfish, golf_club, golfcart, gondola_(boat), goose, gorilla, gourd, grape, grater, gravestone, gravy_boat, green_bean, green_onion, griddle, grill, grits, grizzly, grocery_bag, guitar, gull, gun, hair_curler, hair_dryer, hairbrush, hairnet, hairpin, halter_top, ham, hamburger, hammer, hammock, hamper, hamster, hand_glass, hand_towel, handbag, handcart, handcuff, handkerchief, handle, handsaw, hardback_book, harmonium, hat, hatbox, headband, headboard, headlight, headscarf, headset, headstall_(for_horses), heart, heater, helicopter, helmet, heron, highchair, hinge, hippopotamus, hockey_stick, hog, home_plate_(baseball), honey, hook, hookah, hornet, horse, horse_buggy, horse_carriage, hose, hot-air_balloon, hot_sauce, hotplate, hourglass, houseboat, hummingbird, hummus, iPod, ice_maker, ice_pack, ice_skate, icecream, identity_card, igniter, inhaler, inkpad, iron_(for_clothing), ironing_board, jacket, jam, jar, jean, jeep, jelly_bean, jersey, jet_plane, jewel, jewelry, joystick, jumpsuit, kayak, keg, kennel, kettle, key, keycard, kilt, kimono, kitchen_sink, kitchen_table, kite, kitten, kiwi_fruit, knee_pad, knife, knitting_needle, knob, knocker_(on_a_door), koala, lab_coat, ladder, ladle, ladybug, lamb-chop, lamb_(animal), lamp, lamppost, lampshade, lantern, lanyard, laptop_computer, lasagna, latch, lawn_mower, leather, legging_(clothing), legume, lemon, lemonade, lettuce, license_plate, life_buoy, life_jacket, lightbulb, lightning_rod, lime, limousine, lion, lip_balm, liquor, lizard, locker, log, lollipop, loveseat, machine_gun, magazine, magnet, mail_slot, mailbox_(at_home), mallard, mallet, mammoth, manatee, mandarin_orange, manger, manhole, map, marker, martini, mascot, mashed_potato, masher, mask, mast, mat_(gym_equipment), matchbox, mattress, measuring_cup, measuring_stick, meatball, medicine, melon, microphone, microscope, microwave_oven, milestone, milk, milk_can, milkshake, minivan, mint_candy, mirror, mitten, mixer_(kitchen_tool), money, monitor_(computer_equipment) computer_monitor, monkey, mop, motor, motor_scooter, motor_vehicle, motorcycle, mound_(baseball), mouse_(computer_equipment), mousepad, muffin, mug, mushroom, music_stool, musical_instrument, nailfile, napkin, neckerchief, necklace, necktie, needle, nest, newspaper, newsstand, nightshirt, nosebag_(for_animals), noseband_(for_animals), notebook, notepad, nut, nutcracker, oar, octopus_(animal), octopus_(food), oil_lamp, olive_oil, omelet, onion, orange_(fruit), orange_juice, ostrich, ottoman, oven, overalls_(clothing), owl, pacifier, packet, pad, paddle, padlock, paintbrush, painting, pajamas, palette, pan_(for_cooking), pan_(metal_container), pancake, pantyhose, papaya, paper_plate, paper_towel, paperback_book, paperweight, parachute, parakeet, parasail_(sports), parasol, parchment, parka, parking_meter, parrot, passenger_car_(part_of_a_train), passenger_ship, passport, pastry, patty_(food), pea_(food), peach, peanut_butter, pear, peeler_(tool_for_fruit_and_vegetables), pegboard, pelican, pen, pencil, pencil_box, pencil_sharpener, pendulum, penguin, pennant, penny_(coin), pepper, pepper_mill, perfume, persimmon, person, pet, pew_(church_bench), phonebook, phonograph_record, piano, pickle, pickup_truck, pie, pigeon, piggy_bank, pillow, pin_(non_jewelry), pineapple, pinecone, ping-pong_ball, pinwheel, pipe, pipe_bowl, pirate_flag, pistol, pita_(bread), pitcher_(vessel_for_liquid), pitchfork, pizza, place_mat, plastic_bag, plate, platter, playpen, pliers, plow_(farm_equipment), plume, pocket_watch, pocketknife, poker_(fire_stirring_tool), poker_chip, polar_bear, pole, police_cruiser, polo_shirt, poncho, pony, pool_table, pop_(soda), popsicle, postbox_(public), postcard, poster, pot, potato, potholder, pottery, pouch, power_shovel, prawn, pretzel, printer, projectile_(weapon), projector, propeller, prune, pudding, puffer_(fish), puffin, pug-dog, pumpkin, puncher, puppet, puppy, quesadilla, quiche, quilt, rabbit, race_car, racket, radar, radiator, radio_receiver, radish, raft, rag_doll, railcar_(part_of_a_train), raincoat, ram_(animal), raspberry, rat, razorblade, reamer_(juicer), rearview_mirror, receipt, recliner, record_player, reflector, refrigerator, remote_control, rhinoceros, rib_(food), rifle, ring, river_boat, road_map, robe, rocking_chair, rodent, roller_skate, rolling_pin, root_beer, router_(computer_equipment), rubber_band, runner_(carpet), saddle_(on_an_animal), saddle_blanket, saddlebag, safety_pin, sail, salad, salad_plate, salami, salmon_(fish), salmon_(food), salsa, saltshaker, sandal_(type_of_shoe), sandwich, satchel, saucepan, saucer, sausage, sawhorse, saxophone, scale_(measuring_instrument), scarecrow, scarf, school_bus, scissors, scoreboard, scraper, screwdriver, scrubbing_brush, sculpture, seabird, seahorse, seaplane, seashell, sewing_machine, shaker, shampoo, shark, sharpener, shaver_(electric), shaving_cream, shawl, shears, sheep, shepherd_dog, sherbert, shield, shirt, shoe, shopping_bag, shopping_cart, short_pants, shot_glass, shoulder_bag, shovel, shower_cap, shower_curtain, shower_head, shredder_(for_paper), signboard, silo, sink, skateboard, skewer, ski, ski_boot, ski_parka, ski_pole, skirt, skullcap, sled, sleeping_bag, slide, sling_(bandage), slipper_(footwear), smoothie, snake, snowboard, snowman, snowmobile, soap, soccer_ball, sock, sofa, sofa_bed, softball, solar_array, sombrero, soup, soup_bowl, soupspoon, sour_cream, soya_milk, space_shuttle, sparkler_(fireworks), spatula, speaker_(stero_equipment), spear, spectacles, spice_rack, spider, sponge, spoon, sportswear, spotlight, squid_(food), squirrel, stagecoach, stapler_(stapling_machine), starfish, statue_(sculpture), steak_(food), steak_knife, steering_wheel, step_stool, stepladder, stereo_(sound_system), stew, stirrer, stirrup, stool, stop_sign, stove, strainer, strap, straw_(for_drinking), strawberry, street_sign, streetlight, string_cheese, stylus, subwoofer, sugar_bowl, sugarcane_(plant), suit_(clothing), suitcase, sunflower, sunglasses, sunhat, surfboard, sushi, suspenders, sweat_pants, sweatband, sweater, sweatshirt, sweet_potato, swimsuit, sword, syringe, table, table-tennis_table, table_lamp, tablecloth, tachometer, taco, tag, taillight, tambourine, tank_(storage_vessel), tank_top_(clothing), tape_(sticky_cloth_or_paper), tape_measure, tapestry, tarp, tartan, tassel, tea_bag, teacup, teakettle, teapot, teddy_bear, telephone, telephone_booth, telephone_pole, telephoto_lens, television_camera, television_set, tennis_ball, tennis_racket, tequila, thermometer, thermos_bottle, thermostat, thimble, thread, thumbtack, tiara, tiger, tights_(clothing), timer, tinfoil, tinsel, tissue_paper, toast_(food), toaster, toaster_oven, tobacco_pipe, toilet, toilet_tissue, tomato, tongs, toolbox, toothbrush, toothpaste, toothpick, tortilla, tote_bag, tow_truck, towel, towel_rack, toy, tractor_(farm_equipment), traffic_light, trailer_truck, train_(railroad_vehicle), trampoline, trash_can, tray, trench_coat, triangle_(musical_instrument), tricycle, tripod, trophy_cup, trousers, truck, truffle_(chocolate), trunk, turban, turkey_(food), turnip, turtle, turtleneck_(clothing), tux, typewriter, umbrella, underdrawers, underwear, unicycle, urinal, urn, vacuum_cleaner, vase, vat, veil, vending_machine, vent, vest, videotape, vinegar, violin, visor, vodka, volleyball, vulture, waffle, waffle_iron, wagon, wagon_wheel, walking_cane, walking_stick, wall_clock, wall_socket, wallet, walrus, wardrobe, washbasin, watch, water_bottle, water_cooler, water_faucet, water_gun, water_heater, water_jug, water_scooter, water_ski, water_tower, watering_can, watermelon, weathervane, webcam, wedding_cake, wedding_ring, wet_suit, wheel, wheelchair, whipped_cream, whistle, wig, wind_chime, windmill, window_box_(for_plants), windshield_wiper, windsock, wine_bottle, wine_bucket, wineglass, wok, wolf, wooden_leg, wooden_spoon, wreath, wrench, wristband, wristlet, yacht, yogurt, yoke_(animal_equipment), zebra, zucchini.

License

Released under Creative Commons Attribution 4.0. When you use this data, please credit Gupta, Dollar, Girshick - Facebook AI Research (CVPR 2019).

Source and attribution

This dataset is derived from LVIS, created by Gupta, Dollar, Girshick - Facebook AI Research (CVPR 2019), originally licensed Creative Commons Attribution 4.0. We are grateful to the original authors. If you use this data, please cite the original source above.


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