Datasets:
image image | objects dict | segmentation list |
|---|---|---|
{
"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,
... |
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.
Published from Pictograph - annotate, train, and deploy from one API.
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