size_categories:1K<n<10K format:parquet modality:image modality:timeseries library:datasets library:dask library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Person
          '1': Dairy Product
          '2': Table
          '3': Cake
          '4': Pastry
          '5': Tie
          '6': Suit
          '7': Shotgun
          '8': Helmet
          '9': Curtain
          '10': Human arm
          '11': Backpack
          '12': Vehicle
          '13': Food
          '14': Tree
          '15': Footwear
          '16': Shirt
          '17': Human ear
          '18': Human head
          '19': Toy
          '20': Building
          '21': Book
          '22': Human body
          '23': Handbag
          '24': Human face
          '25': Human hand
          '26': Window
          '27': Mammal
          '28': Plant
          '29': Fedora
          '30': Trousers
          '31': Light bulb
          '32': Picture frame
          '33': Office building
          '34': Wine
          '35': Wine glass
          '36': Chair
          '37': Coffee cup
          '38': Coffee table
          '39': Countertop
          '40': Microwave oven
          '41': Football
          '42': Sports uniform
          '43': Tower
          '44': Swimwear
          '45': Skyscraper
          '46': Sunglasses
          '47': Shorts
          '48': Truck
          '49': Wheel
          '50': Van
          '51': Tableware
          '52': Man
          '53': Poster
          '54': Jeans
          '55': Desk
          '56': Television
          '57': Clothing
          '58': Car
          '59': Rifle
          '60': Jacket
          '61': Airplane
          '62': Guitar
          '63': Violin
          '64': Drum
          '65': Microphone
          '66': Train
          '67': Furniture
          '68': Human eye
          '69': Human hair
          '70': Musical instrument
          '71': Adhesive tape
          '72': Cosmetics
          '73': Flag
          '74': Glasses
          '75': Hat
          '76': Traffic light
          '77': Bus
          '78': Street light
          '79': Human foot
          '80': Human leg
          '81': Cabinetry
          '82': Gas stove
          '83': Cupboard
          '84': Kitchen appliance
          '85': Stretcher
          '86': Sculpture
          '87': Couch
          '88': Stool
          '89': Coat
          '90': Door
          '91': Sombrero
          '92': Waste container
          '93': Doll
          '94': Miniskirt
          '95': Baseball bat
          '96': Hiking equipment
          '97': Refrigerator
          '98': Ice cream
          '99': Land vehicle
          '100': Human mouth
          '101': Tent
          '102': Animal
          '103': Woman
          '104': Mixing bowl
          '105': Kitchen utensil
          '106': Bowl
          '107': Surfboard
          '108': Human nose
          '109': Office supplies
          '110': Dog
          '111': Bench
          '112': Saucer
          '113': Plate
          '114': Spoon
          '115': Fork
          '116': Sun hat
          '117': Skirt
          '118': Balloon
          '119': Bicycle
          '120': Bicycle wheel
          '121': Bicycle helmet
          '122': Whiteboard
          '123': Umbrella
          '124': Christmas tree
          '125': Paddle
          '126': Canoe
          '127': Bookcase
          '128': Bottle
          '129': House
          '130': Camera
          '131': Cocktail
          '132': Tin can
          '133': Brassiere
          '134': Bronze sculpture
          '135': Traffic sign
          '136': Horse
          '137': Human beard
          '138': Cowboy hat
          '139': Drink
          '140': Lamp
          '141': Laptop
          '142': Computer keyboard
          '143': Parachute
          '144': Beer
          '145': Organ (Musical Instrument)
          '146': Bull
          '147': Juice
          '148': Knife
          '149': Houseplant
          '150': Flowerpot
          '151': Tap
          '152': Bathtub
          '153': Glove
          '154': Sports equipment
          '155': Vehicle registration plate
          '156': Taxi
          '157': Dress
          '158': Fountain
          '159': Sink
          '160': Mirror
          '161': Bathroom accessory
          '162': Flower
          '163': Piano
          '164': Boat
          '165': Bathroom cabinet
          '166': Chest of drawers
          '167': Drawer
          '168': Motorcycle
          '169': Barrel
          '170': Computer monitor
          '171': Kitchen & dining room table
          '172': Mug
          '173': Fruit
          '174': Pumpkin
          '175': Stairs
          '176': Goggles
          '177': Lighthouse
          '178': Ball
          '179': Bowling equipment
          '180': Snowboard
          '181': Pig
          '182': Insect
          '183': Spider
          '184': Sushi
          '185': Sock
          '186': Watercraft
          '187': Tortoise
          '188': Marine mammal
          '189': Lantern
          '190': Snack
          '191': Plastic bag
          '192': Clock
          '193': Watch
          '194': Mule
          '195': Billboard
          '196': Football helmet
          '197': Palm tree
          '198': Box
          '199': Sofa bed
          '200': Shelf
          '201': Cat
          '202': Tire
          '203': Swimming pool
          '204': Fast food
          '205': Cheese
          '206': Hamburger
          '207': Balance beam
          '208': Candy
          '209': Gondola
          '210': Nightstand
          '211': Pillow
          '212': Bed
          '213': Studio couch
          '214': Cucumber
          '215': Zucchini
          '216': Golf ball
          '217': Dinosaur
          '218': Candle
          '219': Bread
          '220': Sandwich
          '221': Home appliance
          '222': Tool
          '223': Mobile phone
          '224': Weapon
  - name: bbox
    sequence: float32
  splits:
  - name: train
    num_bytes: 1735690664.16
    num_examples: 5386
  download_size: 168160065
  dataset_size: 1735690664.16
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
