size_categories:10K<n<100K format:parquet modality:image library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': The Eiffel Tower
          '1': The Great Wall of China
          '2': The Mona Lisa
          '3': aircraft carrier
          '4': airplane
          '5': alarm clock
          '6': ambulance
          '7': angel
          '8': animal migration
          '9': ant
          '10': anvil
          '11': apple
          '12': arm
          '13': asparagus
          '14': axe
          '15': backpack
          '16': banana
          '17': bandage
          '18': barn
          '19': baseball bat
          '20': baseball
          '21': basket
          '22': basketball
          '23': bat
          '24': bathtub
          '25': beach
          '26': bear
          '27': beard
          '28': bed
          '29': bee
          '30': belt
          '31': bench
          '32': bicycle
          '33': binoculars
          '34': bird
          '35': birthday cake
          '36': blackberry
          '37': blueberry
          '38': book
          '39': boomerang
          '40': bottlecap
          '41': bowtie
          '42': bracelet
          '43': brain
          '44': bread
          '45': bridge
          '46': broccoli
          '47': broom
          '48': bucket
          '49': bulldozer
          '50': bus
          '51': bush
          '52': butterfly
          '53': cactus
          '54': cake
          '55': calculator
          '56': calendar
          '57': camel
          '58': camera
          '59': camouflage
          '60': campfire
          '61': candle
          '62': cannon
          '63': canoe
          '64': car
          '65': carrot
          '66': castle
          '67': cat
          '68': ceiling fan
          '69': cell phone
          '70': cello
          '71': chair
          '72': chandelier
          '73': church
          '74': circle
          '75': clarinet
          '76': clock
          '77': cloud
          '78': coffee cup
          '79': compass
          '80': computer
          '81': cookie
          '82': cooler
          '83': couch
          '84': cow
          '85': crab
          '86': crayon
          '87': crocodile
          '88': crown
          '89': cruise ship
          '90': cup
          '91': diamond
          '92': dishwasher
          '93': diving board
          '94': dog
          '95': dolphin
          '96': donut
          '97': door
          '98': dragon
          '99': dresser
          '100': drill
          '101': drums
          '102': duck
          '103': dumbbell
          '104': ear
          '105': elbow
          '106': elephant
          '107': envelope
          '108': eraser
          '109': eye
          '110': eyeglasses
          '111': face
          '112': fan
          '113': feather
          '114': fence
          '115': finger
          '116': fire hydrant
          '117': fireplace
          '118': firetruck
          '119': fish
          '120': flamingo
          '121': flashlight
          '122': flip flops
          '123': floor lamp
          '124': flower
          '125': flying saucer
          '126': foot
          '127': fork
          '128': frog
          '129': frying pan
          '130': garden hose
          '131': garden
          '132': giraffe
          '133': goatee
          '134': golf club
          '135': grapes
          '136': grass
          '137': guitar
          '138': hamburger
          '139': hammer
          '140': hand
          '141': harp
          '142': hat
          '143': headphones
          '144': hedgehog
          '145': helicopter
          '146': helmet
          '147': hexagon
          '148': hockey puck
          '149': hockey stick
          '150': horse
          '151': hospital
          '152': hot air balloon
          '153': hot dog
          '154': hot tub
          '155': hourglass
          '156': house plant
          '157': house
          '158': hurricane
          '159': ice cream
          '160': jacket
          '161': jail
          '162': kangaroo
          '163': key
          '164': keyboard
          '165': knee
          '166': knife
          '167': ladder
          '168': lantern
          '169': laptop
          '170': leaf
          '171': leg
          '172': light bulb
          '173': lighter
          '174': lighthouse
          '175': lightning
          '176': line
          '177': lion
          '178': lipstick
          '179': lobster
          '180': lollipop
          '181': mailbox
          '182': map
          '183': marker
          '184': matches
          '185': megaphone
          '186': mermaid
          '187': microphone
          '188': microwave
          '189': monkey
          '190': moon
          '191': mosquito
          '192': motorbike
          '193': mountain
          '194': mouse
          '195': moustache
          '196': mouth
          '197': mug
          '198': mushroom
          '199': nail
          '200': necklace
          '201': nose
          '202': ocean
          '203': octagon
          '204': octopus
          '205': onion
          '206': oven
          '207': owl
          '208': paint can
          '209': paintbrush
          '210': palm tree
          '211': panda
          '212': pants
          '213': paper clip
          '214': parachute
          '215': parrot
          '216': passport
          '217': peanut
          '218': pear
          '219': peas
          '220': pencil
          '221': penguin
          '222': piano
          '223': pickup truck
          '224': picture frame
          '225': pig
          '226': pillow
          '227': pineapple
          '228': pizza
          '229': pliers
          '230': police car
          '231': pond
          '232': pool
          '233': popsicle
          '234': postcard
          '235': potato
          '236': power outlet
          '237': purse
          '238': rabbit
          '239': raccoon
          '240': radio
          '241': rain
          '242': rainbow
          '243': rake
          '244': remote control
          '245': rhinoceros
          '246': rifle
          '247': river
          '248': roller coaster
          '249': rollerskates
          '250': sailboat
          '251': sandwich
          '252': saw
          '253': saxophone
          '254': school bus
          '255': scissors
          '256': scorpion
          '257': screwdriver
          '258': sea turtle
          '259': see saw
          '260': shark
          '261': sheep
          '262': shoe
          '263': shorts
          '264': shovel
          '265': sink
          '266': skateboard
          '267': skull
          '268': skyscraper
          '269': sleeping bag
          '270': smiley face
          '271': snail
          '272': snake
          '273': snorkel
          '274': snowflake
          '275': snowman
          '276': soccer ball
          '277': sock
          '278': speedboat
          '279': spider
          '280': spoon
          '281': spreadsheet
          '282': square
          '283': squiggle
          '284': squirrel
          '285': stairs
          '286': star
          '287': steak
          '288': stereo
          '289': stethoscope
          '290': stitches
          '291': stop sign
          '292': stove
          '293': strawberry
          '294': streetlight
          '295': string bean
          '296': submarine
          '297': suitcase
          '298': sun
          '299': swan
          '300': sweater
          '301': swing set
          '302': sword
          '303': syringe
          '304': t-shirt
          '305': table
          '306': teapot
          '307': teddy-bear
          '308': telephone
          '309': television
          '310': tennis racquet
          '311': tent
          '312': tiger
          '313': toaster
          '314': toe
          '315': toilet
          '316': tooth
          '317': toothbrush
          '318': toothpaste
          '319': tornado
          '320': tractor
          '321': traffic light
          '322': train
          '323': tree
          '324': triangle
          '325': trombone
          '326': truck
          '327': trumpet
          '328': umbrella
          '329': underwear
          '330': van
          '331': vase
          '332': violin
          '333': washing machine
          '334': watermelon
          '335': waterslide
          '336': whale
          '337': wheel
          '338': windmill
          '339': wine bottle
          '340': wine glass
          '341': wristwatch
          '342': yoga
          '343': zebra
          '344': zigzag
  splits:
  - name: train
    num_bytes: 72173714.5
    num_examples: 34500
  download_size: 70106975
  dataset_size: 72173714.5
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Dataset Card for Quick, Draw! Dataset

This dataset card aims to provide comprehensive information about the Quick, Draw! dataset, a collection of hand-drawn sketches used for training and evaluating sketch classification models.

## Dataset Details

### Dataset Description

The Quick, Draw! dataset is a large-scale collection of hand-drawn sketches curated by Google Creative Lab. The dataset includes over 50 million unique sketches across 345 object categories, contributed by participants from around the world. The primary purpose of this dataset is to facilitate research in the field of computer vision, particularly for tasks related to sketch recognition and classification.

- **Curated by:** Google Creative Lab
- **Shared by [optional]:** Google

### Dataset Sources

- **Source:** [The Quick, Draw! Dataset](https://github.com/googlecreativelab/quickdraw-dataset)

## Uses

### Direct Use

The dataset is intended for use in developing and evaluating sketch recognition algorithms. It is suitable for tasks such as:

- Training sketch classification models
- Evaluating the performance of sketch recognition systems
- Conducting research in computer vision and machine learning related to hand-drawn images

### Out-of-Scope Use

The dataset is not suitable for use cases that require high-resolution images or photographs. It is also not intended for tasks unrelated to sketch recognition, such as natural image classification.

## Original Dataset Structure

The dataset is organized into categories, each containing a collection of hand-drawn sketches. Each sketch is a black-and-white image representing an object from one of the predefined categories.

- **Number of Categories:** 345
- **Number of Sketches:** 50 million

### Dataset Splits

In this dataset, the Quick, Draw! dataset is provided as a single training set without predefined splits for training, validation, or testing. Due to the large size of the original dataset, I randomly selected 100 samples per category to train within limited resources, resulting in a total of 34,500 sketches.

- **Train Set:**
  - **Number of Examples:** 34,500
