metadata
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
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
Citation
BibTeX:
@article{ha2017neural,
title={A Neural Representation of Sketch Drawings},
author={Ha, David and Eck, Douglas},
journal={arXiv preprint arXiv:1704.03477},
year={2017}
}