metadata
dataset_info:
features:
- 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
'20': baseball bat
'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
'131': garden hose
'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
'157': house plant
'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
- name: packed_drawing
dtype: binary
splits:
- name: train
num_bytes: 5196066788.157136
num_examples: 40341012
- name: test
num_bytes: 1299016825.8428645
num_examples: 10085254
download_size: 6290637578
dataset_size: 6495083614
Quick!Draw! Dataset (per-row bin format)
This is the full 50M-row dataset from QuickDraw! dataset. The row for each drawing contains a byte-encoded packed representation of the drawing and data, which you can unpack using the following snippet:
def unpack_drawing(file_handle):
key_id, = unpack('Q', file_handle.read(8))
country_code, = unpack('2s', file_handle.read(2))
recognized, = unpack('b', file_handle.read(1))
timestamp, = unpack('I', file_handle.read(4))
n_strokes, = unpack('H', file_handle.read(2))
image = []
n_bytes = 17
for i in range(n_strokes):
n_points, = unpack('H', file_handle.read(2))
fmt = str(n_points) + 'B'
x = unpack(fmt, file_handle.read(n_points))
y = unpack(fmt, file_handle.read(n_points))
image.append((x, y))
n_bytes += 2 + 2*n_points
result = {
'key_id': key_id,
'country_code': country_code,
'recognized': recognized,
'timestamp': timestamp,
'image': image,
}
return result
The image in the above is still in line vector format. To convert render this to a raster image (I recommend you do this on-the-fly in a pre-processor):
# packed bin -> RGB PIL
def binToPIL(packed_drawing):
padding = 8
radius = 7
scale = (224.0-(2*padding)) / 256
unpacked = unpack_drawing(io.BytesIO(packed_drawing))
unpacked_image = unpacked['image']
image = np.full((224,224), 255, np.uint8)
for stroke in unpacked['image']:
prevX = round(stroke[0][0]*scale)
prevY = round(stroke[1][0]*scale)
for i in range(1, len(stroke[0])):
x = round(stroke[0][i]*scale)
y = round(stroke[1][i]*scale)
cv2.line(image, (padding+prevX, padding+prevY), (padding+x, padding+y), 0, radius, -1)
prevX = x
prevY = y
pilImage = Image.fromarray(image).convert("RGB")
return pilImage