---
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: 6495083614.0
---
# Quick!Draw! Dataset (per-row bin format)

This is the full 50M-row dataset from [QuickDraw! dataset](https://github.com/googlecreativelab/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
```