image
image
label
class label
87 classes
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
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0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
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0aircraft carrier
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0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier
0aircraft carrier

Dataset Card for Quick, Draw!

Dataset Summary

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located.

Supported Tasks and Leaderboards

  • image-classification: The goal of this task is to classify a given sketch into one of 345 classes. The (closed) leaderboard for this task is available here.

Languages

English.

Dataset Structure

Data Instances

raw

A data point comprises a drawing and its metadata.

{
  'key_id': '5475678961008640',
  'word': 0,
  'recognized': True,
  'timestamp': datetime.datetime(2017, 3, 28, 13, 28, 0, 851730),
  'countrycode': 'MY',
  'drawing': {
    'x': [[379.0, 380.0, 381.0, 381.0, 381.0, 381.0, 382.0], [362.0, 368.0, 375.0, 380.0, 388.0, 393.0, 399.0, 404.0, 409.0, 410.0, 410.0, 405.0, 397.0, 392.0, 384.0, 377.0, 370.0, 363.0, 356.0, 348.0, 342.0, 336.0, 333.0], ..., [477.0, 473.0, 471.0, 469.0, 468.0, 466.0, 464.0, 462.0, 461.0, 469.0, 475.0, 483.0, 491.0, 499.0, 510.0, 521.0, 531.0, 540.0, 548.0, 558.0, 566.0, 576.0, 583.0, 590.0, 595.0, 598.0, 597.0, 596.0, 594.0, 592.0, 590.0, 589.0, 588.0, 586.0]],
    'y': [[1.0, 7.0, 15.0, 21.0, 27.0, 32.0, 32.0], [17.0, 17.0, 17.0, 17.0, 16.0, 16.0, 16.0, 16.0, 18.0, 23.0, 29.0, 32.0, 32.0, 32.0, 29.0, 27.0, 25.0, 23.0, 21.0, 19.0, 17.0, 16.0, 14.0], ..., [151.0, 146.0, 139.0, 131.0, 125.0, 119.0, 113.0, 107.0, 102.0, 99.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 98.0, 100.0, 102.0, 104.0, 105.0, 110.0, 115.0, 121.0, 126.0, 131.0, 137.0, 142.0, 148.0, 150.0]],
    't': [[0, 84, 100, 116, 132, 148, 260], [573, 636, 652, 660, 676, 684, 701, 724, 796, 838, 860, 956, 973, 979, 989, 995, 1005, 1012, 1020, 1028, 1036, 1053, 1118], ..., [8349, 8446, 8468, 8484, 8500, 8516, 8541, 8557, 8573, 8685, 8693, 8702, 8710, 8718, 8724, 8732, 8741, 8748, 8757, 8764, 8773, 8780, 8788, 8797, 8804, 8965, 8996, 9029, 9045, 9061, 9076, 9092, 9109, 9167]]
  }
}

preprocessed_simplified_drawings

The simplified version of the dataset generated from the raw data with the simplified vectors, removed timing information, and the data positioned and scaled into a 256x256 region. The simplification process was: 1.Align the drawing to the top-left corner, to have minimum values of 0. 2.Uniformly scale the drawing, to have a maximum value of 255. 3.Resample all strokes with a 1 pixel spacing. 4.Simplify all strokes using the Ramer-Douglas-Peucker algorithm with an epsilon value of 2.0.

{
  'key_id': '5475678961008640',
  'word': 0,
  'recognized': True,
  'timestamp': datetime.datetime(2017, 3, 28, 15, 28),
  'countrycode': 'MY',
  'drawing': {
    'x': [[31, 32], [27, 37, 38, 35, 21], [25, 28, 38, 39], [33, 34, 32], [5, 188, 254, 251, 241, 185, 45, 9, 0], [35, 35, 43, 125, 126], [35, 76, 80, 77], [53, 50, 54, 80, 78]],
    'y': [[0, 7], [4, 4, 6, 7, 3], [5, 10, 10, 7], [4, 33, 44], [50, 50, 54, 83, 86, 90, 86, 77, 52], [85, 91, 92, 96, 90], [35, 37, 41, 47], [34, 23, 22, 23, 34]]
  }
}

preprocessed_bitmaps (default configuration)

This configuration contains the 28x28 grayscale bitmap images that were generated from the simplified data, but are aligned to the center of the drawing's bounding box rather than the top-left corner. The code that was used for generation is available here.

{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x10B5B102828>,
  'label': 0
}

sketch_rnn and sketch_rnn_full

The sketch_rnn_full configuration stores the data in the format suitable for inputs into a recurrent neural network and was used for for training the Sketch-RNN model. Unlike sketch_rnn where the samples have been randomly selected from each category, the sketch_rnn_full configuration contains the full data for each category.

{
  'word': 0,
  'drawing': [[132, 0, 0], [23, 4, 0], [61, 1, 0], [76, 0, 0], [22, -4, 0], [152, 0, 0], [50, -5, 0], [36, -10, 0], [8, 26, 0], [0, 69, 0], [-2, 11, 0], [-8, 10, 0], [-56, 24, 0], [-23, 14, 0], [-99, 40, 0], [-45, 6, 0], [-21, 6, 0], [-170, 2, 0], [-81, 0, 0], [-29, -9, 0], [-94, -19, 0], [-48, -24, 0], [-6, -16, 0], [2, -36, 0], [7, -29, 0], [23, -45, 0], [13, -6, 0], [41, -8, 0], [42, -2, 1], [392, 38, 0], [2, 19, 0], [11, 33, 0], [13, 0, 0], [24, -9, 0], [26, -27, 0], [0, -14, 0], [-8, -10, 0], [-18, -5, 0], [-14, 1, 0], [-23, 4, 0], [-21, 12, 1], [-152, 18, 0], [10, 46, 0], [26, 6, 0], [38, 0, 0], [31, -2, 0], [7, -2, 0], [4, -6, 0], [-10, -21, 0], [-2, -33, 0], [-6, -11, 0], [-46, 1, 0], [-39, 18, 0], [-19, 4, 1], [-122, 0, 0], [-2, 38, 0], [4, 16, 0], [6, 4, 0], [78, 0, 0], [4, -8, 0], [-8, -36, 0], [0, -22, 0], [-6, -2, 0], [-32, 14, 0], [-58, 13, 1], [-96, -12, 0], [-10, 27, 0], [2, 32, 0], [102, 0, 0], [1, -7, 0], [-27, -17, 0], [-4, -6, 0], [-1, -34, 0], [-64, 8, 1], [129, -138, 0], [-108, 0, 0], [-8, 12, 0], [-1, 15, 0], [12, 15, 0], [20, 5, 0], [61, -3, 0], [24, 6, 0], [19, 0, 0], [5, -4, 0], [2, 14, 1]]
}

Data Fields

raw

  • key_id: A unique identifier across all drawings.
  • word: Category the player was prompted to draw.
  • recognized: Whether the word was recognized by the game.
  • timestamp: When the drawing was created.
  • countrycode: A two letter country code (ISO 3166-1 alpha-2) of where the player was located.
  • drawing: A dictionary where x and y are the pixel coordinates, and t is the time in milliseconds since the first point. x and y are real-valued while t is an integer. x, y and t match in lenght and are represented as lists of lists where each sublist corresponds to a single stroke. The raw drawings can have vastly different bounding boxes and number of points due to the different devices used for display and input.

preprocessed_simplified_drawings

  • key_id: A unique identifier across all drawings.
  • word: Category the player was prompted to draw.
  • recognized: Whether the word was recognized by the game.
  • timestamp: When the drawing was created.
  • countrycode: A two letter country code (ISO 3166-1 alpha-2) of where the player was located.
  • drawing: A simplified drawing represented as a dictionary where x and y are the pixel coordinates. The simplification processed is described in the Data Instances section.

preprocessed_bitmaps (default configuration)

  • image: A PIL.Image.Image object containing the 28x28 grayscale bitmap. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
  • label: Category the player was prompted to draw.
Click here to see the full class labels mapping:
id class
0 aircraft carrier
1 airplane
2 alarm clock
3 ambulance
4 angel
5 animal migration
6 ant
7 anvil
8 apple
9 arm
10 asparagus
11 axe
12 backpack
13 banana
14 bandage
15 barn
16 baseball bat
17 baseball
18 basket
19 basketball
20 bat
21 bathtub
22 beach
23 bear
24 beard
25 bed
26 bee
27 belt
28 bench
29 bicycle
30 binoculars
31 bird
32 birthday cake
33 blackberry
34 blueberry
35 book
36 boomerang
37 bottlecap
38 bowtie
39 bracelet
40 brain
41 bread
42 bridge
43 broccoli
44 broom
45 bucket
46 bulldozer
47 bus
48 bush
49 butterfly
50 cactus
51 cake
52 calculator
53 calendar
54 camel
55 camera
56 camouflage
57 campfire
58 candle
59 cannon
60 canoe
61 car
62 carrot
63 castle
64 cat
65 ceiling fan
66 cell phone
67 cello
68 chair
69 chandelier
70 church
71 circle
72 clarinet
73 clock
74 cloud
75 coffee cup
76 compass
77 computer
78 cookie
79 cooler
80 couch
81 cow
82 crab
83 crayon
84 crocodile
85 crown
86 cruise ship
87 cup
88 diamond
89 dishwasher
90 diving board
91 dog
92 dolphin
93 donut
94 door
95 dragon
96 dresser
97 drill
98 drums
99 duck
100 dumbbell
101 ear
102 elbow
103 elephant
104 envelope
105 eraser
106 eye
107 eyeglasses
108 face
109 fan
110 feather
111 fence
112 finger
113 fire hydrant
114 fireplace
115 firetruck
116 fish
117 flamingo
118 flashlight
119 flip flops
120 floor lamp
121 flower
122 flying saucer
123 foot
124 fork
125 frog
126 frying pan
127 garden hose
128 garden
129 giraffe
130 goatee
131 golf club
132 grapes
133 grass
134 guitar
135 hamburger
136 hammer
137 hand
138 harp
139 hat
140 headphones
141 hedgehog
142 helicopter
143 helmet
144 hexagon
145 hockey puck
146 hockey stick
147 horse
148 hospital
149 hot air balloon
150 hot dog
151 hot tub
152 hourglass
153 house plant
154 house
155 hurricane
156 ice cream
157 jacket
158 jail
159 kangaroo
160 key
161 keyboard
162 knee
163 knife
164 ladder
165 lantern
166 laptop
167 leaf
168 leg
169 light bulb
170 lighter
171 lighthouse
172 lightning
173 line
174 lion
175 lipstick
176 lobster
177 lollipop
178 mailbox
179 map
180 marker
181 matches
182 megaphone
183 mermaid
184 microphone
185 microwave
186 monkey
187 moon
188 mosquito
189 motorbike
190 mountain
191 mouse
192 moustache
193 mouth
194 mug
195 mushroom
196 nail
197 necklace
198 nose
199 ocean
200 octagon
201 octopus
202 onion
203 oven
204 owl
205 paint can
206 paintbrush
207 palm tree
208 panda
209 pants
210 paper clip
211 parachute
212 parrot
213 passport
214 peanut
215 pear
216 peas
217 pencil
218 penguin
219 piano
220 pickup truck
221 picture frame
222 pig
223 pillow
224 pineapple
225 pizza
226 pliers
227 police car
228 pond
229 pool
230 popsicle
231 postcard
232 potato
233 power outlet
234 purse
235 rabbit
236 raccoon
237 radio
238 rain
239 rainbow
240 rake
241 remote control
242 rhinoceros
243 rifle
244 river
245 roller coaster
246 rollerskates
247 sailboat
248 sandwich
249 saw
250 saxophone
251 school bus
252 scissors
253 scorpion
254 screwdriver
255 sea turtle
256 see saw
257 shark
258 sheep
259 shoe
260 shorts
261 shovel
262 sink
263 skateboard
264 skull
265 skyscraper
266 sleeping bag
267 smiley face
268 snail
269 snake
270 snorkel
271 snowflake
272 snowman
273 soccer ball
274 sock
275 speedboat
276 spider
277 spoon
278 spreadsheet
279 square
280 squiggle
281 squirrel
282 stairs
283 star
284 steak
285 stereo
286 stethoscope
287 stitches
288 stop sign
289 stove
290 strawberry
291 streetlight
292 string bean
293 submarine
294 suitcase
295 sun
296 swan
297 sweater
298 swing set
299 sword
300 syringe
301 t-shirt
302 table
303 teapot
304 teddy-bear
305 telephone
306 television
307 tennis racquet
308 tent
309 The Eiffel Tower
310 The Great Wall of China
311 The Mona Lisa
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

sketch_rnn and sketch_rnn_full

  • word: Category the player was prompted to draw.
  • drawing: An array of strokes. Strokes are represented as 3-tuples consisting of x-offset, y-offset, and a binary variable which is 1 if the pen is lifted between this position and the next, and 0 otherwise.
Click here to see the code for visualizing drawings in Jupyter Notebook or Google Colab:
import numpy as np
import svgwrite  # pip install svgwrite
from IPython.display import SVG, display

def draw_strokes(drawing, factor=0.045):
  """Displays vector drawing as SVG.

  Args:
    drawing: a list of strokes represented as 3-tuples
    factor: scaling factor. The smaller the scaling factor, the bigger the SVG picture and vice versa.

  """
  def get_bounds(data, factor):
    """Return bounds of data."""
    min_x = 0
    max_x = 0
    min_y = 0
    max_y = 0

    abs_x = 0
    abs_y = 0
    for i in range(len(data)):
      x = float(data[i, 0]) / factor
      y = float(data[i, 1]) / factor
      abs_x += x
      abs_y += y
      min_x = min(min_x, abs_x)
      min_y = min(min_y, abs_y)
      max_x = max(max_x, abs_x)
      max_y = max(max_y, abs_y)

    return (min_x, max_x, min_y, max_y)

  data = np.array(drawing)
  min_x, max_x, min_y, max_y = get_bounds(data, factor)
  dims = (50 + max_x - min_x, 50 + max_y - min_y)
  dwg = svgwrite.Drawing(size=dims)
  dwg.add(dwg.rect(insert=(0, 0), size=dims,fill='white'))
  lift_pen = 1
  abs_x = 25 - min_x
  abs_y = 25 - min_y
  p = "M%s,%s " % (abs_x, abs_y)
  command = "m"
  for i in range(len(data)):
    if (lift_pen == 1):
      command = "m"
    elif (command != "l"):
      command = "l"
    else:
      command = ""
    x = float(data[i,0])/factor
    y = float(data[i,1])/factor
    lift_pen = data[i, 2]
    p += command+str(x)+","+str(y)+" "
  the_color = "black"
  stroke_width = 1
  dwg.add(dwg.path(p).stroke(the_color,stroke_width).fill("none"))
  display(SVG(dwg.tostring()))

Note: Sketch-RNN takes for input strokes represented as 5-tuples with drawings padded to a common maximum length and prefixed by the special start token [0, 0, 1, 0, 0]. The 5-tuple representation consists of x-offset, y-offset, and p_1, p_2, p_3, a binary one-hot vector of 3 possible pen states: pen down, pen up, end of sketch. More precisely, the first two elements are the offset distance in the x and y directions of the pen from the previous point. The last 3 elements represents a binary one-hot vector of 3 possible states. The first pen state, p1, indicates that the pen is currently touching the paper, and that a line will be drawn connecting the next point with the current point. The second pen state, p2, indicates that the pen will be lifted from the paper after the current point, and that no line will be drawn next. The final pen state, p3, indicates that the drawing has ended, and subsequent points, including the current point, will not be rendered.

Click here to see the code for converting drawings to Sketch-RNN input format:
def to_sketch_rnn_format(drawing, max_len):
  """Converts a drawing to Sketch-RNN input format.

  Args:
    drawing: a list of strokes represented as 3-tuples
    max_len: maximum common length of all drawings

  Returns:
    NumPy array
  """
  drawing = np.array(drawing)
  result = np.zeros((max_len, 5), dtype=float)
  l = len(drawing)
  assert l <= max_len
  result[0:l, 0:2] = drawing[:, 0:2]
  result[0:l, 3] = drawing[:, 2]
  result[0:l, 2] = 1 - result[0:l, 3]
  result[l:, 4] = 1
  # Prepend special start token
  result = np.vstack([[0, 0, 1, 0, 0], result])
  return result

Data Splits

In the configurations raw, preprocessed_simplified_drawings and preprocessed_bitamps (default configuration), all the data is contained in the training set, which has 50426266 examples.

sketch_rnn and sketch_rnn_full have the data split into training, validation and test split. In the sketch_rnn configuration, 75K samples (70K Training, 2.5K Validation, 2.5K Test) have been randomly selected from each category. Therefore, the training set contains 24150000 examples, the validation set 862500 examples and the test set 862500 examples. The sketch_rnn_full configuration has the full (training) data for each category, which leads to the training set having 43988874 examples, the validation set 862500 and the test set 862500 examples.

Dataset Creation

Curation Rationale

From the GitHub repository:

The Quick Draw Dataset is a collection of 50 million drawings across 345 categories, contributed by players of the game Quick, Draw!. The drawings were captured as timestamped vectors, tagged with metadata including what the player was asked to draw and in which country the player was located. You can browse the recognized drawings on quickdraw.withgoogle.com/data.

We're sharing them here for developers, researchers, and artists to explore, study, and learn from

Source Data

Initial Data Collection and Normalization

This dataset contains vector drawings obtained from Quick, Draw!, an online game where the players are asked to draw objects belonging to a particular object class in less than 20 seconds.

Who are the source language producers?

The participants in the Quick, Draw! game.

Annotations

Annotation process

The annotations are machine-generated and match the category the player was prompted to draw.

Who are the annotators?

The annotations are machine-generated.

Personal and Sensitive Information

Some sketches are known to be problematic (see https://github.com/googlecreativelab/quickdraw-dataset/issues/74 and https://github.com/googlecreativelab/quickdraw-dataset/issues/18).

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

Additional Information

Dataset Curators

Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim and Nick Fox-Gieg.

Licensing Information

The data is made available by Google, Inc. under the Creative Commons Attribution 4.0 International license.

Citation Information

@article{DBLP:journals/corr/HaE17,
  author    = {David Ha and
               Douglas Eck},
  title     = {A Neural Representation of Sketch Drawings},
  journal   = {CoRR},
  volume    = {abs/1704.03477},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.03477},
  archivePrefix = {arXiv},
  eprint    = {1704.03477},
  timestamp = {Mon, 13 Aug 2018 16:48:30 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/HaE17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @mariosasko for adding this dataset.

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