Search is not available for this dataset
label
class label 345
classes | packed_drawing
unknown |
|---|---|
173lighter
| [
0,
0,
160,
130,
184,
121,
20,
0,
85,
83,
1,
159,
47,
136,
88,
3,
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34,
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54,
6,
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53,
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8,
0,
84,
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92,
96,
101,
117,
117,
95,
34,
32,
27,
4,
0,
1,
24,
42
] |
272snake
| [
0,
0,
96,
108,
55,
186,
23,
0,
85,
83,
1,
155,
236,
182,
88,
6,
0,
14,
0,
28,
32,
48,
63,
98,
110,
123,
146,
162,
174,
199,
209,
228,
243,
44,
44,
25,
20,
42,
47,
44,
31,
27,
31,
50,
50,
43,
46,
2,
0,
19,
18,
34,
37,
2,
0,
38,
38,
45,
52,
3,
0,
13,
22,
29,
55,
61,
55,
11,
0,
24,
11,
2,
0,
7,
19,
29,
42,
41,
33,
25,
24,
29,
45,
59,
69,
72,
70,
57,
39,
29,
25,
19,
0,
31,
43,
58,
73,
101,
114,
149,
170,
178,
195,
204,
209,
235,
243,
249,
255,
254,
249,
234,
26,
11,
8,
12,
29,
30,
6,
0,
5,
31,
38,
36,
16,
13,
15,
33,
54,
56,
53
] |
75clarinet
| [
0,
0,
96,
50,
132,
189,
17,
0,
67,
65,
1,
19,
57,
186,
88,
6,
0,
12,
0,
88,
70,
50,
30,
0,
90,
125,
143,
144,
135,
34,
9,
84,
133,
169,
196,
223,
251,
255,
253,
245,
243,
220,
219,
7,
0,
131,
117,
116,
131,
143,
163,
160,
238,
211,
179,
140,
119,
96,
94,
9,
0,
81,
109,
119,
164,
209,
206,
197,
189,
155,
90,
68,
42,
29,
0,
39,
73,
88,
104,
8,
0,
104,
104,
107,
127,
133,
134,
126,
124,
108,
99,
91,
77,
80,
91,
102,
100,
12,
0,
84,
75,
70,
70,
76,
84,
89,
89,
82,
72,
68,
67,
145,
145,
141,
132,
129,
130,
135,
157,
162,
161,
157,
149,
12,
0,
69,
50,
46,
44,
51,
60,
72,
71,
67,
49,
42,
40,
179,
185,
185,
181,
175,
174,
192,
198,
201,
202,
199,
187
] |
111face
| [
0,
0,
96,
98,
18,
189,
20,
0,
71,
66,
1,
58,
242,
185,
88,
9,
0,
22,
0,
174,
165,
142,
125,
81,
39,
11,
4,
0,
1,
22,
37,
52,
64,
98,
117,
161,
188,
199,
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190,
181,
36,
25,
8,
2,
0,
21,
53,
73,
99,
162,
211,
230,
245,
252,
255,
252,
226,
195,
156,
105,
56,
39,
2,
0,
37,
37,
91,
91,
2,
0,
148,
148,
85,
85,
3,
0,
42,
44,
52,
113,
106,
103,
2,
0,
131,
133,
113,
113,
6,
0,
63,
67,
77,
92,
101,
111,
165,
190,
210,
214,
213,
187,
7,
0,
75,
75,
88,
91,
106,
112,
113,
191,
185,
180,
201,
182,
189,
204,
5,
0,
31,
36,
67,
77,
85,
136,
126,
121,
127,
142,
4,
0,
129,
165,
171,
177,
133,
134,
138,
148
] |
294streetlight
| [
0,
0,
96,
13,
217,
34,
19,
0,
71,
66,
1,
108,
117,
210,
88,
8,
0,
6,
0,
0,
8,
11,
16,
22,
18,
42,
94,
233,
255,
253,
237,
14,
0,
0,
3,
18,
54,
82,
91,
97,
115,
123,
108,
79,
73,
72,
89,
36,
19,
8,
0,
2,
10,
43,
54,
62,
67,
70,
70,
67,
29,
2,
0,
94,
96,
32,
20,
4,
0,
68,
89,
119,
129,
68,
56,
59,
67,
9,
0,
92,
88,
89,
101,
110,
115,
114,
105,
93,
58,
61,
71,
76,
74,
67,
59,
56,
57,
4,
0,
94,
91,
71,
70,
72,
88,
120,
126,
5,
0,
110,
117,
133,
148,
166,
73,
98,
126,
144,
160,
4,
0,
109,
180,
220,
227,
74,
100,
120,
128
] |
222piano
| [
0,
0,
96,
143,
35,
92,
22,
0,
75,
82,
1,
53,
171,
216,
88,
11,
0,
11,
0,
0,
133,
164,
250,
255,
250,
83,
59,
9,
6,
7,
3,
3,
0,
3,
33,
39,
38,
34,
34,
30,
5,
2,
0,
3,
3,
32,
32,
4,
0,
24,
25,
31,
30,
5,
18,
18,
7,
5,
0,
54,
54,
61,
67,
67,
6,
14,
16,
16,
6,
3,
0,
39,
42,
40,
3,
11,
34,
2,
0,
81,
77,
5,
36,
4,
0,
104,
102,
109,
111,
5,
19,
18,
5,
2,
0,
104,
103,
21,
35,
2,
0,
125,
126,
5,
36,
2,
0,
145,
147,
4,
39,
2,
0,
171,
168,
2,
43
] |
40bottlecap
| [
0,
0,
96,
155,
104,
80,
16,
0,
78,
76,
1,
155,
49,
212,
88,
7,
0,
19,
0,
4,
0,
17,
40,
97,
134,
171,
204,
231,
246,
248,
231,
225,
184,
148,
116,
64,
40,
11,
178,
67,
48,
32,
7,
1,
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5,
16,
31,
43,
63,
67,
83,
90,
92,
91,
85,
66,
13,
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255,
240,
226,
201,
182,
153,
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38,
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9,
1,
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72,
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154,
167,
182,
189,
195,
196,
183,
173,
155,
152,
4,
0,
21,
18,
18,
27,
87,
104,
131,
178,
3,
0,
38,
46,
52,
90,
159,
180,
3,
0,
72,
71,
81,
95,
109,
199,
3,
0,
105,
109,
116,
102,
111,
192,
2,
0,
139,
139,
91,
91
] |
53cactus
| [
0,
0,
96,
71,
55,
41,
21,
0,
71,
66,
1,
79,
34,
216,
88,
6,
0,
12,
0,
34,
31,
29,
44,
70,
113,
127,
137,
145,
147,
151,
153,
255,
241,
29,
6,
0,
2,
17,
37,
70,
94,
104,
250,
2,
0,
134,
166,
64,
40,
3,
0,
81,
78,
50,
53,
44,
17,
3,
0,
52,
44,
0,
105,
102,
102,
2,
0,
128,
136,
157,
157,
2,
0,
116,
119,
76,
76
] |
178lipstick
| [
0,
0,
96,
14,
221,
223,
18,
0,
85,
83,
0,
101,
221,
194,
88,
5,
0,
11,
0,
21,
12,
10,
12,
24,
35,
47,
52,
42,
42,
16,
104,
180,
244,
250,
255,
253,
238,
215,
129,
100,
97,
6,
0,
20,
27,
38,
43,
39,
39,
93,
73,
24,
42,
60,
99,
2,
0,
21,
38,
114,
119,
10,
0,
34,
40,
47,
51,
52,
24,
8,
0,
27,
38,
21,
10,
11,
19,
56,
168,
204,
155,
34,
7,
9,
0,
85,
76,
75,
105,
152,
157,
157,
143,
84,
3,
0,
20,
23,
16,
10,
2,
0,
1
] |
50bus
| [
0,
0,
96,
185,
47,
191,
18,
0,
70,
73,
1,
210,
22,
204,
88,
6,
0,
22,
0,
0,
45,
142,
198,
209,
219,
229,
239,
239,
233,
228,
218,
195,
173,
102,
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36,
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10,
4,
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29,
23,
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48,
72,
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121,
131,
146,
153,
161,
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139,
118,
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16,
13,
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29,
15,
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30,
58,
67,
74,
81,
81,
65,
47,
40,
30,
169,
183,
205,
218,
221,
216,
206,
187,
168,
164,
165,
168,
178,
13,
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223,
214,
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93,
11,
0,
144,
122,
108,
105,
107,
126,
137,
150,
154,
152,
139,
133,
134,
126,
118,
102,
84,
80,
82,
124,
131,
142
] |
259see saw
| [
0,
0,
96,
6,
166,
240,
18,
0,
77,
89,
1,
81,
47,
194,
88,
4,
0,
14,
0,
45,
33,
0,
1,
8,
64,
225,
233,
254,
254,
243,
206,
175,
50,
133,
118,
97,
92,
88,
61,
0,
18,
46,
51,
61,
82,
93,
130,
5,
0,
144,
135,
154,
156,
149,
104,
139,
136,
127,
99,
6,
0,
154,
195,
197,
186,
170,
153,
99,
116,
119,
128,
130,
101,
4,
0,
35,
43,
51,
57,
90,
86,
87,
109
] |
221penguin
| [
0,
0,
96,
226,
21,
139,
19,
0,
85,
83,
0,
191,
25,
190,
88,
17,
0,
7,
0,
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134,
118,
122,
136,
150,
153,
5,
4,
12,
22,
25,
16,
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152,
135,
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18,
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130,
130,
10,
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11,
7,
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127,
121,
124,
132,
138,
143,
144,
27,
59,
64,
66,
62,
51,
26,
6,
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130,
127,
134,
139,
140,
133,
29,
59,
56,
46,
25,
31,
4,
0,
143,
166,
181,
147,
31,
38,
37,
45,
3,
0,
125,
102,
122,
33,
43,
46,
2,
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126,
120,
63,
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121,
127,
68,
71,
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136,
135,
66,
75,
2,
0,
132,
143,
70,
71,
3,
0,
0,
15,
1,
72,
71,
76,
6,
0,
16,
73,
136,
149,
238,
255,
72,
75,
69,
72,
73,
68,
4,
0,
209,
204,
205,
207,
3,
6,
12,
11,
5,
0,
73,
50,
47,
51,
65,
0,
14,
20,
21,
14
] |
269sleeping bag
| [
0,
0,
160,
72,
25,
38,
18,
0,
68,
69,
1,
250,
38,
135,
88,
2,
0,
25,
0,
3,
2,
15,
16,
21,
36,
56,
70,
85,
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8,
25,
33,
56,
65,
70,
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65,
44,
17,
17,
22,
41,
66,
91,
96,
96,
93,
71,
46,
32,
16
] |
278speedboat
| [
0,
0,
32,
15,
210,
40,
16,
0,
85,
83,
1,
180,
10,
121,
88,
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238,
80,
78,
85,
91,
89,
5,
0,
238,
245,
252,
245,
240,
89,
90,
104,
105,
101
] |
129frying pan
| [
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0,
96,
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2,
128,
23,
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83,
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173,
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63,
6,
0,
201,
188,
177,
179,
186,
191,
31,
35,
48,
61,
66,
66
] |
153hot dog
| [
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0,
160,
130,
189,
98,
21,
0,
67,
65,
1,
164,
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135,
88,
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117,
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88,
72,
54,
35,
5,
6,
0,
61,
61,
66,
85,
97,
119,
62,
69,
75,
78,
75,
59
] |
113feather
| [
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0,
96,
86,
101,
68,
19,
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85,
83,
1,
161,
112,
193,
88,
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32,
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133,
112,
83,
44,
5,
0,
6,
22,
41,
63,
87,
123,
167,
183,
200,
214,
226
] |
308telephone
| [
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0,
96,
109,
16,
145,
19,
0,
65,
69,
1,
217,
56,
209,
88,
9,
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5,
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84,
143,
209,
215,
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End of preview. Expand
in Dataset Viewer.
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
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