Datasets:
Tasks:
Object Detection
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
image_id
int64 | image
image | width
int32 | height
int32 | objects
sequence |
|---|---|---|---|---|
1,704 | 640 | 640 | {
"id": [
9089,
9090,
9091
],
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23225
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102
],
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593,
98.5,
46
],
[
146,
17,
259.5,
89.5
]
],
"category": [
7,
11,
5
]
} |
|
2,122 | 640 | 640 | {
"id": [
11297,
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11301
],
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3034,
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3040
],
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102,
443,
429,
67
],
[
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61,
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354
],
[
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119,
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],
[
184,
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132,
32.5
],
[
211,
37,
190,
16
]
],
"category": [
7,
5,
12,
11,
4
]
} |
|
1,074 | 640 | 640 | {
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5720,
5721,
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5725,
5726
],
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110.5,
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[
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[
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[
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[
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305.5,
20.5
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[
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19.5
]
],
"category": [
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5,
7,
4,
4,
2,
1,
2,
3,
2,
4
]
} |
|
268 | 640 | 640 | {
"id": [
1439,
1440,
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],
"area": [
3168,
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],
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123,
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154
],
[
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532,
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]
],
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12,
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]
} |
|
2,642 | 640 | 640 | {
"id": [
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],
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214,
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318,
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106,
19
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[
289,
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17.5
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[
234,
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52.5
],
[
298,
558,
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38.5
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[
269,
573,
99.5,
11.5
]
],
"category": [
5,
2,
4,
12,
7,
5,
6
]
} |
|
953 | 640 | 640 | {
"id": [
5089,
5090,
5091,
5092,
5093,
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5095
],
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19251,
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1914,
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1800,
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],
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143,
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244,
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268,
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180,
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285,
483,
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67
]
],
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7,
11,
2,
2,
4,
2,
5
]
} |
|
2,899 | 640 | 640 | {
"id": [
15552,
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15555,
15556,
15557
],
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54601,
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],
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61,
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44,
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],
[
182,
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[
244,
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179.5,
24
],
[
45,
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],
[
45,
513,
186,
25
]
],
"category": [
7,
5,
7,
11,
1,
4
]
} |
|
517 | 640 | 640 | {
"id": [
2723,
2724,
2725,
2726,
2727,
2728,
2729
],
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32305,
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1687,
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],
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130,
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]
],
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5,
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11,
4,
12,
4
]
} |
|
1,837 | 640 | 640 | {
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9783,
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],
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11787,
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9222
],
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[
57,
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512.5,
23
],
[
182,
32,
257,
97
],
[
210,
135,
185,
12
],
[
17,
178,
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52
],
[
136,
351,
348,
26.5
]
],
"category": [
11,
5,
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7,
2
]
} |
|
2,657 | 640 | 640 | {
"id": [
14170,
14171,
14172,
14173,
14174,
14175,
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14177
],
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26796,
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2956,
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],
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147,
52,
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],
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156,
178,
319.5,
60
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253,
346,
116,
24
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[
281,
378,
65,
26
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[
246,
480,
141,
61
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[
266,
539,
96,
11.5
],
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316,
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137.5,
21.5
],
[
176,
590,
122.5,
21
]
],
"category": [
7,
7,
11,
12,
5,
6,
4,
2
]
} |
|
1,059 | 640 | 640 | {
"id": [
5638,
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5640,
5641,
5642,
5643
],
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2040,
3342,
1550,
539
],
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[
130,
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],
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201,
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239.5,
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[
271,
528,
102,
20
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[
223,
551,
191,
17.5
],
[
144,
593,
58.5,
26.5
],
[
303,
622,
38.5,
14
]
],
"category": [
5,
7,
2,
3,
1,
4
]
} |
|
1,237 | 640 | 640 | {
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6603,
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6607,
6608
],
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2600,
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3540,
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208,
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199,
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255.5,
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]
],
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12,
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6,
7,
2
]
} |
|
2,279 | 640 | 640 | {
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12145
],
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12400,
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258,
164,
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100
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125,
267,
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84
],
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213,
414,
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68
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[
282,
522,
80,
36
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264,
103,
114.5,
28
]
],
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5,
7,
11,
1,
12
]
} |
|
914 | 640 | 640 | {
"id": [
4879,
4880,
4881
],
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15012,
11086,
14442
],
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175,
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278,
54
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256,
144,
120.5,
92
],
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207,
244,
220.5,
65.5
]
],
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11,
5,
7
]
} |
|
1,614 | 640 | 640 | {
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8567,
8568,
8569,
8570,
8571,
8572,
8573
],
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10936,
4911,
768,
1794,
1970,
4657
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235,
109,
173.5,
152.5
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201,
337,
225.5,
48.5
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180,
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288,
503,
48,
16
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218,
573,
92,
19.5
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312,
574,
106.5,
18.5
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217,
594,
202.5,
23
]
],
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5,
7,
11,
12,
2,
4,
2
]
} |
|
767 | 640 | 640 | {
"id": [
4107,
4108,
4109
],
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36288,
6405,
55019
],
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223,
328,
288,
126
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500,
554,
109.5,
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51,
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526.5,
104.5
]
],
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7,
1,
7
]
} |
|
1,356 | 640 | 640 | {
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11,
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} |
|
2,384 | 640 | 640 | {
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12725,
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],
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201,
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233.5,
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11
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} |
|
2,170 | 640 | 640 | {
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11579
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68,
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465.5,
204.5
]
],
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} |
|
2,627 | 640 | 640 | {
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7,
7,
11,
5,
6,
4
]
} |
|
1,663 | 640 | 640 | {
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8854,
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],
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} |
|
2,818 | 640 | 640 | {
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],
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12,
2
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} |
|
2,268 | 640 | 640 | {
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12086,
12087,
12088,
12089
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167,
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303.5,
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]
],
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12,
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} |
|
2,049 | 640 | 640 | {
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],
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]
],
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5,
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12,
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4,
3,
1,
4
]
} |
|
728 | 640 | 640 | {
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3899,
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3901,
3902,
3903
],
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],
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231,
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262,
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],
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} |
|
2,654 | 640 | 640 | {
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} |
|
2,984 | 640 | 640 | {
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} |
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2,463 | 640 | 640 | {
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1,
4
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} |
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899 | 640 | 640 | {
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1,588 | 640 | 640 | {
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} |
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675 | 640 | 640 | {
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End of preview. Expand
in Dataset Viewer.
Dataset Card for wine-labels
** The original COCO dataset is stored at dataset.tar.gz**
Dataset Summary
wine-labels
Supported Tasks and Leaderboards
object-detection: The dataset can be used to train a model for Object Detection.
Languages
English
Dataset Structure
Data Instances
A data point comprises an image and its object annotations.
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
Data Fields
image: the image idimage:PIL.Image.Imageobject containing the image. 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 overdataset["image"][0]width: the image widthheight: the image heightobjects: a dictionary containing bounding box metadata for the objects present on the imageid: the annotation idarea: the area of the bounding boxbbox: the object's bounding box (in the coco format)category: the object's category.
Who are the annotators?
Annotators are Roboflow users
Additional Information
Licensing Information
See original homepage https://universe.roboflow.com/object-detection/wine-labels
Citation Information
@misc{ wine-labels,
title = { wine labels Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/wine-labels } },
url = { https://universe.roboflow.com/object-detection/wine-labels },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Contributions
Thanks to @mariosasko for adding this dataset.
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