Datasets:
Tasks:
Object Detection
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
rf100
License:
cc
| image_id (int64) | image (image) | width (int32) | height (int32) | objects (sequence) |
|---|---|---|---|---|
0 |  | 640 | 640 | {
"id": [
0
],
"area": [
162560
],
"bbox": [
[
0,
77,
640,
254
]
],
"category": [
2
]
} |
7 |  | 640 | 640 | {
"id": [
14,
15
],
"area": [
276160,
21479
],
"bbox": [
[
0,
126,
640,
431.5
],
[
126,
162,
457,
47
]
],
"category": [
2,
1
]
} |
20 |  | 640 | 640 | {
"id": [
42,
43
],
"area": [
125120,
1402
],
"bbox": [
[
0,
154,
640,
195.5
],
[
129,
201,
93.5,
15
]
],
"category": [
2,
1
]
} |
17 |  | 640 | 640 | {
"id": [
33,
34
],
"area": [
225280,
12510
],
"bbox": [
[
0,
116,
640,
352
],
[
127,
492,
417,
30
]
],
"category": [
2,
1
]
} |
6 |  | 640 | 640 | {
"id": [],
"area": [],
"bbox": [],
"category": []
} |
1 |  | 640 | 640 | {
"id": [
1,
2
],
"area": [
118720,
111672
],
"bbox": [
[
0,
113,
640,
185.5
],
[
0,
290,
634.5,
176
]
],
"category": [
2,
2
]
} |
16 |  | 640 | 640 | {
"id": [
31,
32
],
"area": [
296000,
90952
],
"bbox": [
[
0,
84,
640,
462.5
],
[
126,
114,
452.5,
201
]
],
"category": [
2,
1
]
} |
13 |  | 640 | 640 | {
"id": [
26,
27
],
"area": [
180800,
21643
],
"bbox": [
[
0,
169,
640,
282.5
],
[
127,
227,
470.5,
46
]
],
"category": [
2,
1
]
} |
2 |  | 640 | 640 | {
"id": [
3,
4,
5,
6
],
"area": [
113600,
213120,
31992,
20410
],
"bbox": [
[
0,
76,
640,
177.5
],
[
0,
246,
640,
333
],
[
125,
304,
477.5,
67
],
[
121,
135,
392.5,
52
]
],
"category": [
2,
2,
1,
1
]
} |
12 |  | 640 | 640 | {
"id": [
24,
25
],
"area": [
198720,
33784
],
"bbox": [
[
0,
158,
640,
310.5
],
[
126,
191,
303,
111.5
]
],
"category": [
2,
1
]
} |
18 |  | 640 | 640 | {
"id": [
35,
36,
37,
38
],
"area": [
81600,
222720,
14625,
3912
],
"bbox": [
[
0,
104,
640,
127.5
],
[
0,
223,
640,
348
],
[
128,
161,
450,
32.5
],
[
129,
282,
244.5,
16
]
],
"category": [
2,
2,
1,
1
]
} |
14 |  | 640 | 640 | {
"id": [
28
],
"area": [
288320
],
"bbox": [
[
0,
126,
640,
450.5
]
],
"category": [
2
]
} |
9 |  | 640 | 640 | {
"id": [
18,
19
],
"area": [
224000,
36869
],
"bbox": [
[
0,
161,
640,
350
],
[
125,
220,
458,
80.5
]
],
"category": [
2,
1
]
} |
5 |  | 640 | 640 | {
"id": [
12,
13
],
"area": [
235200,
49324
],
"bbox": [
[
0,
184,
640,
367.5
],
[
126,
241,
436.5,
113
]
],
"category": [
2,
1
]
} |
8 |  | 640 | 640 | {
"id": [
16,
17
],
"area": [
142400,
60192
],
"bbox": [
[
0,
328,
640,
222.5
],
[
127,
361,
456,
132
]
],
"category": [
2,
1
]
} |
15 |  | 640 | 640 | {
"id": [
29,
30
],
"area": [
294400,
21232
],
"bbox": [
[
0,
124,
640,
460
],
[
128,
166,
305.5,
69.5
]
],
"category": [
2,
1
]
} |
19 |  | 640 | 640 | {
"id": [
39,
40,
41
],
"area": [
162880,
175680,
28329
],
"bbox": [
[
0,
72,
640,
254.5
],
[
0,
319,
640,
274.5
],
[
127,
353,
426,
66.5
]
],
"category": [
2,
2,
1
]
} |
11 |  | 640 | 640 | {
"id": [
21,
22,
23
],
"area": [
154240,
12928,
19718
],
"bbox": [
[
0,
179,
640,
241
],
[
127,
446,
404,
32
],
[
128,
238,
174.5,
113
]
],
"category": [
2,
1,
1
]
} |
3 |  | 640 | 640 | {
"id": [
7,
8,
9
],
"area": [
85440,
63180,
9932
],
"bbox": [
[
0,
63,
640,
133.5
],
[
125,
220,
486,
130
],
[
126,
106,
484.5,
20.5
]
],
"category": [
2,
1,
1
]
} |
4 |  | 640 | 640 | {
"id": [
10,
11
],
"area": [
188800,
3791
],
"bbox": [
[
0,
134,
640,
295
],
[
127,
166,
223,
17
]
],
"category": [
2,
1
]
} |
10 |  | 640 | 640 | {
"id": [
20
],
"area": [
269760
],
"bbox": [
[
0,
165,
640,
421.5
]
],
"category": [
2
]
} |
Dataset Card for tweeter-posts
** The original COCO dataset is stored at dataset.tar.gz**
Dataset Summary
tweeter-posts
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/tweeter-posts
Citation Information
@misc{ tweeter-posts,
title = { tweeter posts Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/tweeter-posts } },
url = { https://universe.roboflow.com/object-detection/tweeter-posts },
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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