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[
611,
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[
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[
618,
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[
545,
515,
43,
62
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[
579,
548,
8,
19
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],
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2,
5,
2,
1,
2,
1,
2,
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2,
5,
2,
2,
1,
2,
1,
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2,
5,
2,
1
]
} |
|
85 | 640 | 640 | {
"id": [
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1134,
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1138
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790,
1433,
1269,
1368,
1575,
1631,
2320,
3417,
5534
],
"bbox": [
[
322,
93,
46.5,
17
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[
295,
105,
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30.5
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[
303,
144,
47,
27
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[
270,
184,
57,
24
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[
247,
219,
52.5,
30
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[
224,
272,
53.5,
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[
237,
327,
59.5,
39
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[
230,
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67,
51
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[
244,
466,
84.5,
65.5
]
],
"category": [
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2,
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2,
2,
2,
2,
2,
2
]
} |
|
173 | 640 | 640 | {
"id": [
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847,
999,
1075,
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1113,
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1858
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"bbox": [
[
312,
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11.5
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[
318,
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[
341,
134,
31,
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[
306,
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[
403,
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[
373,
236,
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[
306,
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[
299,
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[
201,
380,
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[
153,
463,
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3,
3,
3,
2,
3,
3,
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]
} |
End of preview. Expand
in Dataset Viewer.
Dataset Card for aerial-pool
** The original COCO dataset is stored at dataset.tar.gz
**
Dataset Summary
aerial-pool
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.Image
object 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/aerial-pool
Citation Information
@misc{ aerial-pool,
title = { aerial pool Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/aerial-pool } },
url = { https://universe.roboflow.com/object-detection/aerial-pool },
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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