DunnBC22/yolos-small-Cell_Tower_Detection
Object Detection
•
Updated
•
22
•
1
image_id
int64 0
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} |
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394 | 640 | 640 | {
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} |
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233 | 640 | 640 | {
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122,
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363 | 640 | 640 | {
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288 | 640 | 640 | {
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553 | 640 | 640 | {
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} |
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522 | 640 | 640 | {
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]
} |
|
698 | 640 | 640 | {
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3472
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[
272,
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56,
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]
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1
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} |
|
561 | 640 | 640 | {
"id": [
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277,
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543,
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2,
1,
1
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} |
|
497 | 640 | 640 | {
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2310,
2311,
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2314,
2315,
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607,
1323,
958,
520,
484,
580,
636,
1248,
502,
1645
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"bbox": [
[
332,
198,
22.5,
27
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223,
290,
31.5,
42
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435,
295,
27,
35.5
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326,
319,
20,
26
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288,
350,
19,
25.5
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334,
388,
21.5,
27
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395,
438,
24,
26.5
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[
214,
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32,
39
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[
334,
582,
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24.5
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],
"category": [
1,
1,
1,
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1,
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} |
** The original COCO dataset is stored at dataset.tar.gz
**
cell-towers
object-detection
: The dataset can be used to train a model for Object Detection.English
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]
}
}
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 over dataset["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.Annotators are Roboflow users
See original homepage https://universe.roboflow.com/object-detection/cell-towers
@misc{ cell-towers,
title = { cell towers Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cell-towers } },
url = { https://universe.roboflow.com/object-detection/cell-towers },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Thanks to @mariosasko for adding this dataset.