DunnBC22/yolos-small-Wall_Damage
Object Detection
•
Updated
•
34
image_id
int64 0
324
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int32 640
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]
],
"category": [
2
]
} |
|
173 | 640 | 640 | {
"id": [
173
],
"area": [
123165
],
"bbox": [
[
132,
103,
357,
345
]
],
"category": [
2
]
} |
|
146 | 640 | 640 | {
"id": [
146
],
"area": [
182770
],
"bbox": [
[
9,
103,
627,
291.5
]
],
"category": [
2
]
} |
|
114 | 640 | 640 | {
"id": [
114
],
"area": [
59578
],
"bbox": [
[
357,
314,
192.5,
309.5
]
],
"category": [
2
]
} |
|
36 | 640 | 640 | {
"id": [
36
],
"area": [
168002
],
"bbox": [
[
66,
340,
569.5,
295
]
],
"category": [
2
]
} |
|
163 | 640 | 640 | {
"id": [
163
],
"area": [
52305
],
"bbox": [
[
512,
0,
82.5,
634
]
],
"category": [
2
]
} |
|
231 | 640 | 640 | {
"id": [
231
],
"area": [
168290
],
"bbox": [
[
120,
57,
303.5,
554.5
]
],
"category": [
3
]
} |
** The original COCO dataset is stored at dataset.tar.gz
**
wall-damage
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/wall-damage
@misc{ wall-damage,
title = { wall damage Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/wall-damage } },
url = { https://universe.roboflow.com/object-detection/wall-damage },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Thanks to @mariosasko for adding this dataset.