DunnBC22/yolos-small-Axial_MRIs
Object Detection
•
Updated
•
41
•
2
image_id
int64 0
252
| image
imagewidth (px) 640
640
| width
int32 640
640
| height
int32 640
640
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} |
|
149 | 640 | 640 | {
"id": [
151
],
"area": [
5796
],
"bbox": [
[
415,
290,
69,
84
]
],
"category": [
1
]
} |
|
135 | 640 | 640 | {
"id": [
137
],
"area": [
10680
],
"bbox": [
[
133,
395,
133.5,
80
]
],
"category": [
2
]
} |
|
169 | 640 | 640 | {
"id": [
173
],
"area": [
2625
],
"bbox": [
[
378,
225,
52.5,
50
]
],
"category": [
2
]
} |
|
243 | 640 | 640 | {
"id": [
248
],
"area": [
14231
],
"bbox": [
[
450,
335,
103.5,
137.5
]
],
"category": [
1
]
} |
|
180 | 640 | 640 | {
"id": [
184
],
"area": [
2966
],
"bbox": [
[
170,
385,
56.5,
52.5
]
],
"category": [
1
]
} |
|
132 | 640 | 640 | {
"id": [
134
],
"area": [
21375
],
"bbox": [
[
108,
270,
142.5,
150
]
],
"category": [
1
]
} |
|
5 | 640 | 640 | {
"id": [
5
],
"area": [
7500
],
"bbox": [
[
277,
328,
100,
75
]
],
"category": [
2
]
} |
** The original COCO dataset is stored at dataset.tar.gz
**
axial-mri
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/axial-mri
@misc{ axial-mri,
title = { axial mri Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/axial-mri } },
url = { https://universe.roboflow.com/object-detection/axial-mri },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Thanks to @mariosasko for adding this dataset.