DunnBC22/yolos-small-Abdomen_MRI
Object Detection
•
Updated
•
26
•
2
image_id
int64 0
1.89k
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imagewidth (px) 640
640
| width
int32 640
640
| height
int32 640
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1119
],
"area": [
17603,
18843
],
"bbox": [
[
254,
157,
109,
161.5
],
[
132,
174,
112.5,
167.5
]
],
"category": [
1,
1
]
} |
|
772 | 640 | 640 | {
"id": [
787
],
"area": [
16254
],
"bbox": [
[
213,
167,
126,
129
]
],
"category": [
1
]
} |
|
51 | 640 | 640 | {
"id": [
52
],
"area": [
18143
],
"bbox": [
[
291,
112,
138.5,
131
]
],
"category": [
1
]
} |
|
354 | 640 | 640 | {
"id": [
360
],
"area": [
18759
],
"bbox": [
[
280,
174,
111,
169
]
],
"category": [
1
]
} |
|
723 | 640 | 640 | {
"id": [
736
],
"area": [
37267
],
"bbox": [
[
220,
203,
166,
224.5
]
],
"category": [
1
]
} |
|
419 | 640 | 640 | {
"id": [
425,
426
],
"area": [
35730,
54792
],
"bbox": [
[
300,
196,
198.5,
180
],
[
289,
174,
217,
252.5
]
],
"category": [
1,
1
]
} |
|
797 | 640 | 640 | {
"id": [
813
],
"area": [
11377
],
"bbox": [
[
255,
163,
92.5,
123
]
],
"category": [
1
]
} |
|
146 | 640 | 640 | {
"id": [
150
],
"area": [
10743
],
"bbox": [
[
238,
135,
95.5,
112.5
]
],
"category": [
1
]
} |
|
1,079 | 640 | 640 | {
"id": [
1100
],
"area": [
15533
],
"bbox": [
[
289,
154,
107.5,
144.5
]
],
"category": [
1
]
} |
** The original COCO dataset is stored at dataset.tar.gz
**
abdomen-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/abdomen-mri
@misc{ abdomen-mri,
title = { abdomen mri Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/abdomen-mri } },
url = { https://universe.roboflow.com/object-detection/abdomen-mri },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Thanks to @mariosasko for adding this dataset.