DunnBC22/yolos-tiny-Brain_Tumor_Detection
Object Detection
•
Updated
•
171
•
4
image_id
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6.93k
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6,282 | 640 | 640 | {
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3,101 | 640 | 640 | {
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3,924 | 640 | 640 | {
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4,874 | 640 | 640 | {
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2,821 | 640 | 640 | {
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243,
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1,
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]
} |
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4,120 | 640 | 640 | {
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2953,
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299,
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} |
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2,070 | 640 | 640 | {
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2140,
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430,
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1,
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]
} |
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1,871 | 640 | 640 | {
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} |
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4,551 | 640 | 640 | {
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248,
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1,
2
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} |
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5,681 | 640 | 640 | {
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328,
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101.5,
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]
} |
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4,685 | 640 | 640 | {
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280,
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122.5,
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} |
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5,496 | 640 | 640 | {
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11929,
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216,
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224,
150,
282.5,
168
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1,
2
]
} |
|
5,641 | 640 | 640 | {
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12245,
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1,
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} |
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3,595 | 640 | 640 | {
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} |
|
5,825 | 640 | 640 | {
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592,
14369,
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18.5,
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389,
315,
125.5,
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[
488,
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42.5,
66.5
]
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1,
2,
3
]
} |
|
1,761 | 640 | 640 | {
"id": [
3780,
3781
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5412,
1380
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443,
184,
61.5,
88
],
[
435,
187,
40,
34.5
]
],
"category": [
2,
3
]
} |
|
2,318 | 640 | 640 | {
"id": [
4999,
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"area": [
5529,
397
],
"bbox": [
[
328,
317,
109.5,
50.5
],
[
381,
368,
18.5,
21.5
]
],
"category": [
2,
3
]
} |
|
5,474 | 640 | 640 | {
"id": [
11880,
11881,
11882
],
"area": [
11453,
42660,
14892
],
"bbox": [
[
275,
184,
93.5,
122.5
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[
232,
139,
197.5,
216
],
[
269,
176,
109.5,
136
]
],
"category": [
1,
2,
3
]
} |
|
6,844 | 640 | 640 | {
"id": [
14866,
14867,
14868
],
"area": [
12866,
44293,
18074
],
"bbox": [
[
280,
139,
117.5,
109.5
],
[
237,
136,
237.5,
186.5
],
[
269,
131,
138.5,
130.5
]
],
"category": [
1,
2,
3
]
} |
** The original COCO dataset is stored at dataset.tar.gz
**
brain-tumor-m2pbp
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/brain-tumor-m2pbp
@misc{ brain-tumor-m2pbp,
title = { brain tumor m2pbp Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/brain-tumor-m2pbp } },
url = { https://universe.roboflow.com/object-detection/brain-tumor-m2pbp },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Thanks to @mariosasko for adding this dataset.