daksh5656/detr-resnet-50_finetuned_cppe5
Object Detection
•
Updated
•
18
image_id
int64 0
2.78k
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640
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int32 640
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1,848 | 640 | 640 | {
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1,328 | 640 | 640 | {
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2,431 | 640 | 640 | {
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1,103 | 640 | 640 | {
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767 | 640 | 640 | {
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1,257 | 640 | 640 | {
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771 | 640 | 640 | {
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2,134 | 640 | 640 | {
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1,703 | 640 | 640 | {
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1,882 | 640 | 640 | {
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2,029 | 640 | 640 | {
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141.5,
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2,630 | 640 | 640 | {
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|
2,713 | 640 | 640 | {
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|
1,231 | 640 | 640 | {
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92.5,
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1,498 | 640 | 640 | {
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2,002 | 640 | 640 | {
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} |
|
2,220 | 640 | 640 | {
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5495
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7328,
5240,
4032
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257,
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283,
96,
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3,
3,
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} |
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1,137 | 640 | 640 | {
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2810
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21217
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[
266,
304,
205,
103.5
]
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2
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} |
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2,077 | 640 | 640 | {
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10060
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[
268,
67,
176.5,
57
]
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3
]
} |
|
2,019 | 640 | 640 | {
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16231,
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143,
186,
122.5,
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347,
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109.5,
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"category": [
3,
3
]
} |
|
683 | 640 | 640 | {
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1687,
1688,
1689
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28518,
14105,
9167
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[
477,
34,
162.5,
175.5
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41,
426,
155,
91
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[
353,
476,
103,
89
]
],
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3,
3,
3
]
} |
|
1,790 | 640 | 640 | {
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4428,
4429,
4430,
4431
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13562,
10174,
48750,
10106
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524,
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108.5,
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[
184,
246,
119,
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443,
260,
187.5
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[
94,
370,
115.5,
87.5
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1,
1,
1,
1
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} |
|
1,151 | 640 | 640 | {
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2857,
2858,
2859
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5584,
11475,
2115
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79,
101,
73,
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283,
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[
112,
616,
90,
23.5
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3,
3,
3
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} |
|
1,183 | 640 | 640 | {
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118,
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1
]
} |
|
1,996 | 640 | 640 | {
"id": [
4952,
4953
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29241,
15126
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317,
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121.5
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2,
2
]
} |
|
118 | 640 | 640 | {
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291
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17017
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"bbox": [
[
156,
114,
187,
91
]
],
"category": [
2
]
} |
|
2,476 | 640 | 640 | {
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6085,
6086
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6828,
11670
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113,
8,
95.5,
71.5
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482,
212,
136.5,
85.5
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3,
3
]
} |
|
1,006 | 640 | 640 | {
"id": [
2468,
2469,
2470
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10920,
10550,
10342
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377,
294,
104,
105
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107,
229,
105.5,
100
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[
420,
127,
98.5,
105
]
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4,
4,
4
]
} |
|
1,997 | 640 | 640 | {
"id": [
4954,
4955,
4956,
4957
],
"area": [
15340,
12654,
24510,
17845
],
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[
247,
184,
118,
130
],
[
478,
240,
111,
114
],
[
293,
47,
172,
142.5
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[
6,
60,
123.5,
144.5
]
],
"category": [
4,
4,
4,
4
]
} |
|
1,767 | 640 | 640 | {
"id": [
4374,
4375,
4376,
4377
],
"area": [
18453,
17680,
9268,
13865
],
"bbox": [
[
257,
469,
199.5,
92.5
],
[
475,
460,
160,
110.5
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584,
152,
55.5,
167
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[
107,
546,
147.5,
94
]
],
"category": [
2,
2,
2,
2
]
} |
** The original COCO dataset is stored at dataset.tar.gz
**
liver-disease
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/liver-disease
@misc{ liver-disease,
title = { liver disease Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/liver-disease } },
url = { https://universe.roboflow.com/object-detection/liver-disease },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
Thanks to @mariosasko for adding this dataset.