DunnBC22/yolos-small-Axial_MRIs
Object Detection
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Updated
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42
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image_id
int64 0
252
| image
imagewidth (px) 640
640
| width
int32 640
640
| height
int32 640
640
| objects
sequence |
|---|---|---|---|---|
95 | 640 | 640 | {
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|
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87 | 640 | 640 | {
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224 | 640 | 640 | {
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236 | 640 | 640 | {
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143 | 640 | 640 | {
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|
60 | 640 | 640 | {
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|
110 | 640 | 640 | {
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|
199 | 640 | 640 | {
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|
9 | 640 | 640 | {
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|
71 | 640 | 640 | {
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|
89 | 640 | 640 | {
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|
43 | 640 | 640 | {
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|
33 | 640 | 640 | {
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|
223 | 640 | 640 | {
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|
131 | 640 | 640 | {
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|
72 | 640 | 640 | {
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|
35 | 640 | 640 | {
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[
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1
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|
220 | 640 | 640 | {
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|
26 | 640 | 640 | {
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|
77 | 640 | 640 | {
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|
238 | 640 | 640 | {
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|
161 | 640 | 640 | {
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|
114 | 640 | 640 | {
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|
183 | 640 | 640 | {
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|
59 | 640 | 640 | {
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|
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|
210 | 640 | 640 | {
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|
55 | 640 | 640 | {
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|
46 | 640 | 640 | {
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|
29 | 640 | 640 | {
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|
139 | 640 | 640 | {
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|
24 | 640 | 640 | {
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|
30 | 640 | 640 | {
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|
17 | 640 | 640 | {
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|
141 | 640 | 640 | {
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|
65 | 640 | 640 | {
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|
119 | 640 | 640 | {
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|
80 | 640 | 640 | {
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|
150 | 640 | 640 | {
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2
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|
96 | 640 | 640 | {
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1
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|
246 | 640 | 640 | {
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2
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|
39 | 640 | 640 | {
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|
40 | 640 | 640 | {
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|
207 | 640 | 640 | {
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|
177 | 640 | 640 | {
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|
218 | 640 | 640 | {
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|
127 | 640 | 640 | {
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2
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|
213 | 640 | 640 | {
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2
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|
64 | 640 | 640 | {
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2
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|
211 | 640 | 640 | {
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|
194 | 640 | 640 | {
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1
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|
245 | 640 | 640 | {
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|
173 | 640 | 640 | {
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1
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|
14 | 640 | 640 | {
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1
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|
67 | 640 | 640 | {
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|
247 | 640 | 640 | {
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1
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|
116 | 640 | 640 | {
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2
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|
188 | 640 | 640 | {
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|
22 | 640 | 640 | {
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1
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|
128 | 640 | 640 | {
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1
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|
16 | 640 | 640 | {
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2
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|
196 | 640 | 640 | {
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2
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|
152 | 640 | 640 | {
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1
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|
111 | 640 | 640 | {
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2
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|
76 | 640 | 640 | {
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1
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|
54 | 640 | 640 | {
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2
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} |
|
102 | 640 | 640 | {
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1
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} |
|
146 | 640 | 640 | {
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2
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} |
|
120 | 640 | 640 | {
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2
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} |
|
56 | 640 | 640 | {
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[
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1
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} |
|
53 | 640 | 640 | {
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2
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} |
|
8 | 640 | 640 | {
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[
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2
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} |
|
50 | 640 | 640 | {
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50
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4812
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1
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} |
|
209 | 640 | 640 | {
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213
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[
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1
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} |
|
51 | 640 | 640 | {
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51
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4062
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[
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1
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} |
|
86 | 640 | 640 | {
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7012
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[
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82.5,
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1
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} |
|
197 | 640 | 640 | {
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201
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5636
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[
150,
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83.5,
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],
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1
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} |
|
252 | 640 | 640 | {
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257
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[
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2
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} |
|
212 | 640 | 640 | {
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216
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1
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} |
|
25 | 640 | 640 | {
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25
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1
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} |
|
230 | 640 | 640 | {
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235
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[
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2
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} |
|
49 | 640 | 640 | {
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2
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} |
|
136 | 640 | 640 | {
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1
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} |
|
13 | 640 | 640 | {
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1
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} |
|
179 | 640 | 640 | {
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183
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|
149 | 640 | 640 | {
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1
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|
135 | 640 | 640 | {
"id": [
137
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"area": [
10680
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"bbox": [
[
133,
395,
133.5,
80
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],
"category": [
2
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} |
|
169 | 640 | 640 | {
"id": [
173
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"area": [
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"bbox": [
[
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52.5,
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"category": [
2
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|
243 | 640 | 640 | {
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248
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"area": [
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"bbox": [
[
450,
335,
103.5,
137.5
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],
"category": [
1
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} |
|
180 | 640 | 640 | {
"id": [
184
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"area": [
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"bbox": [
[
170,
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56.5,
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"category": [
1
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|
132 | 640 | 640 | {
"id": [
134
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"bbox": [
[
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|
5 | 640 | 640 | {
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} |
End of preview. Expand
in Dataset Viewer.
Dataset Card for axial-mri
** The original COCO dataset is stored at dataset.tar.gz**
Dataset Summary
axial-mri
Supported Tasks and Leaderboards
object-detection: The dataset can be used to train a model for Object Detection.
Languages
English
Dataset Structure
Data Instances
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]
}
}
Data Fields
image: the image idimage:PIL.Image.Imageobject 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 overdataset["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.
Who are the annotators?
Annotators are Roboflow users
Additional Information
Licensing Information
See original homepage https://universe.roboflow.com/object-detection/axial-mri
Citation Information
@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 },
}"
Contributions
Thanks to @mariosasko for adding this dataset.
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Homepage:
universe.roboflow.com
Point of Contact:
francesco.zuppichini@gmail.com
Size of downloaded dataset files:
4.79 MB
Size of the auto-converted Parquet files:
4.79 MB
Number of rows:
371
Models trained or fine-tuned on Francesco/axial-mri