DunnBC22/yolos-small-Axial_MRIs
Object Detection
•
Updated
•
16
•
2
image_id
int64 0
252
| image
imagewidth (px) 640
640
| width
int32 640
640
| height
int32 640
640
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sequence |
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149 | 640 | 640 | {
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135 | 640 | 640 | {
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133.5,
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|
169 | 640 | 640 | {
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|
243 | 640 | 640 | {
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|
180 | 640 | 640 | {
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132 | 640 | 640 | {
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5 | 640 | 640 | {
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End of preview. Expand
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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