Datasets:
metadata
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': bone-fracture
'1': angle
'2': fracture
'3': line
'4': messed_up_angle
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
- es
license:
- cc
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: Huesitos
tags:
- rf100
- medical
- code
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.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 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.
Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bone-fracture-7fylg
Citation Information
@misc{ bone-fracture-7fylg,
title = { bone fracture 7fylg Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bone-fracture-7fylg } },
url = { https://universe.roboflow.com/object-detection/bone-fracture-7fylg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
contributions dataset = {[@mariosasko](https://github.com/mariosasko)}
}"