DunnBC22/yolos-tiny-Brain_Tumor_Detection
Object Detection • Updated • 51 • 4
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** 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.