The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Dataset Labels
['tumor']
Number of Images
{'valid': 234, 'test': 125, 'train': 2377}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("disha07/brainybrain", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9/dataset/1
Citation
@misc{
brain-lesion-kmiz9_dataset,
title = { Brain lesion Dataset },
type = { Open Source Dataset },
author = { brain leisons },
howpublished = { \\url{ https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 } },
url = { https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { apr },
note = { visited on 2024-04-21 },
}
License
CC BY 4.0
Dataset Summary
This dataset was exported via roboflow.com on April 20, 2024 at 11:41 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
- collaborate with your team on computer vision projects
- collect & organize images
- understand and search unstructured image data
- annotate, and create datasets
- export, train, and deploy computer vision models
- use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 2736 images. Lesion are annotated in COCO format.
The following pre-processing was applied to each image:
- Auto-orientation of pixel data (with EXIF-orientation stripping)
- Resize to 640x640 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
The following transformations were applied to the bounding boxes of each image:
- 50% probability of horizontal flip
- Downloads last month
- 42