brainybrain / README.md
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metadata
task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow2huggingface
disha07/brainybrain

Dataset Labels

['tumor']

Number of Images

{'valid': 234, 'test': 125, 'train': 2377}

How to Use

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