--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface ---
disha07/brainybrain
### Dataset Labels ``` ['tumor'] ``` ### Number of Images ```json {'valid': 234, 'test': 125, 'train': 2377} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python 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](https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9/dataset/1?ref=roboflow2huggingface) ### 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