Instructions to use Arunisto/brain_tumor_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arunisto/brain_tumor_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Arunisto/brain_tumor_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Arunisto/brain_tumor_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- Arunisto/brain_tumor_dataset
language:
- en
base_model:
- microsoft/swin-tiny-patch4-window7-224
pipeline_tag: image-classification
library_name: transformers
tags:
- computer_vision
- image_classification
Pre-trained swin transformer model for brain tumor classification
Model Description
This is pre-trained swintransformer model for classify brain tumor.
Training Dataset
This model trained with MRI Scan images of brain tumor healthy and tumor
Dataset Format
- Images: JPEG/PNG formats
- Dataset: Arunisto/brain_tumor_dataset
Training Details
- Model: microsoft/swin-tiny-patch4-window7-224
- Framework: Transformers
- Epochs: 10
Usage
This model can be used to detect two type of brain condition is healthy or tumor
Limitations
- The model is perform well only with brain top-side only, and for healthy it's only predicted with the score of 65% only
Future Work
- needs to run more epochs
- trying to classify different tumor conditions
License
Apache 2.0
Citation
If you use this model in your research or projects, please cite it as follows:
@misc{arun-arunisto2024yolov8_bike_odometer,
title={Pre-trained swin transformer model for brain tumor classification},
author={Arun Arunisto},
year={2024},
howpublished={\url{https://huggingface.co/Arunisto/brain_tumor_classification}},
}