This model is a fine-tuned version of google/vit-base-patch16-224 on the Mahadih534/brain-tumor-dataset dataset. It achieves the following results on the evaluation set:

This model is a fine-tuned version of google/vit-base-patch16-224, which is a Vision Transformer (ViT)

ViT model is originaly a transformer encoder model pre-trained and fine-tuned on ImageNet 2012. It was introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al. The model processes images as sequences of 16x16 patches, adding a [CLS] token for classification tasks, and uses absolute position embeddings. Pre-training enables the model to learn rich image representations, which can be leveraged for downstream tasks by adding a linear classifier on top of the [CLS] token. The weights were converted from the timm repository by Ross Wightman.

This must be used for classification of x-ray images of the brain to diagnose of brain tumor.

The model was fine-tuned in the dataset Mahadih534/brain-tumor-dataset that contains 253 brain images. This dataset was originally created by Yousef Ghanem.

The original dataset was splitted into training and evaluation subsets, 80% for training and 20% for evaluation. For robust framework evaluation, the evaluation subset is further split into two equal parts for validation and testing. This results in three distinct datasets: training, validation, and testing

The following hyperparameters were used during training: