vit-base-oxford-brain-tumor_x-ray

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

vit-base-oxford-brain-tumor_x-ray

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:

• Loss: 0.2882
• Accuracy: 0.9231
• Precision: 0.9231
• Recall: 0.9231
• F1: 0.9231

Model description

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.

Intended uses & limitations

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

Training and evaluation data

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

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 20
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1