Update README.md

README.md

@@ -40,6 +40,7 @@ It achieves the following results on the evaluation set:
 ## Model description
 
 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/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.
@@ -50,9 +51,11 @@ This must be used for classification of x-ray images of the brain to diagnose of
 
 ## Training and evaluation data
 
-More information needed
+The model was fine-tuned in the dataset [Mahadih534/brain-tumor-dataset](https://huggingface.co/datasets/Mahadih534/brain-tumor-dataset) that contains 253 brain images. This dataset was originally created by Yousef Ghanem.
+
 
 ## Training procedure
+The model was fine-tuned 
 
 ### Training hyperparameters