Update README.md

README.md

@@ -6,6 +6,7 @@ tags:
 - generated_from_trainer
 datasets:
 - imagefolder
+- Mahadih534/brain-tumor-dataset
 metrics:
 - accuracy
 - precision
@@ -36,6 +37,7 @@ model-index:
     - name: F1
       type: f1
       value: 0.9230769230769231
+pipeline_tag: image-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -53,17 +55,25 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+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.
 
 ## Intended uses & limitations
 
-More information needed
+This must be used for classification of x-ray images of the brain to diagnose of brain tumor.
 
 ## 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.
+
+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 procedure
 
 ### Training hyperparameters
 
@@ -91,4 +101,4 @@ The following hyperparameters were used during training:
 - Transformers 4.41.2
 - Pytorch 2.3.0+cu121
 - Datasets 2.20.0
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1