update model card

README.md

@@ -31,24 +31,26 @@ should probably proofread and complete it, then remove this comment. -->
 
 # alzheimer-image-classification-google-vit-base-patch16
 
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [Alzheimer MRI data](https://www.kaggle.com/datasets/sachinkumar413/alzheimer-mri-dataset).
 It achieves the following results on the evaluation set:
 - Loss: 0.2127
 - Accuracy: 0.9261
 
 ## Model description
 
-More information needed
+The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.
 
-## Intended uses & limitations
+Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
+
+Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
 
-More information needed
+By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image
+
+## Intended uses & limitations
 
-## Training and evaluation data
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you.
 
-More information needed
 
-## Training procedure
 
 ### Training hyperparameters