Update README.md

README.md

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 license: apache-2.0
-base_model: motheecreator/vit-Facial-Expression-Recognition
+base_model: google/vit-base-patch16-224-in21k
 tags:
 - generated_from_trainer
 metrics:
@@ -15,24 +15,31 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vit-Facial-Expression-Recognition
 
-This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the None 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 [FER 2013](https://www.kaggle.com/datasets/msambare/fer2013),[MMI Facial Expression Database](https://mmifacedb.eu/), and [AffectNet dataset](https://www.kaggle.com/datasets/noamsegal/affectnet-training-data) datasets.
 It achieves the following results on the evaluation set:
 - Loss: 0.4503
 - Accuracy: 0.8434
 
 ## Model description
 
-More information needed
+The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition. 
 
-## Intended uses & limitations
+It is trained on the FER2013, MMI facial Expression, and AffectNet datasets, which consist of facial images categorized into seven different emotions:
+- Angry
+- Disgust
+- Fear
+- Happy
+- Sad
+- Surprise
+- Neutral
 
-More information needed
 
-## Training and evaluation data
+## Data Preprocessing
 
-More information needed
-
-## Training procedure
+The input images are preprocessed before being fed into the model. The preprocessing steps include:
+- **Resizing:** Images are resized to the specified input size.
+- **Normalization:** Pixel values are normalized to a specific range.
+- **Data Augmentation:** Random transformations such as rotations, flips, and zooms are applied to augment the training dataset.
 
 ### Training hyperparameters