Update README.md

README.md

@@ -1,43 +1,62 @@
 ---
 license: apache-2.0
-base_model: motheecreator/vit-Facial-Expression-Recognition
+base_model: google/vit-base-patch16-224-in21k
 tags:
 - generated_from_trainer
-datasets:
-- image_folder
 metrics:
 - accuracy
 model-index:
-- name: vit-Facial-Expression-Recognition
+- name: Facial Expression Recognition
   results:
   - task:
       name: Image Classification
       type: image-classification
-    dataset:
-      name: image_folder
-      type: image_folder
-      config: default
-      split: train
-      args: default
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7390639923591213
+      value: 0.8571428571428571
+pipeline_tag: image-classification
 ---
 
 <!-- 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-Facial-Expression-Recognition
+# Vision Transformer (ViT) for Facial Expression Recognition Model Card
 
-This model is a fine-tuned version of [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition) on the image_folder dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.8219
-- Accuracy: 0.7391
+## Model Overview
+
+- **Model Name:** [motheecreator/vit-Facial-Expression-Recognition](https://huggingface.co/motheecreator/vit-Facial-Expression-Recognition)
+
+- **Task:** Facial Expression/Emotion Recognition
+
+- **Datasets:** [FER2013](https://www.kaggle.com/datasets/msambare/fer2013), [MMI Facial Expression Database](https://mmifacedb.eu)
+
+- **Model Architecture:** [Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)
+
+- **Finetuned from model:** [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
+
+- Loss: 0.4353
+- Accuracy: 0.8571
 
 ## Model description
 
-More information needed
+The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition. 
+
+It is trained on the FER2013 and MMI facial Expression datasets , which consist of facial images categorized into seven different emotions:
+- Angry
+- Disgust
+- Fear
+- Happy
+- Sad
+- Surprise
+- Neutral
+
+## Data Preprocessing
+
+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.
 
 ## Intended uses & limitations
 
@@ -53,25 +72,16 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 32
+- total_train_batch_size: 128
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 5
-
-### Training results
+- num_epochs: 10
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.7175        | 1.0   | 654  | 0.7081          | 0.7309   |
-| 0.6952        | 2.0   | 1308 | 0.6931          | 0.7379   |
-| 0.5041        | 3.0   | 1962 | 0.7038          | 0.7444   |
-| 0.2461        | 4.0   | 2617 | 0.7843          | 0.7393   |
-| 0.1846        | 5.0   | 3270 | 0.8219          | 0.7391   |
 
 
 ### Framework versions
@@ -79,4 +89,4 @@ The following hyperparameters were used during training:
 - Transformers 4.36.0
 - Pytorch 2.0.0
 - Datasets 2.1.0
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0