Update README.md

README.md

@@ -6,6 +6,7 @@ metrics:
 - accuracy
 tags:
 - art
+base_model: google/vit-base-patch16-224
 ---
 # Model Card for Model ID
 
@@ -15,45 +16,46 @@ This model is designed for classifying images as either 'real art' or 'fake art'
 
 ### Model Description
 
-This model utilizes a CNN architecture augmented with ELA techniques to classify images into two categories: 'real art' and 'fake art'. The CNN is trained to detect patterns and features, while ELA highlights inconsistencies or artifacts that are characteristic of AI-generated images.
-
+This model leverages the Vision Transformer (ViT) architecture, which applies self-attention mechanisms to process images.
+The model classifies images into two categories: 'real art' and 'fake art'.
+It captures intricate patterns and features that help in distinguishing between the two categories without the need for Convolutional Neural Networks (CNNs).
 
 ### Direct Use
 
-This model can be used to classify images as 'real art' or 'fake art' based on the presence of artifacts and features characteristic of each category.
-
+This model can be used to classify images as 'real art' or 'fake art' based on visual features learned by the Vision Transformer.
 
 
 ### Out-of-Scope Use
 
-The model may not perform well on images outside the scope of art or where the visual characteristics are drastically different from those in the training dataset. It is not suitable for use in other domains like medical imaging or non-artistic visual data.
+The model may not perform optimally on images outside the art domain or on artworks 
+with significantly different visual characteristics compared to those in the training dataset.
+It is not suitable for medical imaging or other non-artistic visual tasks.
 
 ## Bias, Risks, and Limitations
 
-Users should be aware of the limitations and potential biases of the model, including the possibility of misclassification for artworks that significantly differ from those in the training set. Continuous evaluation and refinement may be required to maintain accuracy.
+Users should be mindful of the model's limitations and potential biases, particularly regarding artworks that differ significantly from the training data.
+Regular updates and evaluations may be necessary to ensure the model remains accurate and effective.
 
 ### Recommendations
 
 
 ## How to Get Started with the Model
 
-Prepare Data: Organize your images into appropriate folders and preprocess them by resizing and normalizing.
-Train the Model: Use the provided code to train the model on your dataset.
-Evaluate: Test the model on a separate set of images to assess performance.
+Prepare Data: Organize your images into appropriate folders, ensuring they are resized and normalized.
+Train the Model: Utilize the provided code to train the Vision Transformer model on your dataset.
+Evaluate: Assess the model's performance on a separate test set of images.
 
-[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
 Dataset: [Link to dataset or description]
-Preprocessing: Images are resized, normalized, and enhanced with ELA to highlight features.
+Preprocessing: Images are resized, normalized, and prepared for input to the Vision Transformer.
 
 ### Training Procedure
 
-Images are resized to a uniform dimension and normalized. ELA is applied to emphasize artifacts in the images.
-
+Images are resized to a uniform dimension and normalized. The Vision Transformer model is then trained on these preprocessed images.
 
 
 #### Training Hyperparameters