Update README.md

README.md

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 Our project aims to develop an image classification system capable of distinguishing between paintings created by humans and those generated by artificial intelligence.
 
 By leveraging a combination of classification techniques and machine learning, we aim to create a model that can accurately classify different types of images and detect the critical differences between works of art.
@@ -15,7 +16,8 @@ For this project, we utilized several models, including CNN, ELA, RESNET50, and
 
 After building and running these models and evaluating their prediction results, this is the evaluation of Results:
 
-It can be observed that, according to the *Accuracy* metric, two models meet the desired threshold of at least 85%, which are: the *CNN+ELA* model (85%) and the *ViT* model (92%).
+It can be observed that, according to the *Accuracy* metric, <span style="font-size:20px"> two models meet the desired threshold of at least 85%, which are: the *CNN+ELA* model (85%) and the *ViT* model (92%).</span>
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 According to the *Recall* metric, we set a performance threshold of at least 80%, and there are two models that meet this requirement: the *CNN+ELA* model (83.5%) and the *ViT* model (95.7%).