Update README.md

README.md

@@ -12,10 +12,9 @@ sdk_version: 4.44.0
 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.
-For this project, we utilized several models, including CNN, ELA, RESNET50, and VIT.
+For this project, we utilized several models, including **CNN, ELA, RESNET50, and VIT**.
 
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 After building and running these models and evaluating their prediction results, this is the evaluation of Results:
 
 <span style="font-size:18px"> 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%).</span>
@@ -28,6 +27,6 @@ The following table presents the test metric results for all the models implemen
 <img src="https://cdn-uploads.huggingface.co/production/uploads/66d6f28a19214d743ca1eb43/q6g7SAHT-enMFOkoXqxWc.png" alt="Description" width="500" style="display: block; margin-left: auto; margin-right: auto;"/>
 
 
-**After comparing the different results, it can be seen that the model with the highest performance across all metrics is the ***ViT*** model, achieving the best results according to all the criteria we set in the initial phase.**
+<span style="font-size:20px">**After comparing the different results, it can be seen that the model with the highest performance across all metrics is the ***ViT*** model, achieving the best results according to all the criteria we set in the initial phase.**.</span>
 
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