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.
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 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.
-After building and running these models and evaluating their prediction results, the VIT model achieved the highest score.
-Therefore, we selected it to effectively classify and differentiate the images.
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+After building and running these models and evaluating their prediction results, this is the evaluation of Results:
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+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%).
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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%).
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+The following table presents the test metric results for all the models implemented in this project.
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+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66d6f28a19214d743ca1eb43/iOp2BvuTWfiRsJE9cGqxT.png)
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+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.