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| title: README | |
| emoji: 💻 | |
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| sdk: gradio | |
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| sdk_version: 4.44.0 | |
| <p style="text-align:center;"> | |
| 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**. | |
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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> | |
| <span style="font-size:18px">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%).</span> | |
| The following table presents the test metric results for all the models implemented in this project. | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/66d6f28a19214d743ca1eb43/q6g7SAHT-enMFOkoXqxWc.png" alt="Description" width="600" style="display: block; margin-left: auto; margin-right: auto;"/> | |
| <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 <span style="color:indianred"> VIT model</span>, achieving the best results according to all the criteria we set in the initial phase.**.</span> | |
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