| --- |
| license: unknown |
| language: |
| - en |
| metrics: |
| - accuracy |
| tags: |
| - art |
| base_model: google/vit-base-patch16-224 |
| datasets: |
| - DataScienceProject/Art_Images_Ai_And_Real_ |
| pipeline_tag: image-classification |
| library_name: transformers |
| --- |
| # Model Card for Model ID |
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| This model is designed for classifying images as either 'real art' or 'fake art' using a Convolutional Neural Network (CNN) combined with Error Level Analysis (ELA). The CNN extracts features from images, and ELA enhances artifacts that help distinguish between real and AI-generated art. |
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| ## Model Details |
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| ### Model Description |
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| 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). |
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| ### Direct Use |
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| This model can be used to classify images as 'real art' or 'fake art' based on visual features learned by the Vision Transformer. |
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| ### Out-of-Scope Use |
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| 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. |
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| ## Bias, Risks, and Limitations |
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| 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. |
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| ### Recommendations |
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| ## How to Get Started with the Model |
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| 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. |
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| ## Training Details |
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| ### Training Data |
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| Dataset: [Link to dataset or description] |
| Preprocessing: Images are resized, normalized, and prepared for input to the Vision Transformer. |
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| ### Training Procedure |
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| Images are resized to a uniform dimension and normalized. The Vision Transformer model is then trained on these preprocessed images. |
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| #### Training Hyperparameters |
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| ## Evaluation |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| #### Factors |
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| #### Metrics |
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| ### Results |
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| #### Summary |
