Image Classification
Transformers
English
art
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- license: unknown
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+ ---
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+ license: unknown
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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+ tags:
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+ - art
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+ ---
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+ # 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 utilizes a CNN architecture augmented with ELA techniques to classify images into two categories: 'real art' and 'fake art'. The CNN is trained to detect patterns and features, while ELA highlights inconsistencies or artifacts that are characteristic of AI-generated images.
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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 the presence of artifacts and features characteristic of each category.
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+ ### Out-of-Scope Use
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+ The model may not perform well on images outside the scope of art or where the visual characteristics are drastically different from those in the training dataset. It is not suitable for use in other domains like medical imaging or non-artistic visual data.
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+ ## Bias, Risks, and Limitations
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+ Users should be aware of the limitations and potential biases of the model, including the possibility of misclassification for artworks that significantly differ from those in the training set. Continuous evaluation and refinement may be required to maintain accuracy.
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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 and preprocess them by resizing and normalizing.
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+ Train the Model: Use the provided code to train the model on your dataset.
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+ Evaluate: Test the model on a separate set of images to assess performance.
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+ [More Information Needed]
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+ ## Training Details
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+ ### Training Data
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+ Dataset: [Link to dataset or description]
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+ Preprocessing: Images are resized, normalized, and enhanced with ELA to highlight features.
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+ ### Training Procedure
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+ Images are resized to a uniform dimension and normalized. ELA is applied to emphasize artifacts in the 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