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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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- precision |
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- f1 |
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- recall |
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tags: |
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- art |
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base_model: google/vit-base-patch16-224 |
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datasets: |
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- DataScienceProject/Art_Images_Ai_And_Real_ |
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pipeline_tag: image-classification |
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library_name: transformers |
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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' or 'fake-AI generated' using a Vision Transformer (VIT) . |
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Our goal is to accurately classify the source of the image with at least 85% accuracy and achieve at least 80% in the recall test. |
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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. |
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The model classifies images into two categories: 'real ' and 'fake - AI generated'. |
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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 well on images outside the scope of art or where the visual characteristics are drastically different from those in the training dataset. |
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### Recommendations |
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Run the traning code on pc with an nvidia gpu better then rtx 3060 and at least 6 core cpu / use google collab. |
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## How to Get Started with the Model |
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Prepare Data: Organize your images into appropriate folders and run the code. |
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## model architecture |
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## Training Details |
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-Dataset: DataScienceProject/Art_Images_Ai_And_Real_ |
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Preprocessing: Images are resized, converted to 'rgb' format , transformed into tensor and stored in special torch dataset. |
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#### Training Hyperparameters |
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optimizer = optim.Adam(model.parameters(), lr=0.001) |
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num_epochs = 10 |
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criterion = nn.CrossEntropyLoss() |
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## Evaluation |
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The model takes 15-20 minutes to run , based on our dataset , equipped with the following pc hardware: cpu :i9 13900 ,ram: 32gb , gpu: rtx 3080 |
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your mileage may vary. |
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### Testing Data, Factors & Metrics |
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-precision |
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-recall |
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-f1 |
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-confusion_matrix |
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-accuracy |
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### Results |
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-test accuracy = 0.92 |
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-precision = 0.893 |
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-recall = 0.957 |
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-f1 = 0.924 |
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#### Summary |
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This model is by far the best of what we tried (CNN , Resnet , CNN + ELA). |
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