Instructions to use DataScienceProject/Vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataScienceProject/Vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DataScienceProject/Vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DataScienceProject/Vit", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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#### Factors
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#### Metrics
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### Results
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# Model Card for Model ID
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## Model Details
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### Results
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