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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### 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
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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 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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