Instructions to use Uzef/Deepfake-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Uzef/Deepfake-Image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Uzef/Deepfake-Image", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("Uzef/Deepfake-Image", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Create deepfakeconfig.py
Browse files- deepfakeconfig.py +7 -0
deepfakeconfig.py
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from transformers import PretrainedConfig
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import torch
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class DeepFakeConfig(PretrainedConfig):
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model_type = "ResNet"
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def __init__(self,**kwargs):
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super().__init__(**kwargs)
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self.DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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