Instructions to use SivaResearch/Fake_Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SivaResearch/Fake_Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SivaResearch/Fake_Detection", 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("SivaResearch/Fake_Detection", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1e9e4ab8248352b6161e53a8d397f20a6e95a3493acb7641fcfefc20c319e47
- Size of remote file:
- 94.3 MB
- SHA256:
- 55b70cf572d8fcd290f26b474c9b1a0c8379f324df3f6932555ea0a633b89c6a
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