Instructions to use dima806/deepfake_vs_real_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/deepfake_vs_real_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/deepfake_vs_real_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/deepfake_vs_real_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/deepfake_vs_real_image_detection") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82a92b5201cbcab424e21419899f60d4f50abd59eef4046913fc1980ba167485
- Size of remote file:
- 4.41 kB
- SHA256:
- 43f6d25546b8da26ab17504f529643fe71525e1a77fe75ca99c8e672a1e11a7c
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