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:
- e075871f51a2d4959a5aafbfc4371272612902cc67ef3398baed5abaf7383d23
- Size of remote file:
- 343 MB
- SHA256:
- 7a653ae69f0c39545941da9b7d6ea309aa81c7d69946cdf06f4ea10b6a24eb0d
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