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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ce5fda315aa298610cec5ec6377955aa9a4d9eb893b1c24285469330cfd2f8e
- Size of remote file:
- 343 MB
- SHA256:
- b38e7d3de2be44b224cb4cffe881a0c4b75f7283c5d4a2017fbadd58bd41fdef
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