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:
- aa01b154f369c5585221658a5e0b2e325b1f97b120a707f50aa71dddac2e98a4
- Size of remote file:
- 687 MB
- SHA256:
- f79103d18f35fce10ed3263f681c4583d0852f6a22a308dc9202b63818c1675c
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