Instructions to use Hemg/Deepfake-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/Deepfake-image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Hemg/Deepfake-image") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Hemg/Deepfake-image") model = AutoModelForImageClassification.from_pretrained("Hemg/Deepfake-image", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Hemg/Deepfake-image: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/Hemg/Deepfake-image/resolve/main/training_args.bin
- Command line
-
hf download hf://Hemg/Deepfake-image/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Hemg/Deepfake-image/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- a73101ee73165dc2191946973f31598a1d037bac3a5f6ff0472b2613eea98deb
- Size of remote file:
- 4.86 kB
- SHA256:
- b28b3b1b89d0eb70d6c1f2d0133fdabdb4dfd865456550b104d30dfedc2888a4
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