Instructions to use joyc360/deepfakes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joyc360/deepfakes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="joyc360/deepfakes") 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("joyc360/deepfakes") model = AutoModelForImageClassification.from_pretrained("joyc360/deepfakes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from joyc360/deepfakes: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/joyc360/deepfakes/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://joyc360/deepfakes/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/joyc360/deepfakes/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 15e36d6a7de34d2335cc8e5e0b266c37fdf2bc7bbd0c8f076c1c5e13f368a2b5
- Size of remote file:
- 343 MB
- SHA256:
- 5aa55ecca854df1b562daf78ce91ecae801511595f01d49fba19fd94d17b75f9
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