Instructions to use Wvolf/ViT_Deepfake_Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wvolf/ViT_Deepfake_Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Wvolf/ViT_Deepfake_Detection") 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("Wvolf/ViT_Deepfake_Detection") model = AutoModelForImageClassification.from_pretrained("Wvolf/ViT_Deepfake_Detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download .gitattributes from Wvolf/ViT_Deepfake_Detection: direct link, hf CLI and curl.
- Browser
- Download file 245 Bytes
-
https://huggingface.co/Wvolf/ViT_Deepfake_Detection/resolve/main/.gitattributes
- Command line
-
hf download hf://Wvolf/ViT_Deepfake_Detection/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/Wvolf/ViT_Deepfake_Detection/resolve/main/.gitattributes
245 Bytes
| checkpoint-22604/optimizer.pt filter=lfs diff=lfs merge=lfs -text | |
| checkpoint-22604/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text | |
| pytorch_model.bin filter=lfs diff=lfs merge=lfs -text | |
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |