Instructions to use sandesh2233/Deepfakes_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sandesh2233/Deepfakes_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sandesh2233/Deepfakes_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("sandesh2233/Deepfakes_detection") model = AutoModelForImageClassification.from_pretrained("sandesh2233/Deepfakes_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sandesh2233/Deepfakes_detection: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/sandesh2233/Deepfakes_detection/resolve/main/training_args.bin
- Command line
-
hf download hf://sandesh2233/Deepfakes_detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sandesh2233/Deepfakes_detection/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 1c22d2fbc8bfaca0d47f275dbdf8566f402c59b7e3ee6c571af43984afd3c5c3
- Size of remote file:
- 5.2 kB
- SHA256:
- 79a42ae28b395ccf7ff0e18f1120f3d108c7f48a8ce2d975d4e63c9efb73f2cf
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