Instructions to use bhaskari/deepfake-detector-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- OpenCLIP
How to use bhaskari/deepfake-detector-weights with OpenCLIP:
import open_clip model, preprocess_train, preprocess_val = open_clip.create_model_and_transforms('hf-hub:bhaskari/deepfake-detector-weights') tokenizer = open_clip.get_tokenizer('hf-hub:bhaskari/deepfake-detector-weights') - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66179497635e3661dcc74f4c956806e88ec82258b059eb7836274cd01fd026e9
- Size of remote file:
- 4.8 GB
- SHA256:
- 83580b28798d7a6219443ad953ef3817bc6d5c92a812ad4e7e7d27ae1ab83e4e
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