Video Classification
Transformers
PyTorch
English
afmoe
deepfake-detection
clip
vit
spatiotemporal-adapters
bf16
reproducibility
Instructions to use Begrudging/deepfake-detector-dfd-sota with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Begrudging/deepfake-detector-dfd-sota with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="Begrudging/deepfake-detector-dfd-sota")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Begrudging/deepfake-detector-dfd-sota", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Begrudging/deepfake-detector-dfd-sota: direct link, hf CLI and curl.
- Browser
- Download file 1.28 GB
-
https://huggingface.co/Begrudging/deepfake-detector-dfd-sota/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Begrudging/deepfake-detector-dfd-sota/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Begrudging/deepfake-detector-dfd-sota/resolve/main/pytorch_model.bin
1.28 GB
- Xet hash:
- 4dbb5b33026b5762157d032266db18a72778919ccfb7859aa242ced43abbb6a9
- Size of remote file:
- 1.28 GB
- SHA256:
- 0ec26a4a79ee33a38d725ed2240606fa451397ec5262f2f28fbbed1ac46ddc2f
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