Instructions to use muneeb1812/videomae-base-fake-video-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muneeb1812/videomae-base-fake-video-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="muneeb1812/videomae-base-fake-video-classification")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("muneeb1812/videomae-base-fake-video-classification") model = AutoModelForVideoClassification.from_pretrained("muneeb1812/videomae-base-fake-video-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from muneeb1812/videomae-base-fake-video-classification: direct link, hf CLI and curl.
- Browser
- Download file 345 MB
-
https://huggingface.co/muneeb1812/videomae-base-fake-video-classification/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://muneeb1812/videomae-base-fake-video-classification@refs/pr/2/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/muneeb1812/videomae-base-fake-video-classification/resolve/refs%2Fpr%2F2/pytorch_model.bin
345 MB
- Xet hash:
- 44e9248ee857115c58cb61385d358042b5220781ede8fa2db9c9c53c118020aa
- Size of remote file:
- 345 MB
- SHA256:
- 274401d5eaa6701f020cf02e22e24778b27a24c3fdc57074a3a22506837779d7
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