Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-ucf-16-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16-ucf-16-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16-ucf-16-shot")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-ucf-16-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-ucf-16-shot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6342346ce4c8ddff703e2ae45578ef7e073cfdecfec53a95c11d18066faae98a
- Size of remote file:
- 780 MB
- SHA256:
- 8d0942634299dde9365615d5e54d76a91e71f56f758b89515b1dffb2a5d4331f
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