Video Classification
Transformers
PyTorch
Safetensors
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("microsoft/xclip-base-patch16") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 673b0fe56e605cc4c506eb8f44e85972d29d779f61384b471638ed4e9f309d00
- Size of remote file:
- 780 MB
- SHA256:
- b5db836d57b7e22001cb2bab581719e910b4c571d926fe839b5d07bc0ba2d03e
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