Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-16-frames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16-16-frames with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16-16-frames")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-16-frames") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-16-frames", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a27cb24ab4d13213780945273fb5db9f686bf32dc2741f19b5a888e94fe8a57
- Size of remote file:
- 780 MB
- SHA256:
- 752c56d1d5a2ad74fa7e89a07bd8eafecd6de1c6c804ca647f8b674416f5e896
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