Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-ucf-4-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-4-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-4-shot")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-ucf-4-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-ucf-4-shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 600ecea83221bb27071c2e197368ea28a5ec6a94340151f90f26c09a8b2775c2
- Size of remote file:
- 780 MB
- SHA256:
- 5abf3a3e14a927e281331c44b026a4443403e4ff305837fcb078f17a388f9462
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