Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-ucf-2-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-2-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-2-shot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-ucf-2-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-ucf-2-shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/xclip-base-patch16-ucf-2-shot: direct link, hf CLI and curl.
- Browser
- Download file 780 MB
-
https://huggingface.co/microsoft/xclip-base-patch16-ucf-2-shot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/xclip-base-patch16-ucf-2-shot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/xclip-base-patch16-ucf-2-shot/resolve/main/pytorch_model.bin
780 MB
- Xet hash:
- c2dda51908419e3677389dbf2510ea3b797a73d556797f28fcc7f0ebd1082a23
- Size of remote file:
- 780 MB
- SHA256:
- 17cbf411f62081c621a7f1551716edabc74ca48afaed3d86d73d80182990cbb0
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