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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from microsoft/xclip-base-patch16-16-frames: direct link, hf CLI and curl.
- Browser
- Download file 780 MB
-
https://huggingface.co/microsoft/xclip-base-patch16-16-frames/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/xclip-base-patch16-16-frames/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/xclip-base-patch16-16-frames/resolve/main/pytorch_model.bin
780 MB
- Xet hash:
- 43985b4773f0e90423e6e3c4101257501c80e24a438301e8072d9d6f3f52b361
- Size of remote file:
- 780 MB
- SHA256:
- d6b66745d2f632d7bcf191752b15eaf3ca5a895e622404a62bea7a00397fd91c
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