Video Classification
Transformers
PyTorch
Safetensors
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch32-16-frames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch32-16-frames with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch32-16-frames")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("microsoft/xclip-base-patch32-16-frames") model = AutoModel.from_pretrained("microsoft/xclip-base-patch32-16-frames") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b51db646bc00a6f3b66db412cd1ca15cef86a1faab4c58983638489346d74916
- Size of remote file:
- 786 MB
- SHA256:
- f67aa8bc3f994af8685621a7fcd571a7b2495accf43455a2ee1da5e7c7038939
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