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 AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch32-16-frames") model = AutoModel.from_pretrained("microsoft/xclip-base-patch32-16-frames") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3f6d45175afd2aca4b0e9984a0c1ae6b54bd046bcc452289f542d0b54c89586
- Size of remote file:
- 787 MB
- SHA256:
- 02de90afd3075f4623195d6a29949eaf0165159088daf16057f1c7b99c53f10b
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