Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-hmdb-8-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16-hmdb-8-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16-hmdb-8-shot")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-hmdb-8-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-hmdb-8-shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e525455e35f7b889b0131b033bf59d4de41c5dd7a534a5e91e56e8f3a05848e
- Size of remote file:
- 780 MB
- SHA256:
- 409cba200065ca1a9fc2a74980b5c164f29d524b41811cc0e2e672b88ac61668
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