Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-hmdb-4-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-4-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-4-shot")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-hmdb-4-shot") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-hmdb-4-shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c4d64f5a0eae9442041a17b51b94838feed33b9616edfa12a11a667b0cce59b
- Size of remote file:
- 780 MB
- SHA256:
- 74e990e05850128c298192ff8fea7bee3d9d4a82d46ba47919b680d71580a691
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.