Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-kinetics-600-16-frames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch16-kinetics-600-16-frames with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch16-kinetics-600-16-frames")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch16-kinetics-600-16-frames") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-kinetics-600-16-frames") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d02cd556a64d674611d6d943a48fcb965ff07de21c2129f1ab3c6c01f2edb424
- Size of remote file:
- 780 MB
- SHA256:
- 5dd940c1e5b054d58210edf285a7e91021eb5b9c226e7f58c624420394e8736f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.