Video Classification
Transformers
PyTorch
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch16-kinetics-600 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 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")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("microsoft/xclip-base-patch16-kinetics-600") model = AutoModel.from_pretrained("microsoft/xclip-base-patch16-kinetics-600") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd41131da911568c4b692c88f615d927fbfee4d38a85027e7e02127c35e70c6c
- Size of remote file:
- 780 MB
- SHA256:
- 927a73a0d9ebc3ac4087dae59d3f831de65a29d37a2accb2b541ca4377838e69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.