Instructions to use MCG-NJU/videomae-base-short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MCG-NJU/videomae-base-short with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="MCG-NJU/videomae-base-short")# Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-short") model = AutoModelForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MCG-NJU/videomae-base-short: direct link, hf CLI and curl.
- Browser
- Download file 377 MB
-
https://huggingface.co/MCG-NJU/videomae-base-short/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MCG-NJU/videomae-base-short/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MCG-NJU/videomae-base-short/resolve/main/pytorch_model.bin
377 MB
- Xet hash:
- 90ac0b8eba7dc3a634d8bf7aacb0d3cdf35153a40951037e36be5467d5c824cd
- Size of remote file:
- 377 MB
- SHA256:
- 3e1d7c6ed86b5285ad1608cc996fe8333794646c72ead9b9ad401f38921384ae
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