Instructions to use MCG-NJU/videomae-base-ssv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MCG-NJU/videomae-base-ssv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="MCG-NJU/videomae-base-ssv2")# Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-ssv2") model = AutoModelForPreTraining.from_pretrained("MCG-NJU/videomae-base-ssv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7291f84a1c85383d4b88abbebcd40c89c9514960a7283da471dab6eebffb2ab5
- Size of remote file:
- 377 MB
- SHA256:
- 5c45daffe6fd1b5589ba91c2fece9cb53acc210fafed872328629cd1d3f3cf86
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