Instructions to use hf-internal-testing/tiny-random-TimesformerForVideoClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-TimesformerForVideoClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="hf-internal-testing/tiny-random-TimesformerForVideoClassification")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-TimesformerForVideoClassification") model = AutoModelForVideoClassification.from_pretrained("hf-internal-testing/tiny-random-TimesformerForVideoClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c78594ada7277d7222934c6da6d285fef17040eb57883fcdd9d6613e95f0d75a
- Size of remote file:
- 262 kB
- SHA256:
- 1024833c72f4fce5b232afa16a8537fb45e4e17312b20ddac935f68446cfa826
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