Instructions to use hf-tiny-model-private/tiny-random-TimesformerForVideoClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-TimesformerForVideoClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="hf-tiny-model-private/tiny-random-TimesformerForVideoClassification")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-TimesformerForVideoClassification") model = AutoModelForVideoClassification.from_pretrained("hf-tiny-model-private/tiny-random-TimesformerForVideoClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e956f08b0ee86ac763e4b3518c8a77ae206cccb29401552f85284c3ee819885
- Size of remote file:
- 262 kB
- SHA256:
- d13f642b5d18608132b4e0569a70d8a5b79296f7eebed177e30ddd160f0c2fda
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