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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b164ed66a11d3a13d63f8f45c630f11bca3b5452795a1ab711d5fc15995a76a1
- Size of remote file:
- 285 kB
- SHA256:
- 98b016771805d65ee7d14a5c5c06c0882fea086d8cb9f9a40c36725b087cd637
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