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:
- 939c9beebb4708ddea5d1becc64b9327fef7d89fff9d6e11b720bc45c3f07371
- Size of remote file:
- 493 kB
- SHA256:
- 9bf3f8ed5aae02378efbe97e0354e405736b9a78f47417fac792c8072529ecdc
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