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:
- 670f384965a646c308ca6702eba8cff50e8c4e6e129ce34df4f0b5e41121ce3a
- Size of remote file:
- 373 kB
- SHA256:
- ce8e0bff2bd98fd545c2ec55f4f772ef88e98bafbb6b9456eabddd8c904ca2af
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