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:
- 02fb66049ee0ec6b3881bb7a5b640e4c3e531f177d7624ee88e6570c6ae82727
- Size of remote file:
- 285 kB
- SHA256:
- 2e6fa9f6c2a642af57a388ab23b31bf5662c170c6ecaa384997ed72b105f4563
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.