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") - Notebooks
- Google Colab
- Kaggle
Upload ONNX weights
#2 opened almost 2 years ago
by
Xenova
Adding `safetensors` variant of this model
#1 opened about 2 years ago
by
SFconvertbot