Instructions to use cocovani/test_with_sdfvd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cocovani/test_with_sdfvd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="cocovani/test_with_sdfvd")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("cocovani/test_with_sdfvd") model = AutoModelForVideoClassification.from_pretrained("cocovani/test_with_sdfvd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42f859b74dfc2ae9206f543130e57ff584a3a507a5a686fefea9b47e1a4b03a3
- Size of remote file:
- 5.3 kB
- SHA256:
- 2a4abe70fd64abc03b92934e644d92ca34aea8af0864d68c6a8293978899ec82
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.