Instructions to use asmaa1/videomae-base-groub13-14-finetuned-SLT-subset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asmaa1/videomae-base-groub13-14-finetuned-SLT-subset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="asmaa1/videomae-base-groub13-14-finetuned-SLT-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("asmaa1/videomae-base-groub13-14-finetuned-SLT-subset") model = AutoModelForVideoClassification.from_pretrained("asmaa1/videomae-base-groub13-14-finetuned-SLT-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abefe5b1b2f193e8fe8c95317b64301861420d56d0a12dc52b1e5c2077b63d72
- Size of remote file:
- 4.09 kB
- SHA256:
- 4d202d80a8a6475bc7ddbf65355b20b5100498b6556d80b10190af9112c8b060
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.