Instructions to use asmaa1/videomae-base-groub23-24-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-groub23-24-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-groub23-24-finetuned-SLT-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("asmaa1/videomae-base-groub23-24-finetuned-SLT-subset") model = AutoModelForVideoClassification.from_pretrained("asmaa1/videomae-base-groub23-24-finetuned-SLT-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74a05ebf6f365efab6cfe45655bc117eca552f1bf459fbcf23ac160341979a2f
- Size of remote file:
- 345 MB
- SHA256:
- 562ca06c74ac0dda02352ff6f07c52d7761066afad50c9ba4dfde0708f00743b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.