Instructions to use asmaa1/videomae-base-groub21-22-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-groub21-22-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-groub21-22-finetuned-SLT-subset")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("asmaa1/videomae-base-groub21-22-finetuned-SLT-subset") model = AutoModelForVideoClassification.from_pretrained("asmaa1/videomae-base-groub21-22-finetuned-SLT-subset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96f01235b7b43f19042a9cd93bf532e1de91a961927c8b3005a5a350d3b554bb
- Size of remote file:
- 345 MB
- SHA256:
- c1ce8315a8b07718e0060532235d8c70f2d89c161816d89d73a57e362516e43f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.