Instructions to use LinStevenn/videomae-base-readminds-assignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinStevenn/videomae-base-readminds-assignment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="LinStevenn/videomae-base-readminds-assignment")# Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("LinStevenn/videomae-base-readminds-assignment") model = AutoModelForVideoClassification.from_pretrained("LinStevenn/videomae-base-readminds-assignment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec9e16ae5f625d31185a8ebfcedaaed12b2d2f61178aeecb632b49c084be0aec
- Size of remote file:
- 345 MB
- SHA256:
- 96b28998f88c246a3b227bcb3699ce237caa3c83bf0d73e51752af1fde046d00
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.