Instructions to use Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForVideoClassification processor = AutoImageProcessor.from_pretrained("Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset") model = AutoModelForVideoClassification.from_pretrained("Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset/resolve/main/training_args.bin
- Command line
-
hf download hf://Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Babaili/videomae-base-finetuned-cropped-shooting-and-layup-dataset/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 81e1655aa7dd89cdbc37ad6a0f5dd4a3ff12e306934272e20580f661acda9578
- Size of remote file:
- 4.03 kB
- SHA256:
- 4a607111bd421b641e59e2e3f22ef7b2f563fbf0aad9d87a66c770ee7c807634
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.