Instructions to use AnonymousSub/ucf_101_subset_100_samples_vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnonymousSub/ucf_101_subset_100_samples_vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="AnonymousSub/ucf_101_subset_100_samples_vit")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("AnonymousSub/ucf_101_subset_100_samples_vit") model = AutoModel.from_pretrained("AnonymousSub/ucf_101_subset_100_samples_vit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ce6199da2bf8479c998da0becac9ec22d33f1727e35579e2c97621dc9200359
- Size of remote file:
- 346 MB
- SHA256:
- 14cf0917bee6f09d5c5b7f05a2d26cd76b0a42b97b3a7949df6bba9ba7f2f274
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.