Instructions to use nvidia/groupvit-gcc-yfcc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/groupvit-gcc-yfcc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/groupvit-gcc-yfcc")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc") model = AutoModel.from_pretrained("nvidia/groupvit-gcc-yfcc") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a3480c3f00febafe584e0f599ff2b90631f6133b09efa7379d07987f68d0fcf
- Size of remote file:
- 223 MB
- SHA256:
- da21c48494896296493bf77c56589c0143f235234026fc6645bb7475ec788664
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.