Instructions to use facebook/dinov2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/dinov2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="facebook/dinov2-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base") model = AutoModel.from_pretrained("facebook/dinov2-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from facebook/dinov2-base: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/facebook/dinov2-base/resolve/refs%2Fpr%2F4/flax_model.msgpack
- Command line
-
hf download hf://facebook/dinov2-base@refs/pr/4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/facebook/dinov2-base/resolve/refs%2Fpr%2F4/flax_model.msgpack
346 MB
- Xet hash:
- 427cbfce8e94b62aa3ce37d88769d59b2dc8b5ff95b13d4973badd61ae9aca7d
- Size of remote file:
- 346 MB
- SHA256:
- 1178fb02b0360dc02f5b3501f158b29afd4ab2dede1cf7a8c8cececf6a1ddeb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.