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%2F12/flax_model.msgpack
- Command line
-
hf download hf://facebook/dinov2-base@refs/pr/12/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/facebook/dinov2-base/resolve/refs%2Fpr%2F12/flax_model.msgpack
346 MB
- Xet hash:
- d49cd03e0389060d1161ae37dd6f75edfa0dac081b3a334d94b33dec17f07107
- Size of remote file:
- 346 MB
- SHA256:
- 19e34b5ded1143464704df7455a4de8ba934605545584e8e08420513695dc58f
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