Instructions to use facebook/dinov2-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/dinov2-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="facebook/dinov2-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/dinov2-large") model = AutoModel.from_pretrained("facebook/dinov2-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/dinov2-large: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/facebook/dinov2-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/dinov2-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/dinov2-large/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- 746d9afe23df469b53bb2137bfb4c844aca361298607d7df9b5b520395291a40
- Size of remote file:
- 1.22 GB
- SHA256:
- 8320e4778a7f8850d10f30d97e9138438e1851af1576fea789c43746140cc655
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