Instructions to use pcuenq/vit_large_patch14_dinov2.lvd142m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use pcuenq/vit_large_patch14_dinov2.lvd142m with timm:
import timm model = timm.create_model("hf_hub:pcuenq/vit_large_patch14_dinov2.lvd142m", pretrained=True) - Transformers
How to use pcuenq/vit_large_patch14_dinov2.lvd142m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="pcuenq/vit_large_patch14_dinov2.lvd142m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pcuenq/vit_large_patch14_dinov2.lvd142m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f05fcaf4228b2cb245bdf0934858bb2d6b908776c8b019a1f7e961087ec49ebc
- Size of remote file:
- 1.22 GB
- SHA256:
- 96413a203d0d00d673ae8de0a28959ae42f280106fa047b399fe9233fd31855c
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