Instructions to use timm/vit_small_patch16_dinov3.lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_small_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf_hub:timm/vit_small_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use timm/vit_small_patch16_dinov3.lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_small_patch16_dinov3.lvd1689m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_small_patch16_dinov3.lvd1689m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8e877f50b6d87f71a12ba56d9fd38e99567da4e22007a88732522ad626b1189
- Size of remote file:
- 86.4 MB
- SHA256:
- b7e7b7d1518a4dad2d92304ff47967c3dfee806f863552a06870de1e7498e7a2
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