Instructions to use timm/vit_huge_plus_patch16_dinov3.lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_huge_plus_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf_hub:timm/vit_huge_plus_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use timm/vit_huge_plus_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_huge_plus_patch16_dinov3.lvd1689m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_huge_plus_patch16_dinov3.lvd1689m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2953f88e0de649808bb53f9336ccd9cbc90572c4866def4afbf0e59d8da24598
- Size of remote file:
- 3.36 GB
- SHA256:
- 94e7e93a954a9d20f039401af98230a7994b75aaaf1e4eb02abe90172c27c000
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