Instructions to use zenless-lab/vit_large_patch16_dinov3.lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use zenless-lab/vit_large_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf_hub:zenless-lab/vit_large_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use zenless-lab/vit_large_patch16_dinov3.lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="zenless-lab/vit_large_patch16_dinov3.lvd1689m") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenless-lab/vit_large_patch16_dinov3.lvd1689m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22421637b7beb4aa4d78fc27d233381599ebdd5067c12447c4c494d7c0503c1d
- Size of remote file:
- 1.21 GB
- SHA256:
- a0c23b973ef6990edf46320f06d04cbcb8dd7389d951dc25797dbc9a37516fb7
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