Instructions to use zenless-lab/vit_base_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_base_patch16_dinov3.lvd1689m with timm:
import timm model = timm.create_model("hf_hub:zenless-lab/vit_base_patch16_dinov3.lvd1689m", pretrained=True) - Transformers
How to use zenless-lab/vit_base_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_base_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_base_patch16_dinov3.lvd1689m", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "vit_base_patch16_dinov3", | |
| "num_classes": 0, | |
| "num_features": 768, | |
| "global_pool": "avg", | |
| "pretrained_cfg": { | |
| "tag": "lvd1689m", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 256, | |
| 256 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 1.0, | |
| "crop_mode": "center", | |
| "mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "num_classes": 0, | |
| "pool_size": null, | |
| "first_conv": "patch_embed.proj", | |
| "classifier": "head", | |
| "license": "dinov3-license" | |
| } | |
| } |