Instructions to use camenduru/dinov3-vitl16-pretrain-lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use camenduru/dinov3-vitl16-pretrain-lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="camenduru/dinov3-vitl16-pretrain-lvd1689m")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("camenduru/dinov3-vitl16-pretrain-lvd1689m") model = AutoModel.from_pretrained("camenduru/dinov3-vitl16-pretrain-lvd1689m") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "DINOv3ViTModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "drop_path_rate": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_size": 1024, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "key_bias": false, | |
| "layer_norm_eps": 1e-05, | |
| "layerscale_value": 1.0, | |
| "mlp_bias": true, | |
| "model_type": "dinov3_vit", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "num_register_tokens": 4, | |
| "patch_size": 16, | |
| "pos_embed_jitter": null, | |
| "pos_embed_rescale": 2.0, | |
| "pos_embed_shift": null, | |
| "proj_bias": true, | |
| "query_bias": true, | |
| "rope_theta": 100.0, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.56.0.dev0", | |
| "use_gated_mlp": false, | |
| "value_bias": true | |
| } | |