Instructions to use Fluxmire/dinov3-vits16-pretrain-lvd1689m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fluxmire/dinov3-vits16-pretrain-lvd1689m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Fluxmire/dinov3-vits16-pretrain-lvd1689m")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("Fluxmire/dinov3-vits16-pretrain-lvd1689m") model = AutoModel.from_pretrained("Fluxmire/dinov3-vits16-pretrain-lvd1689m") - Notebooks
- Google Colab
- Kaggle
File size: 743 Bytes
1b5ff8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"architectures": [
"DINOv3ViTModel"
],
"attention_dropout": 0.0,
"drop_path_rate": 0.0,
"hidden_act": "gelu",
"hidden_size": 384,
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 1536,
"key_bias": false,
"layer_norm_eps": 1e-05,
"layerscale_value": 1.0,
"mlp_bias": true,
"model_type": "dinov3_vit",
"num_attention_heads": 6,
"num_channels": 3,
"num_hidden_layers": 12,
"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
}
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