Image Feature Extraction
timm
PyTorch
Safetensors
vit
radiomics
medical-imaging
vision-transformer
dino
dinov2
feature-extraction
foundation-model
Eval Results (legacy)
Instructions to use Snarcy/RadioDino-s16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Snarcy/RadioDino-s16 with timm:
import timm model = timm.create_model("hf_hub:Snarcy/RadioDino-s16", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "vit_small_patch16_224", | |
| "num_classes": 0, | |
| "num_features": 384, | |
| "global_pool": "token", | |
| "pretrained_cfg": { | |
| "tag": "dino", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "fixed_input_size": true, | |
| "interpolation": "bicubic", | |
| "crop_pct": 0.9, | |
| "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" | |
| }, | |
| "architectures": [ | |
| "ViTModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 384, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "vit", | |
| "num_attention_heads": 6, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 16, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.10.0.dev0" | |
| } |