Image Feature Extraction
timm
PyTorch
Safetensors
red-blood-cells
hematology
medical-imaging
vision-transformer
dino
dinov2
feature-extraction
foundation-model
Eval Results (legacy)
Instructions to use Snarcy/RedDino-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Snarcy/RedDino-base with timm:
import timm model = timm.create_model("hf_hub:Snarcy/RedDino-base", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "architecture": "vit_base_patch14_dinov2", | |
| "num_classes": 0, | |
| "num_features": 768, | |
| "global_pool": "token", | |
| "pretrained_cfg": { | |
| "tag": "lvd142m", | |
| "custom_load": false, | |
| "input_size": [ | |
| 3, | |
| 224, | |
| 224 | |
| ], | |
| "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": "cc-by-nc-4.0" | |
| } | |
| } |