Model reference · open weights
RadioDino-b16 is an open-weight embedding model from Snarcy. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | Snarcy |
|---|---|
| Type | Embedding models |
| Task | Image embed |
| Parameters (lead) | 86M |
| Runs with | timm |
| Released | 2025-05-12 |
| Popularity | 1k downloads / month |
| Licence | Open weights |
About
RadioDINO-b16 is a self-supervised Vision Transformer foundation model developed for radiomics and medical imaging. It is based on the DINO framework and pretrained on the large-scale RadImageNet dataset (1.35 million CT, MRI, and Ultrasound images across 165 classes and 11 anatomical regions). This model is part of the Radio DINO family and was created to extract robust, general-purpose features for downstream medical tasks including classification, segmentation, and interpretability analysis.
Unlike traditional radiomics methods that rely on handcrafted features and supervised models pretrained on natural images, RadioDINO-b16 offers a domain-adapted alternative that consistently outperforms previous models on diverse medical benchmarks. It has been rigorously validated on the MedMNISTv2 benchmark suite and shown to be effective even without fine-tuning.
🧠 Developed by Luca Zedda, Andrea Loddo, and Cecilia Di Ruberto 🏥 Department of Mathematics and Computer Science, University of Cagliari 📄 Published in: Computers in Biology and Medicine, 2025
b16)from PIL import Image
from torchvision import transforms
import timm
import torch
# Load model from Hugging Face Hub
model = timm.create_model("hf_hub:Snarcy/RadioDino-b16", pretrained=True)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# Load and preprocess a sample image
image = Image.open("path/to/your/image").convert("RGB")
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
input_tensor = transform(image).unsqueeze(0).to(device)
# Forward pass to obtain feature embedding
with torch.no_grad():
embedding = model(input_tensor)
If you use this model, please cite the following paper:
Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis Luca Zedda, Andrea Loddo, Cecilia Di Ruberto Computers in Biology and Medicine, Volume 195, 2025, 110583 https://doi.org/10.1016/j.compbiomed.2025.110583
@article{ZEDDA2025110583,
title = {Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis},
journal = {Computers in Biology and Medicine},
volume = {195},
pages = {110583},
year = {2025},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2025.110583},
url = {https://www.sciencedirect.com/science/article/pii/S0010482525009345},
author = {Luca Zedda and Andrea Loddo and Cecilia {Di Ruberto}},
keywords = {Radiomics, Self-supervised learning, Deep learning, DINO, DINOV2, Medical imaging, Feature extraction, Generalizability},
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Benchmarks
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Image Classification | BreastMNIST | F1 | 87.690 |
| Image Classification | PneumoniaMNIST | F1 | 93.290 |
| Image Classification | OrganAMNIST | F1 | 97.200 |
| Image Classification | OrganCMNIST | F1 | 94.570 |
| Image Classification | OrganSMNIST | F1 | 78.150 |
| Image Classification | BUSI | F1 | 91.730 |
Using it via the API
Once AxForge deploys radiodino-b16 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (radiodino-b16 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"radiodino-b16","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.