Update README.md

README.md

@@ -1,9 +1,155 @@
----
-tags:
-- image-classification
-- timm
-- transformers
-library_name: timm
-license: apache-2.0
----
-# Model card for tbd_b
+---
+library_name: timm
+license: cc-by-4.0
+pipeline_tag: image-feature-extraction
+tags:
+- radiology
+- medical-imaging
+- xray
+- ct
+- mri
+- ultrasound
+- foundation-model
+- vision-transformer
+- self-supervised
+- dino
+- dinov2
+
+model-index:
+- name: OmniRad-base
+  results:
+  - task:
+      type: image-feature-extraction
+    dataset:
+      name: RadImageNet
+      type: radimagenet
+    metrics:
+    - name: Representation learning
+      type: other
+      value: "Self-supervised pretrained encoder"
+---
+
+# OmniRad: A General-Purpose Radiological Foundation Model
+<!--
+[📄 Paper](https://arxiv.org/abs/XXXX.XXXXX) |
+-->
+ [💻 Code](https://github.com/unica-visual-intelligence-lab/OmniRad)
+
+**OmniRad** is a **self-supervised radiological foundation model** designed to learn **stable, transferable, and task-agnostic visual representations** for medical imaging. It is pretrained on large-scale, heterogeneous radiological data and intended for reuse across **classification**, **segmentation**, and **exploratory vision–language** tasks without task-specific pretraining.
+
+This repository provides the **OmniRad-base** variant, a compact Vision Transformer encoder that offers an excellent trade-off between computational efficiency and representational power.
+
+---
+
+## Key Features
+
+- **Radiology-focused foundation model** pretrained on >1M radiological images
+- **Self-supervised learning** based on a customized DINOv2 framework
+- **Task-agnostic encoder** reusable across classification, segmentation, and multimodal pipelines
+- **Strong transferability** across modalities (CT, MRI, X-ray, ultrasound)
+- **Radiomics-oriented design**, emphasizing representation stability and reuse
+
+---
+
+
+## Example Usage: Feature Extraction
+
+```python
+from PIL import Image
+from torchvision import transforms
+import timm
+import torch
+
+# Load OmniRad-base from Hugging Face Hub
+model = timm.create_model(
+    "hf_hub:Snarcy/OmniRad-base",
+    pretrained=True,
+    num_classes=0  # return embeddings
+)
+
+model.eval()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Preprocessing
+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],
+    ),
+])
+
+# Load image
+image = Image.open("path/to/radiology_image.png").convert("RGB")
+x = transform(image).unsqueeze(0).to(device)
+
+# Extract features
+with torch.no_grad():
+    embedding = model(x)  # shape: [1, 384]
+
+
+```
+---
+
+## Available Downstream Code
+
+The **official OmniRad repository** provides **end-to-end implementations** for all evaluated downstream tasks:
+
+👉 **https://github.com/unica-visual-intelligence-lab/OmniRad**
+
+Including:
+- **Image-level classification** (MedMNIST v2 benchmarks)
+- **Dense medical image segmentation** (MedSegBench, frozen encoder + lightweight decoders)
+- **Radiological image captioning** (BART-based vision–language framework)
+- Full training, evaluation, and ablation scripts
+- Reproducible experimental configurations matching the paper
+
+---
+## Model Details
+
+- **Architecture:** Vision Transformer (ViT-B)
+- **Patch size:** 14
+- **Embedding dimension:** 768
+- **Pretraining framework:** Modified DINOv2 (global crops only)
+- **Pretraining dataset:** RadImageNet (~1.2M radiological images)
+- **Input resolution:** 224 × 224
+- **Backbone type:** Encoder-only (no task-specific heads)
+
+### Pretraining Notes
+
+- Local crops are removed to improve training stability and downstream transferability
+- No feature collapse observed during training
+- Same hyperparameter configuration used across small and base variants
+- Designed to support frozen-backbone adaptation and lightweight fine-tuning
+
+---
+
+
+## Intended Use
+
+OmniRad is intended as a **general-purpose radiological image encoder** for:
+
+- Image-level classification (e.g., disease or organ recognition)
+- Dense prediction (e.g., medical image segmentation via adapters or decoders)
+- Radiomics feature extraction
+- Representation transfer across datasets, modalities, and institutions
+- Exploratory vision–language research (e.g., radiological image captioning)
+
+**Not intended for direct clinical deployment without task-specific validation.**
+
+---
+
+
+
+## License
+
+This project and the released model weights are licensed under the Creative Commons
+Attribution 4.0 International (CC BY 4.0) license.
+
+<div align="center">
+
+**Made with ❤️ by [UNICA Visual Intelligence Lab](https://github.com/unica-visual-intelligence-lab)**
+
+</div>