Model Card: Athena-0 (Histopathology Foundation Model)

Athena-0 is a ViT-G/14 foundation model trained on only 115 million patches derived from a diverse set of 282k H&E slides, emphasizing slide diversity over patch volume and achieving near state-of-the-art performance on both tile- and slide-level downstream tasks.

Model Details

• Model type: ViT-G/14
• Params: ~1.1B
• Input: RGB patches 224×224
• Output: 1536-dim features (CLS)

Training Data

• Slides: ~282,500 H&E WSIs
• Patches: ~115 million
• Diversity: Multi-country (25), multi-institution, 8 scanner models, broad organ coverage

Paper

• Reference: Diversity Over Scale: Whole-Slide Image Variety Enables H&E Foundation Model Training with Fewer Patches (2025)

Install

This model requires the official DINOv2 repository.
Clone it and add it to your PYTHONPATH:

git clone https://github.com/facebookresearch/dinov2.git
export PYTHONPATH="$PYTHONPATH:$(pwd)/dinov2"

Usage

from huggingface_hub import snapshot_download
import torch
from torchvision import transforms
import sys

local_dir = snapshot_download(
    "PAICON-GmbH/Athena-0",
    revision="main",
    allow_patterns=["athena0.py","model_39999.safetensors","config.json"]
)
sys.path.insert(0, local_dir)

from athena0 import Athena0
model, transform = Athena0.from_pretrained(weights_path=f"{local_dir}/model_39999.safetensors", device="cuda")
model.eval()

inp = torch.rand(3, 224, 224)
inp = transforms.ToPILImage()(inp)
inp = transform(inp).unsqueeze(0).to("cuda")

with torch.inference_mode():
    features = model(inp)

assert features.shape == (1, 1536)