You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

CROWN

A Universal Visual Foundation Model for Computational Cytopathology.

CROWN is a visual foundation model pretrained on over 10 million cytology images.
It provides transferable and annotation-free feature representations for a wide range of cytological image analysis tasks.

Highlights

Pretrained with a DINOv2-style self-supervised framework on large-scale cytology data
Strong transferability across classification, retrieval, segmentation, detection, and slide-level weakly supervised tasks
Robust under significant domain shifts, serving as a single scalable backbone for cytology research

Quick usage

After downloading our model weights：

from models.vision_transformer import vit_large
import torch

model = vit_large(
    patch_size=16,
    img_size=224,
    init_values=1.0,
    block_chunks=4,
    ffn_layer="swiglufused",
)

state_dict = torch.load("CROWN.pth", map_location="cpu")
model.load_state_dict(state_dict, strict=True)
model.eval()

Used for feature extraction:

feat = model.forward_features(img)["x_norm_clstoken"]

Citation

If you find our work useful in your research or if you use parts of this code please consider citing our paper:

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

Image Feature Extraction

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support