---
license: cc-by-nc-nd-4.0
language:
- en
pipeline_tag: image-feature-extraction
library_name: timm
---
# Model Card for StainNet

<!-- Provide a quick summary of what the model is/does. -->

`StainNet` is a lightweight foundation model for special staining histology images. Arxiv preprint paper: [https://arxiv.org/abs/2512.10326]

The model is a Vision Transformer Small/16 with DINO [1] self-supervised pre-training on 1,418,938 patch images from 20,231 special staining whole slide images (WSIs) in HISTAI [2].

## Using StainNet to extract features from special staining pathology image

```python
import timm
import torch
import torchvision.transforms as transforms


model = timm.create_model('hf_hub:JWonderLand/StainNet', pretrained=True)

preprocess = transforms.Compose([
            transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
            transforms.ToTensor(),
            transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
        ])

model = model.to('cuda')
model.eval()

input = torch.randn([1, 3, 224, 224]).cuda()

with torch.no_grad():
    output = model(input) # [1, 384]
```

## Citation

If `StainNet` is helpful to you, please cite our work.

```
@misc{li2025stainnet,
      title={StainNet: A Special Staining Self-Supervised Vision Transformer for Computational Pathology}, 
      author={Jiawen Li and Jiali Hu and Xitong Ling and Yongqiang Lv and Yuxuan Chen and Yizhi Wang and Tian Guan and Yifei Liu and Yonghong He},
      year={2025},
      eprint={2512.10326},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.10326}, 
}
```

## References

[1] Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9650-9660).

[2] Nechaev, D., Pchelnikov, A., & Ivanova, E. (2025). HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology. arXiv preprint arXiv:2505.12120.