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---
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. |