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--- |
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license: cc-by-nc-nd-4.0 |
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language: |
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- en |
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--- |
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# Model Card for StainNet |
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<!-- Provide a quick summary of what the model is/does. --> |
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`StainNet` is a lightweight foundation model for special staining histology images. |
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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]. |
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## Using StainNet to extract features from special staining pathology image |
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```python |
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import timm |
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import torch |
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model = timm.create_model('hf_hub:JWonderLand/StainNet', pretrained=True) |
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preprocess = transforms.Compose([ |
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transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), |
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transforms.ToTensor(), |
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), |
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]) |
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model = model.to('cuda') |
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model.eval() |
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input = torch.randn([1, 3, 224, 224]).cuda() |
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with torch.no_grad(): |
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output = model(input) # [1, 384] |
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``` |
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## Citation |
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If `StainNet` is helpful to you, please cite our work. |
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``` |
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@misc{TBA |
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} |
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``` |
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## References |
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[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). |
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[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. |
