Update README.md

README.md

@@ -2,4 +2,47 @@
 license: cc-by-nc-nd-4.0
 language:
 - en
----
+---
+# 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.
+
+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
+
+```python
+import timm
+import torch
+
+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{TBA
+}
+```
+
+## 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.