Update README.md

README.md

@@ -1,10 +1,61 @@
 ---
-tags:
-- image-classification
-- timm
-- transformers
-pipeline_tag: image-classification
+license: cc-by-nc-nd-4.0
+language:
+- en
+pipeline_tag: image-feature-extraction
 library_name: timm
-license: apache-2.0
 ---
-# Model card for StainNet-Base
+# Model Card for StainNet-Base
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+`StainNet-Base` is a foundation model for histology images from **immunohistochemistry** and **special stains**. Arxiv preprint paper: [https://arxiv.org/abs/2512.10326]
+
+The model is a **Vision Transformer Base/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-Base to extract features from immunohistochemistry and special staining pathology images
+
+```python
+import timm
+import torch
+import torchvision.transforms as transforms
+
+
+model = timm.create_model('hf_hub:JWonderLand/StainNet-Base', 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, 768]
+```
+
+## Citation
+
+If `StainNet-Base` 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.