Instructions to use JWonderLand/StainNet-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use JWonderLand/StainNet-Base with timm:
import timm model = timm.create_model("hf_hub:JWonderLand/StainNet-Base", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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`StainNet-Base` is a foundation model for histology images from **immunohistochemistry** and **special stains**. Arxiv preprint paper: [https://arxiv.org/abs/2512.10326]
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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
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## Using StainNet-Base to extract features from immunohistochemistry and special staining pathology images
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`StainNet-Base` is a foundation model for histology images from **immunohistochemistry** and **special stains**. Arxiv preprint paper: [https://arxiv.org/abs/2512.10326]
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The model is a **Vision Transformer Base/16** with DINO [1] self-supervised pre-training on 1,418,938 patch images from 20,231 immunohistochemistry and special stain whole slide images (WSIs) in HISTAI [2].
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## Using StainNet-Base to extract features from immunohistochemistry and special staining pathology images
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