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---
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license: cc-by-nc-sa-4.0
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language:
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- en
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pipeline_tag: image-feature-extraction
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tags:
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- pathology
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- foundation_model
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- vit
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---
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# SP85M
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ViT-base (85M parameters) trained on 423,000 H&E slides from the Mount Sinai Health System.
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## Model Usage
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To get started, first clone the repository with this command:
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```bash
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git clone --no-checkout https://huggingface.co/MountSinaiCompPath/SP85M && cd SP85M && git sparse-checkout init --no-cone && git sparse-checkout set '/*' '!*.bin' && git checkout
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```
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Now you can use the following code:
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```python
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from PIL import Image
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import numpy as np
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import vision_transformer
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import torch
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import torch.nn as nn
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import torchvision.transforms as transforms
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from huggingface_hub import PyTorchModelHubMixin
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class SP85M(nn.Module, PyTorchModelHubMixin):
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def __init__(self):
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super().__init__()
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self.encoder = vision_transformer.vit_small(num_classes=0)
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def forward(self, x):
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return self.encoder(x)
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# Download up model
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model = SP85M.from_pretrained("MountSinaiCompPath/SP85M")
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# Set up transform
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
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])
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# Image
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img = np.random.randint(0, 256, size=224*224*3).reshape(224,224,3).astype(np.uint8)
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img = Image.fromarray(img)
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img = transform(img).unsqueeze(0)
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# Inference
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with torch.no_grad():
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h = model(img)
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``` |
