| ---
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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
|
| ---
|
|
|
| # 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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|
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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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| ``` |
