| | ---
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| | license: cc-by-nc-sa-4.0
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| | language:
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| | - en
|
| | 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:
|
| | ```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:
|
| | ```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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| |
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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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| |
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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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| |
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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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| |
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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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| |
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| | # Inference
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| | with torch.no_grad():
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| | h = model(img)
|
| | ``` |
