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| license: cc-by-nc-4.0 |
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| # Model Card for PathDINOv3 |
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| **PathDINOv3** is a powerful self-supervised foundation model strictly designed for advanced pathology image analysis, trained from scratch utilizing the cutting-edge **DINOv3** framework. |
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| The model relies on a Vision Transformer architecture combined with DINOv3 self-supervised pre-training to establish robust and highly discriminative token-level representations across varied tissue morphologies. |
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| # Using PathDINOv3 to extract features from pathology images |
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| ```python |
| import timm |
| import torch |
| import torchvision.transforms as transforms |
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| model = timm.create_model('hf_hub:minxoy/PathDINOv3', pretrained=True, init_values=1e-5, dynamic_img_size=True) |
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| preprocess = transforms.Compose([ |
| transforms.Resize(224), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),]) |
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| model = model.to('cuda') |
| model.eval() |
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| input = torch.randn([1, 3, 224, 224]).cuda() |
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| with torch.no_grad(): |
| output = model(input) |
| ``` |
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| # Training Pipeline |
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| Self Supervised Learning: https://github.com/facebookresearch/dinov3 |
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