metadata
license: cc-by-nc-4.0
Model Card for PathDINOv3
PathDINOv3 is a powerful self-supervised foundation model strictly designed for advanced pathology image analysis, trained from scratch utilizing the cutting-edge DINOv3 framework.
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.
Using PathDINOv3 to extract features from pathology images
import timm
import torch
import torchvision.transforms as transforms
model = timm.create_model('hf_hub:minxoy/PathDINOv3', pretrained=True, init_values=1e-5, dynamic_img_size=True)
preprocess = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),])
model = model.to('cuda')
model.eval()
input = torch.randn([1, 3, 224, 224]).cuda()
with torch.no_grad():
output = model(input)
Training Pipeline
Self Supervised Learning: https://github.com/facebookresearch/dinov3