Model card for vit_base_patch16_224.kaiko_ai_towards_large_pathology_fms
Model Details
Model Usage
Image Embeddings
from torchvision.transforms import v2
from PIL import Image
import requests
import torch
import timm
import io
# get example histology image
url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQc7_xZpGOfQT7sxKwf2w5lL4GAq6IX_CbTzP1NGeenzA&s"
image = Image.open(io.BytesIO(requests.get(url).content))
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/vit_base_patch16_224.kaiko_ai_towards_large_pathology_fms",
dynamic_img_size=True,
pretrained=True,
).eval()
# get image transform
preprocessing = v2.Compose(
[
v2.ToImage(),
v2.Resize(size=224),
v2.CenterCrop(size=224),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
),
]
)
data = preprocessing(image).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
Citation
@misc{ai2024largescale,
title = {Towards Large-Scale Training of Pathology Foundation Models},
author = {kaiko.ai and Nanne Aben and Edwin D. de Jong and Ioannis Gatopoulos and Nicolas Känzig and Mikhail Karasikov and Axel Lagré and Roman Moser and Joost van Doorn and Fei Tang},
year = {2024},
eprint = {2404.15217},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}