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1aurent / resnet50.lunit_bt
Model's Last Updated: November 27 2023
image-classification
Introduction of resnet50.lunit_bt
Model Details of resnet50.lunit_bt
Model card for resnet50.lunit_bt
A ResNet50 image classification model.
Trained on 33M histology patches from various pathology datasets.
Model Details
- Model Type: Feature backbone
- SSL Method: Barlow Twins
-
Model Stats:
- Params (M): 23.6
- Image sizes (max): 1024 × 768 x 3
-
Papers:
- Benchmarking Self-Supervised Learning on Diverse Pathology Datasets: https://arxiv.org/abs/2212.04690
-
Datasets:
- BACH
- CRC
- MHIST
- PatchCamelyon
- CoNSeP
- Original: https://github.com/lunit-io/benchmark-ssl-pathology
- License: lunit-non-commercial
Model Usage
Image Embeddings
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/resnet50.lunit_bt",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
Citation
@inproceedings{kang2022benchmarking,
author = {Kang, Mingu and Song, Heon and Park, Seonwook and Yoo, Donggeun and Pereira, Sérgio},
title = {Benchmarking Self-Supervised Learning on Diverse Pathology Datasets},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {3344-3354}
}
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Updated:November 27 2023
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