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Sponsored by
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1aurent / resnext50_32x4d.tiatoolbox-pcam
Model's Last Updated: November 07 2023
image-classification
Introduction of resnext50_32x4d.tiatoolbox-pcam
Model Details of resnext50_32x4d.tiatoolbox-pcam
Model card for resnext50_32x4d.tiatoolbox-pcam
A ResNeXt-D image classification model.
Trained by
Tissue Image Analytics (TIA) Centre
on "pcam" histology patches.
Model Details
- Model Type: Image classification / Feature backbone
-
Model Stats:
- Params (M): 23.1
- Image size: 96 x 96 x 3
- Dataset : Patch Camelyon (PCam)
- Original: https://github.com/TissueImageAnalytics/tiatoolbox
- License : Creative Commons Zero v1.0 Universal
Model Usage
Image Classification
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/resnext50_32x4d.tiatoolbox-pcam",
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)
data = transforms(img).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
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/resnext50_32x4d.tiatoolbox-pcam",
pretrained=True,
num_classes=0,
).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)
data = transforms(img).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
@article{Pocock2022,
author = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
doi = {10.1038/s43856-022-00186-5},
issn = {2730-664X},
journal = {Communications Medicine},
month = {sep},
number = {1},
pages = {120},
publisher = {Springer US},
title = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
url = {https://www.nature.com/articles/s43856-022-00186-5},
volume = {2},
year = {2022}
}
Runs of 1aurent resnext50_32x4d.tiatoolbox-pcam on huggingface.co
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More Information About resnext50_32x4d.tiatoolbox-pcam huggingface.co Model
Url of resnext50_32x4d.tiatoolbox-pcam
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Provider of resnext50_32x4d.tiatoolbox-pcam huggingface.co
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Updated:November 27 2023
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