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Sponsored by
Vmake Labs Video Enhancer
1aurent / wide_resnet50_2.tiatoolbox-pcam
Model's Last Updated: November 07 2023
image-classification
Introduction of wide_resnet50_2.tiatoolbox-pcam
Model Details of wide_resnet50_2.tiatoolbox-pcam
Model card for wide_resnet50_2.tiatoolbox-pcam
A Wide-ResNet-B 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): 66.9
- 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/wide_resnet50_2.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/wide_resnet50_2.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 wide_resnet50_2.tiatoolbox-pcam on huggingface.co
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More Information About wide_resnet50_2.tiatoolbox-pcam huggingface.co Model
Url of wide_resnet50_2.tiatoolbox-pcam
wide_resnet50_2.tiatoolbox-pcam huggingface.co Url
Provider of wide_resnet50_2.tiatoolbox-pcam huggingface.co
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Updated:November 27 2023
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