File size: 2,183 Bytes
cf33e74 97c97f9 cf33e74 97c97f9 f8c877d 97c97f9 3994bae 97c97f9 a4e86d7 97c97f9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
tags:
- image-classification
- timm
library_name: timm
license: other
license_name: lunit-non-commercial
license_link: https://github.com/lunit-io/benchmark-ssl-pathology/blob/main/LICENSE
datasets:
- 1aurent/BACH
- 1aurent/NCT-CRC-HE
- 1aurent/PatchCamelyon
pipeline_tag: image-classification
---
# 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](https://github.com/lunit-io/benchmark-ssl-pathology/blob/main/LICENSE)
## Model Usage
### Image Embeddings
```python
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
```bibtex
@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}
}
``` |