Update README.md

README.md

@@ -3,6 +3,77 @@ tags:
 - image-classification
 - timm
 library_name: timm
-license: apache-2.0
+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_SwAV
+
+# Model card for resnet50.lunit_swav
+
+A ResNet50 image classification model. \
+Trained on 33M histology patches from various pathology datasets.
+
+![](https://github.com/lunit-io/benchmark-ssl-pathology/raw/main/assets/ssl_teaser.png)
+
+## Model Details
+
+- **Model Type:** Feature backbone
+- **SSL Method:** SwAV
+- **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_swav",
+  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}
+}
+```