Update README.md

README.md

@@ -180,7 +180,6 @@ inference: false
 datasets:
 - owkin/camelyon16-features
 - owkin/nct-crc-he
-- 1aurent/NCT-CRC-HE
 metrics:
 - roc_auc
 ---
@@ -188,21 +187,26 @@ metrics:
 # Model card for vit_base_patch16_224.owkin_pancancer
 
 A Vision Transformer (ViT) image classification model. \
-Trained by Owkin on 40M pan-cancer histology tiles from TCGA.
+Trained by Owkin on 40 million pan-cancer histology tiles from TCGA-COAD.
+
+A version using the transformers library is also available here: https://huggingface.co/owkin/phikon
 
 ![](https://github.com/owkin/HistoSSLscaling/blob/main/assets/main_figure.png?raw=true)
 
 ## Model Details
 
 - **Model Type:** Feature backbone
+- **Developed by**: Owkin
+- **Funded by**: Owkin and IDRIS
 - **Model Stats:**
-  - Params (M): 85.8
+  - Params: 85.8M (base)
   - Image size: 224 x 224 x 3
+  - Patch size: 16 x 16 x 3
 - **Papers:**
-  - Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling: https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v2
-- **Dataset:** TGCA: https://portal.gdc.cancer.gov/
-- **Original:** https://github.com/owkin/HistoSSLscaling/
-- **License:** https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt
+  - [Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling](https://www.medrxiv.org/content/10.1101/2023.07.21.23292757v2)
+- **Dataset:** Pancancer40M, created from [TCGA-COAD](https://portal.gdc.cancer.gov/repository?facetTab=cases&filters=%7B%22content%22%3A%5B%7B%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-COAD%22%5D%7D%2C%22op%22%3A%22in%22%7D%2C%7B%22content%22%3A%7B%22field%22%3A%22files.experimental_strategy%22%2C%22value%22%3A%5B%22Diagnostic%20Slide%22%5D%7D%2C%22op%22%3A%22in%22%7D%5D%2C%22op%22%3A%22and%22%7D&searchTableTab=cases)
+- **Original:** https://github.com/owkin/HistoSSLscaling
+- **License:** [Owkin non-commercial license](https://github.com/owkin/HistoSSLscaling/blob/main/LICENSE.txt)
 
 ## Model Usage
 
@@ -229,7 +233,8 @@ model = timm.create_model(
 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
+input = transforms(img).unsqueeze(0) # (batch_size, num_channels, img_size, img_size) shaped tensor
+output = model(input)  # (batch_size, num_features) shaped tensor
 ```
 
 ## Citation