Update README.md

README.md

@@ -202,9 +202,11 @@ A version using the transformers library is also available here: https://hugging
   - Params: 85.8M (base)
   - Image size: 224 x 224 x 3
   - Patch size: 16 x 16 x 3
+- **Pre-training:**
+  - 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)
+  - Framework: [iBOT](https://github.com/bytedance/ibot), self-supervised, masked image modeling, self-distillation
 - **Papers:**
   - [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)
 
@@ -233,8 +235,8 @@ model = timm.create_model(
 data_config = timm.data.resolve_model_data_config(model)
 transforms = timm.data.create_transform(**data_config, is_training=False)
 
-input = transforms(img).unsqueeze(0) # (batch_size, num_channels, img_size, img_size) shaped tensor
-output = model(input)  # (batch_size, num_features) shaped tensor
+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